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<p>I do not think it is worth it. This will make GPUs use less power and might even make them faster. Maybe wait a year and see how the landscape has changed since then. It is worth mentioning that technology is slowing anyways. So waiting for a year might net you a GPU, which will stay current for more than 5 years.</p>
<p>There will be a time when cheap HBM memory can be manufactured. Such GPUs might be available in years. As such, playing the waiting game can be a pretty smart choice. Be aware of memory, as discussed in the previous section, but also power requirements and cooling.</p>
<p>In general, I would recommend the RTX for anyone that can afford it. It will equip you not only for now but will be a very effective card for the next years. As such, it is a good investment that will stay strong. We will probably see cheap HBM memory in years, so after that, you definitely want to upgrade.</p>
<p>Again, it is crucial, though, that you make sure that heating issues in your GPU servers are taken care of before you commit to specific GPUs for your servers. More on GPU clusters below. I will update the blog post about this as more and more data is rolling in what is a proper setup.</p>
<p>If you do not like used cards, but the RTX  All of these cards are very cost-effective solutions and will ensure fast training of most networks. If you use the right memory tricks and are fine with some extra programming, there are now enough tricks to make a 24 GB neural network fit into a 10 GB GPU. As such, if you accept a bit of uncertainty and some extra programming, the RTX might also be a better choice compared to the RTX since performance is quite similar between these cards.</p>
<p>It is not clear yet if there will be an RTX , but if you are on a limited budget, it might also be worth waiting a bit more. If your budget is limited, but you still need large amounts of memory, then old, used Tesla or Quadro cards from eBay might be best for you. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is paramount.</p>
<p>GPU cluster design depends highly on use. However, often universities can get an exemption from this rule. If you want to scale to more than GPUs, you need a highly optimized system, and putting together standard solutions is no longer cutting it.</p>
<p>The GPU system offers a bit more flexibility of deep learning models and applications over the TPU system, while the TPU system supports larger models and provides better scaling. So both systems have their advantages and disadvantages. They will simply run too hot, and their performance will be way below what I report in the charts above. I do not recommend buying Tesla V or A unless you are forced to buy them banned RTX data center policy for companies or unless you want to train very large networks on a huge GPU cluster — these GPUs are just not very cost-effective.</p>
<p>If you can afford better cards, do not buy GTX 16 series cards. These cards do not have tensor cores and, as such, provide relatively poor deep learning performance.</p>
<p>If you are short on money, however, the GTX 16 series cards can be a good option. Your GPUs are already pretty good, and the performance gains are negligible compared to worrying about the PSU and cooling problems for the new power-hungry RTX 30 cards — just not worth it.</p>
<p>The only reason I would want to upgrade from 4x RTX Ti to 4x RTX would be if I do research on huge transformers or other highly compute dependent network training. However, if memory is a problem, you may first consider some memory tricks to fit large models on your 4x RTX Tis before upgrading to RTX s.</p>
<p>These are pretty good GPUs. This reasoning is valid for many other GPUs: If memory is tight, an upgrade is right. Generally, no. PCIe 4. It is okay if you have an 8x GPU machine, but otherwise, it does not yield many benefits. It allows better parallelization and a bit faster data transfer. Data transfers are not a bottleneck in any application.</p>
<p>So there is no real reason to get a PCIe 4. Same as with PCIe 4. PCIe lanes are needed for parallelization and fast data transfers, which are seldom a bottleneck. You need to get one of the two-slot variants, or you can try to spread them out with PCIe extenders.</p>
<p>Besides space, you should also immediately think about cooling and a suitable PSU. This will keep the cards very cool. There might also be other variants which are cheaper though.</p>
<p>PCIe extenders might also solve both space and cooling issues, but you need to make sure that you have enough space in your case to spread out the GPUs.</p>
<p>Make sure your PCIe extenders are long enough! Yes, you can! But you cannot parallelize efficiently across GPUs of different types. This works just fine, but parallelization across those GPUs will be inefficient since the fastest GPU will wait for the slowest GPU to catch up to a synchronization point usually gradient update. Generally, NVLink is not useful.</p>
<p>Otherwise, it yields almost no benefits over standard PCIe transfers. Definitely buy used GPUs. If that is too expensive, it is best to roll with free GPU cloud services. Rotate between services and accounts until you can afford your own GPU. The calculator can also be used to calculate a pure GPU carbon footprint. You will find that GPUs produce much, much more carbon than international flights. As such, you should make sure you have a green source of energy if you do not want to have an astronomical carbon footprint.</p>
<p>If no electricity provider in our area provides green energy, the best way is to buy carbon offsets. Many people are skeptical about carbon offsets. Do they work? Are they scams? I believe skepticism just hurts in this case, because not doing anything would be more harmful than risking the probability of getting scammed. If you worry about scams, just invest in a portfolio of offsets to minimize risk. I worked on a project that produced carbon offsets about ten years ago.</p>
<p>The carbon offsets were generated by burning leaking methane from mines in China. UN officials tracked the process, and they required clean digital data and physical inspections of the project site. In that case, the carbon offsets that were produced were highly reliable. I believe many other projects have similar quality standards. It does not seem so. Since the granularity of the sparse matrix needs to have 2 zero-valued elements, every 4 elements, the sparse matrices need to be quite structured.</p>
<p>It might be possible to adjust the algorithm slightly, which involves that you pool 4 values into a compressed representation of 2 values, but this also means that precise arbitrary sparse matrix multiplication is not possible with Ampere GPUs.</p>
<p>We built dozens of systems at our university with Threadrippers, and they all work great — no complaints yet. Case design will give you C better temperatures, space between GPUs will provide you with C improvements.</p>
<p>The bottom line, if you have space between GPUs, cooling does not matter. If you have no space between GPUs, you need the right cooler design blower fan or another solution water cooling, PCIe extenders , but in either case, case design and case fans do not matter. Not in the next years. It is a three-way problem: Tensor Cores, software, and community.</p>
<p>Packed low-precision math does not cut it. Rumors show that some data center card with Tensor Core equivalent is planned for , but no new data emerged since then. How am I supposed to use them? So here AMD has come a long way, and this issue is more or less solved. However, if you solve software and the lack of Tensor Cores, AMD still has a problem: the lack of community. Julia has a lot of potential, and many would say, and rightly so, that it is the superior programming language for scientific computing.</p>
<p>Yet, Julia is barely used compared to Python. This is because the Python community is very strong. Numpy, SciPy, Pandas are powerful software packages that a large number of people congregate around.</p>
<p>AMD will always snatch a part of the market share in specific subgroups e. Rule-of-thumb: If you expect to do deep learning for longer than a year, it is cheaper to get a desktop GPU.</p>
<p>Otherwise, cloud instances are preferable unless you have extensive cloud computing skills and want the benefits of scaling the number of GPUs up and down at will. For the exact point in time when a cloud GPU is more expensive than a desktop depends highly on the service that you are using, and it is best to do a little math on this yourself.</p>
<p>So if you expect to run deep learning models after days, it is better to buy a desktop instead of using AWS on-demand instances. AWS spot instances are a bit cheaper at about 0. However, many users on Twitter were telling me that on-demand instances are a nightmare, but that spot instances are hell. This means you need a pretty good spot instance management infrastructure to make it worth it to use spot instances. But if you have it, AWS spot instances and similar services are pretty competitive.</p>
<p>You need to own and run a desktop for 20 months to run even compared to AWS spot instances. This means if you expect to run deep learning workloads in the next 20 months, a desktop machine will be cheaper and easier to use. You can do similar calculations for any cloud service to make the decision if you go for a cloud service or a desktop.</p>
<p>In general, utilization rates are lower for professions where thinking about cutting edge ideas is more important than developing practical products. Some areas have low utilization rates interpretability research , while other areas have much higher rates machine translation, language modeling. In general, the utilization of personal machines is almost always overestimated. I have little money : Buy used cards. I have almost no money : There are a lot of startups that promo their clouds: Use free cloud credits and switch companies accounts until you can afford a GPU.</p>
<p>I am a competitive computer vision, pretraining, or machine translation researcher : 4x RTX  Wait until working builds with good cooling, and enough power are confirmed I will update this blog post.</p>
<p>I am an NLP researcher : If you do not work on machine translation, language modeling, or pretraining of any kind, an RTX will be sufficient and cost-effective. Depending on what area you choose next startup, Kaggle, research, applied deep learning , sell your GPUs, and buy something more appropriate after about three years next-gen RTX 40s GPUs. I want to try deep learning, but I am not serious about it : The RTX Super is excellent but may require a new power supply to be used.</p>
<p>If your motherboard has a PCIe x16 slot and you have a power supply with around W, a GTX Ti is a great option since it will not require any other computer components to work with your desktop computer.</p>
<p>For past updates of this blog post, I want to thank Mat Kelcey for helping me to debug and test custom code for the GTX ; I want to thank Sander Dieleman for making me aware of the shortcomings of my GPU memory advice for convolutional nets; I want to thank Hannes Bretschneider for pointing out software dependency problems for the GTX ; and I want to thank Oliver Griesel for pointing out notebook solutions for AWS instances. Yes, I would love to see a Portuguese translation!</p>
<p>Go for it. The only thing that I would ask you to do if you translate it section by section is to include the source somewhere in the introduction to the translation. Great info here. Wondering your thoughts on the new apple m1 max chip with 64GB RAM— will it be practical for training?</p>
<p>First off I want to thank you for taking the time to write all these well researched articles. Your website has been incredibly useful. Because of the specifics of how I would use that machine in interactive pieces, it would not be possible to use a remote cloud machine because of latency and bandwidth requirements — I absolutely need a physical machine. The maximalist in me wants to go for a WX, but that might be overkill. I can think of ways of truly using that hardware to its limits, so I believe its power would not go to waste.</p>
<p>On the more reasonable side of the spectrum a X would likely give nice performance better on single core tasks, worse on multi-core tasks that scale up massively , but there are no possibilities for expandability. It is much more affordable however — this is more realistically achievable. Hi Simon! I think the choice of A GPUs is great in your use-case. I think both CPU options can have their advantages. For inference, a CPU can sometimes be critical to achieving good latency, but that can depend on many factors, mainly how much pre and post-processing needs to be done on CPU.</p>
<p>Depending on this, it could be that a good CPU has large benefits or not. On the other hand, such performance will not be very noticeable for general use, but might be critical for user-facing applications. So if you aim to build projects that are user-facing and require low latency it might be worth it to go with the WX.</p>
<p>However, since CPUs have quirks and many threads are difficult to use, in many cases you will get the same performance for the much cheaper X. As such, it might also be a waste of money. I think I would probably go with the X, and if the user needs to wait for an additional 50ms then the user just needs to wait for that!</p>
<p>Thank you for your insight. After some more research, I have found that in synthetic benchmarks the X actually outperforms the WX in both single and multi core performance which is very surprising given it has half the core count — it seems the architecture version Gen 3 vs Gen 2 plays a large role here. Gen 3 Threadripper Pro is likely coming out soon, as some benchmark scores have been leaked and they are definitely scoring higher than Ryzen there.</p>
<p>I would love to buy a faster graphics card to speed up the training of my models but graphics card prices have increased dramatically in  Try to wait a bit longer and look for used laptops with a better GPU.</p>
<p>I am looking to rent a dedicated GPU server for inferencing only. I have a flask app listening for base64 requests which my python app pushes through tensorflow and sends the output image back to the client. Currently its using CPU and a single image which is usually p takes 20 seconds per request. The problem I have is that the server can get a request at any time during the day, so hourly rental is just not cost effective at the moment.</p>
<p>One day there might be 1 request, and the next day there might be  This is a difficult use case. I would probably build a small desktop with a GPU for this sake to reduce costs, but upload speed is important for this which is usually small for residential internet. This would be a pure work PC and not for games.</p>
<p>For now I will go with one or A and expand later to 2 or 3. Since you have mentioned that you have experience with Threadripper systems, so I wanted to ask whether going for Threadripper X or X is better or going for Threadripper Pro WX is better?</p>
<p>So I am looking atleast this PC to last for next years. So I wanted to do like an incremental build now going for GB or incrementing it as computing needs increases. You thoughts on this would be a golden advise. Thanks in Advance. The question is what do you need that much RAM for? But I am still confused what does CNN mean?</p>
<p>One quick question — have you looked in Graphcore IPUs at all? This memory alone does not make for a fast processor and one would need a special kind of neural network to take full advantage of IPUs. Which barebones supermicro system should I pick up?</p>
<p>Your case sounds actually like you would benefit a lot from a desktop computer that you stock up as needed. One disadvantage is that the warranty and support will expire often in about 3 years, so this advantage of a server system would be nullified. It is also a waste if you only need 4 GPUs. Really nice in-depth article, thanks. Tensor cores are limited mainly by the memory access time within a GPU.</p>
<p>The main memory is too slow to fully utilize the tensor cores even under perfect conditions other than trivial in-place computation.</p>
<p>The has 12GB memory vs 8GB for the  The has a bit bus with tensor cores vs a bus with tensor cores. If you just want to run things without any trouble I would recommend the RTX  Thanks for your trying to sharing with us. I am going to build up a system with 4 GPUs. It is hard to choose between A and A What do you think works better for computer vision? The A is clearly better, but also more expensive.</p>
<p>If money is not the problem I would go with the A If you look at cost-performance, these two come actually pretty close, so either GPU is fine. First sorry for my bad english. Your essay has benefited me a lot,but I have some questions to ask. Is study GAN paper is the good way to getting start,or do Image type on kaggle? Also,I want to buy a new PC,but is out of stock,should I wait for it or just buy ? Is GAN need 12gvram or 8G is enough but faster?</p>
<p>Thank for your answer! So the RTX is great. If you want to do research, I would recommend that you try to find another researcher to work with from whom you can learn how to do research. This is awesome! Thank you so much for this. Or could it be like an Asus RTX , or any of the other manufacturers?</p>
<p>You can buy GPUs from any manufacturers. Sort of, one for gaming and the other for DL? Just thinking out loud, basically. Thanks for this amazing article Tim!</p>
<p>Has been useful for multiple years now! We have budget for a multiple GPU machine. We work with large 3D images and so considering the for its higher memory. Would you recommend it? Thank you. Great article! I am researching now the best budget AI reinforcement learning hardware combination for a laptop. After some research and reading this article I basically ended up with two choices.</p>
<p>From the article I see that more memory for GPU is definitely a good thing, however, for Reinforcement learning, you mentioned that the more CPU cores the better. I think there will not be a great difference between those CPUs, either one is fine and you should see no large differences in performance for RL. The server has s. I am a noob, but i want something that I can carry with me and develop on easily and seamlessly transfer to server.</p>
<p>It highly depends on the nature of those datasets and the complexity of those models. Beyond that, you might need a larger GPU with more memory. I am planning to start researching GANs, I am waiting for the ti with 12gb, hopefully, it will not be too much overpriced.</p>
<p>Finally, running a single GPU for a considerable time I imagine a week could create heating problems in a ventilated case and room? Do you have recommendations for water-coolers? Awesome articles! I am trying to build an autonomous drone and the on board computer will be an Xavier NX. I am pretty sure that can handle inference of the system, but I am trying to understand which GPU I should train my prototype on.</p>
<p>That is considering I will start with the CV aspect asking the drone to find free paths, recognize colors, etc. This progression might take years. I would love to hear the difference between these fields as it relates to choosing hardware. This blog is the best source of information about how to build a machine learning box, however it has what I see as crucial problem. In particular, the supposed leader in that category is the  But I have signed myself up for several services that alert me for the availability of this card at this price, and for months I have not been able to get it.</p>
<p>The MSRP is essentially meaningless. The cost estimates seem fair in a normal market. Sadly, this is not a normal market. We plan to use them inorder to train our models. We are working on text to audio task wavenet, waveglow, tacotron 2, and voice varifier and audio to text task wav2vec and wav2letter.</p>
<p>We use Librispeech for both about hours. Also in case we have used all of the fund we will be allowed to use the university labs 2x  Currently we are stuck in deciding the type of the ec2 instance.</p>
<p>Our options are k80, V and A p2, p3, p4 types. Also we are not able to guiss how many GPUs of the type we should choose do we need? Do you think its better to build my own station than buy one from the company?</p>
<p>Could you update the charts with current prices as the distribution problems for RTXseries are predicted to last until end of the Q2 this year. Also could you give your opinion on RTXti. I do my current learning on a dual socket intel server that only has one PCIe x16 lane slot and 3 x8 slots.</p>
<p>I do kaggle level work. Happy to provide more info if needed to answer. So the problem with the insufficient video memory is real. Tim, Please allow this machine learning inexperienced cheapo to ask your opinion about a GPU choice. The goal is to create some software that will provide suggestions for a certain type of textual work. Prices are local for my country.</p>
<p>What do you think? Thank you for this great article! My problem is the shortage of the  What if I go for the A instead? More technically, is there downsides having one bigger card for training? Can I run multiple training tasks on that GPU. Can deep learning beneficiates from larger batch size on such a card? If the AMD wx is too expensive no prices yet compare to the next zen 3 genesis peak x, is the x with A be still a beast and reliable?</p>
<p>The A is tempting because it takes only 16 lanes and 2 or 3 slots compare to 2x , and no nvlink to get 48Go with 2x  What are your toughts? Thanks again,. It depends. If you train triplet loss with online hard-negative mining, then large batches are a must. On batch of 64 was enough to saturate the GPU inference and no further speed up with bigger batches was observed. One training task usually maxes out the GPU and asks for even more.</p>
<p>Also, I may explore participating in Kaggle competitions in the future. Hi Tim, awesome article — thanks for sharing your wisdom with the community. Would I even be able to fit two of these cards in the cases? Thanks for any pointers. Any thoughts on the new 12GB release? Gets past that 11GB barrier mentioned for Transformer models. Hi, thanks so much for your incredible work. Hi, can you post your updates after CES ?</p>
<p>So I was thinking about getting?? What is your opinion? Thank you for your response and be well. Hi, thanks for the detailed explanation on all aspects. Which threadripper would you recommend tho? Thank you for all of the information you have provided. It was very informative for me. I was wondering if you can help me. I work in a small company, and for now we are just two data scientists, and at most we will grow to have data scientists in 2 years.</p>
<p>We are going to train some deep learning models for image processing. Can you tell me what kind of GPU we should get for a server for our company?</p>
<p>I recently built a new rig for hobbyist ML. My question is I have about 2k reserved for the graphics card right now. I have academia research experience with NLP and Computer Vision, but I also have little exposure to many other subfields and would like to experiment in other fields. Hi Tim Really great effort in putting together all the salient features for researchers in Deep Learning, especially useful for students like me.</p>
<p>Would also be helpful if you could recommend some cost cutting measures that I could make to this list. Would it be better for me to switch to a cloud or get a ?</p>
<p>I think in your case you want to have many small GPUs. At least if your models are too big. For that using cloud computing can be very efficient. I would not worry about the lifetime of the GPU, it should be fine even if it runs all the time.</p>
<p>Do you expect the same for the to-be-released Ti? Or is the temperature issue a sign of limits being reached and Nvidia just trying to get away with it? That has been fixed with the RTX 30 series and the Ti should be just fine.</p>
<p>Hello Tim. Great article. It is helping me a lot in deciding my next GPU and machine for doing deep learning. I have one question that I want to ask as there is not much information out there in such situations. First of all, I am serious about deep learning.</p>
<p>I regularly take on personalized medium-scaled deep learning projects and I also want to increase the standard and quality of my deep learning blog posts. Also, traveling between jobs for the next two years is almost mandatory for me. I want to upgrade to a good laptop.</p>
<p>I know that Max-Q variants keep the temperature hovering around degrees Celcius for hours on end. Still, I am a bit skeptical. I really want to get your opinion on this.</p>
<p>Hoping to hear from you. Hello Sovit, I think the Max-Q is a great GPU and the high temperature should be fine unless you often use your laptop on your lap, which can become uncomfortable over time. I think it is a great option in your case. Another option might be a dedicated cloud GPU. However, in the case of a cloud GPU you will need stable internet access, which is not always readily available when traveling. Thank you so much Tim. As you are providing two options, I think I will go for the Max Q machine for the next two years.</p>
<p>It will help me in a lot of ways and will also provide me with the flexibility to take on my projects on my own time. Thanks again for your feedback. Your articles are really awesome and help me a lot. Fantastic article. With model parallelism or the even rarer pipeline parallelism you can however spread the model across GPUs and save more memory. However, software support for this is not readily available yet but should become common in the next 6 months.</p>
<p>So if you need lots of memory, a single 24 GB GPU will currently serve you better and will still serve you well in a couple of months. Hi Tim, Thanks for sharing all your detailed insights! Amazing learning experience for me. I am working on Deep Reinforcement Learning for trading in the financial markets. My understanding is that the deep learning part in this context would be for finding out the optimal policy for the RL.</p>
<p>Would that rather imply a smaller training dataset size like 10GB or a bigger one like 24GB ? Thank you! Cheers, Roman. As such, I would go for the and invest the extra money in a CPU with more cores and more power. Thank you, Tim! Just returned the Ryzen 9 x and got the Threadripper x with 32 cores and 64 threads!</p>
<p>I think it is better to start small and depending on how you like it, upgrade later to something larger or if needed, so something really big. Thanks a lot for all the informations on your website. I have a question, I just started a master program in computer science and I would love to continue afterwards to grad schools in deep learning, especially in RL.</p>
<p>My question is hardware related: I have a desktop computer I build a few years ago, but the GPU zotac 6gb is a bottleneck now when I try to train big models. Hence I believe the gpu is a good fit. I wonder what to do with the old graphics card. Is it a good idea to use it for the system display since it eats up some memory , so that the new gpu would be fully available for models? Also, I am afraid the two GPUs would be stacked together too closely on the motherboard.</p>
<p>Would it be a good idea to invest into water cooling or an other motherboard? The blowers of the old card would be just above the new one in the current display. Hello Justin, I would not worry too much about cooling. I think cooling will just be fine, but what I would do is: Test the GTX driving the displays and monitor cooling, if cooling is not good remove the GTX and sell it online and power the displays with the RTX  I observed that when fully loaded the temperature of the GPU is about 81C, and the fan noise is strong.</p>
<p>Is this normal? Currently my training time is short but I am not sure if when I train some very large model, could this temperature last hours or even days without causing any thermal issue or damage to the GPU?</p>
<p>However, they are a little different although both of them are  Will there be any compatibility issue or will one of them be bottleneck because of the differences between two cards?</p>
<p>If so, I will refund one of them and get a new one. I am considering purchasing a Razor Core X thunderbolt 3 enclosure. I plan on doing some nlp deep learning models. Would I be ok with an 8gb card, or should i spend a little extra for a 10gb card? Hi Tim! Thanks for the excellent and thoroughly researched post. But the performance looks so much better on paper..</p>
<p>I would go for the A and use power limiting if you run into cooling issues. It is just the better card all around and the experience to make it work in a build will pay off in the coming years. Also make sure that you exhaust all kinds of memory tricks to safe memory, such as gradient checkpointing, bit compute, reversible residual connections, gradient accumulation, and others.</p>
<p>This can often help to quarter the memory footprint at minimal runtime performance loss. As its so hard to get a new GPU card of your choice. Can pairing a ti with RTX or in future or Pairing a ti with any RTX ti, or give us the advantages of pooling resources of a multi-gpu PC for deep learning?</p>
<p>It can work, but the details can be complicated. Some combinations of GPUs work better than others. In general, the smaller the gap between GPU performance, the better. Been following this blog and post including the full hardware guide for some time now. Thinking of a x build. I would recommend a minimum of an RTX or two in your case. The reason for this is both memory and speed that is required to build on the most recent models or to apply them to a different academic field.</p>
<p>Thanks for the post! I was looking into the Nvidia Jetson AGX Xavier, would that have better performance training deep learning algorithms, especially with 32GB of ram then the ? The power consumption of only 30 watts and the 8-core ARM chip are also atractive.</p>
<p>The Jetson GPUs are rather slow. I would only recommend them for robotics applications or if you really need a very low power solution. Compared it to GTX simple conv on mnist. On equal batch sizes Jetson works a bit slower but I ingested it with larger batch than gtx and train epoch on mnist faster. Thanks for leaving a comment about your experience, Alex! I agree, from my experience, the main problem with Jetson is the ARM CPU which makes it a pain to install the libraries that you need to run stuff.</p>
<p>I saw that you seem to prefer AMD Threadripper in terms of hardware, but what about its impact in software libraries, etc available for Deep Learning? In general, the most CPU intensive deep learning tasks are data preprocessing. Both CPUs usually do just as well for these tasks. Hi, thank you for your article, will you update the article to include the RTX ti that will be released tomorrow  Thank you for this is a great post!</p>
<p>I am curious if you have any thoughts about where the soon-to-be-released A fits in? Where do you expect this to fall, in particular when compared to the RTX ?</p>
<p>I am not a CS student , this is just a hobby for me so I want to spend as little as I can. I want to learn image recognition. I would probably wait for RTX Ti cards which will have more memory. It should be cheap enough and give you a bit more memory 10GB. I see in another comment you recommended the over the XT since Nvidia has tensor cores — but what do you think about the fact that the XT has quite a bit more memory than the ?</p>
<p>It also draws slightly less power, and has a lower sticker price, which are both appealing on the cost front. Do you really expect the performance discrepancy between the cards to be significant enough offset the benefits of the extra memory?</p>
<p>I am thinking about 2x vs 2x XT. I do not think AMD will catch up in cross-node communication for some time. The problem with the RX XT might be that you are not able to use it in the first place.</p>
<p>This might be changing in the future, but it seems it is not straightforward to use these new GPUs right out of the box. So it is mostly a tradeoff between speed vs memory if ROCm works. Apple recently released a tensorflow fork with hardware acceleration for macs.</p>
<p>Do you think this is apple trying to be competitive in deep learning or is it just adding support just because they can? It is just adding support, it seems. The Apple M1 processor is not powerful enough to train neural networks but will be very useful to prototype neural networks that you will deploy on iPhones.</p>
<p>As such, it is an excellent processor to work with deep learning on iPhones, but you would probably train neural networks on GPU servers and transfer the weights to your MacBook to do further prototyping on the already trained network.</p>
<p>ASICs are great! TPUs are solid, just as you said. If startups shoulder that cost, there is still the software and community problem. If this is not available, it gets difficult. The main problem with ASICs is usability. The fastest accelerator is worthless if you cannot use it! That is why all Intel accelerators failed. Once you get a usable ASIC, it is about community.</p>
<p>NVIDIA GPUs have such a large community that if you have a problem, you can find a solution easily by googling or by asking a random person on the internet. With ASICs, there is no community, and only experts from the company can help you. I had a couple of follow-up questions regarding a build I want to do in the near future early is the target, for now.</p>
<p>The is off the table since prices are already absurdly inflated in my country. Also, is there any particular AIB model you would recommend? I can go with up to 3-slot GPUs. With that, I would probably go with an RTX  The VRAM on that one is a little small, though. It is a difficult choice. Can you confirm it? There is a fair chance that you have to replace GPU every few weeks. Some vendors have guarantees on RTX cards for data centers, but this is rare and might incur extra costs.</p>
<p>I see you do not discuss much about the ti in your latest recommendations. Is this because it is not manufactured any more? I still see some ti as b-stock about the same price as where I am. I also considered the , but it seems a bit overly expensive for other than the big memory.</p>
<p>However, memory often seems a limiting factor. Does this also enable training bigger models that do not fit in the memory of a single GPU?</p>
<p>But sometimes I like to finetune some transformers and train some of the bigger CV models etc. If you can find it cheap, it is definitely worth picking up.</p>
<p>If memory is a problem, you might also want to wait Q1 for the release of the new RTX Ti etc which have extended memory. For now, a single RTX will be better for training large models. Picking up the right motherboard is really tricky though. I am trying to build a system with only 1 RTX but I want the system to be expandable. Am I right until here?</p>
<p>Or does it make sense? And the 2nd problem I have. Lets say 2 GPU-system is my only option because of budget restrictions. Motherboard descriptions are not explicit. Most of the time the descriptions breakdown lane usage by CPU generation. For example:. Do you know how can I understand it? Hi Gokhan, It depends. Usually running 3 GPUs at 4 lanes each is quite okay.</p>
<p>Parallelism will not be that great, but it can still yield good speedups and if you use your GPUs independently you should see almost no decrease in performance.</p>
<p>You can usually find this information in the Newegg specification section of the motherboard in question. Thank you Tim. This is the cheapest board I could find and I hope it will work. And they said yes it is possible. It matches with the calculations you did on the article. Also currently a large majority of PCI-e extenders are 3. Especially when not using large NN? Hi Arnaud, I see sklearn more like an exploration tool.</p>
<p>I think you can always explore algorithms on a smaller scale and then use dedicated GPU implementations. For R, GPUs should be used. Similarly, I believe. Hi Tim. A single rail is usually better because it has a standard form factor, which allows using standard cases. Otherwise, use still air but buy the blower GPU variant.</p>
<p>Thanks for the link with the info. It indeed seems these GPUs are not supported immediately. They might be added later, but there does not seem to be a big official push since ROCm is designed for datacenter GPUs. Thanks for this nice article. As I constantly got OOM error in a current Kaggle competition 16G memory for training a graph transformer , I snabbed one on newegg and started building a single card rig behind it. Now I have a simple question: should I use the integrated graphics on CPU to connect with the monitor for display purposes?</p>
<p>Does connecting the graphics card with a dual-monitor in QHD affect the performance of the graphics card during training? Usually, the displays do not need that much memory. Thanks Tim. However, it does not seem possible to do the same thing on Windows. Do you have a reference or other justification for the utilization rates you state?</p>
<p>I copy them below for your convenience. Hi Brad! At any one time, GPUs were used. Thanks for such a great article. Which is the best workstation GPU in terms of performance vs cost ratio? Thanks again. Workstation GPUs are more expensive though and sometimes they have more memory. I do not recommend workstation GPUs. They lack of Tensor Cores, but overall are good choice for most of the games and pro software. Some competition is always good. Anyone have used them?? What are your opinions about those docker-based apps?</p>
<p>Is process of instalation straightforward? Docker-based options should be pretty straightforward to install. Thanks for the detailed information. To make an example: If your mini-batch takes ms through the network and use transfer a batch of 32 ImageNet images xx3 then the transfer on 4 PCIe lanes will take 2. I happily got my old Radeon Pro WX working with rocm 3.</p>
<p>Hi Miguel! You might have some usability issues here and there, but if you are already using ROCm 3. If you want to support the community, buying an AMD GPU and writing an experience report about it would be very helpful and valuable! Hi Tim and other readers of this great source. I plan to put in one rtx for now, but would like to build it such that I can add up to 3 more cards. Considering all the potential cooling and power issues, I am open to a two chassis build.</p>
<p>Once chassis could host my cpu, ram, storage, power supply etc. Basically a PC. I am ok even if I need 4 such cables, one for each GPU. Can this kind of Frankenstein build work? There is now more information about cooling and power. It seems power limiting works well and does not limit performance much.</p>
<p>Also cooling can be done with ordinary blower-style GPUs. Thanks so much. This looks encouraging. Especially as I am considering s, and at V, so just 10amps draw. And maybe a lower TDP processor like Ryzen 7 , Will keep an eye out for updates on the blower editions. I read around a bit more and a couple of other things I realized: 1. It is a lot easier to plug in a few pcie cables than itnis to assemble a whole pc.</p>
<p>Having an external enclosure with its own power also means I can leave the GPUs off and use only the regular pc. So if it is possible, I still want to try the frankenbuild option. Will such a build work?</p>
<p>Any big stumbling blocks? Would deeply appreciate any pointers. See at Microsoft opens in new tab. Richard Devine is an Editor at Windows Central. Currently you'll find him covering all manner of PC hardware and gaming, and you can follow him on Twitter and Instagram. Windows Central Windows Central. Richard Devine. More about microsoft.</p>
<p>Microsoft Rewards now supported in 38 additional countries, including This new interactive map shows the scale of Microsoft Azure's global c See all comments  Pretty much all VM solutions made by different vendors.</p>
<p>Some are bare-metal hyperviors as opposed to being on top of a host OS Virtual Box is a free desktop operating system VM good for end user applications. VMware Workstation is a paid VM hypervisor good for desktop and server OSes, and has great hardware acceleration for playing early Windows games.</p>
<p>Personally, I've had the most fun playing with VMware Workstation. Virtual Box has limitations, and Hyper-V didn't work well with Linux or with hardware accelerated applications like games. Have you tried use RemoteFX hyperv's feature? They are competing virtualization technologies. There are others too, but at the end of the day, it comes down to a combination of use-case, preference, and cost.</p>
<p>There are many articles covering features of each in great detail. Best approach when starting is looking for features you need for your use case first to eliminate those that won't meet your needs. Feel free to hit me up offline if you have specific questions, as this topic can get very involved very quickly. Being able to join Office AzureAD is an awesome feature, especially for small business environments where you don't have an on premise server running anymore.</p>
<p>I hope Microsoft elaborates on the integration features between Windows 10 and Office AzureAD, where a user logs in with his Office account and finds everyting set up, from his OneDrive, his Outlook Exchange account, Office apps, roaming of settings like browser favorites, etc. It certainly isn't something that a small company without any IT staff could or should set up. It is so frustrating when you need to get some work done quickly but the computer restarts automatically to update which could take a long time depending on the update.</p>
<p>You also have Group Policy editor on Pro You can trim down Windows updates even more. I have it set to Notify me only and notfiy me to download. So, if a update comes up when I am doing something and skip it for now Also set no reboot with user logged on. I may be mistaken, but restart after updates is being scheduled for 3 o'clock in the morning. Why everybody feels the need to update directly is beyond me. Also, there are a couple of tools like ShutUp They have active hours and they will be further expanding active hours.</p>
<p>Honestly, updates don't take that long especially if you have a SSD, but I know not everyone does. I understand putting updates off and I'm guilty of it as well, but it's ultimately better to patch the computer.</p>
<p>Plus when you put updates off for long enough, it does tell you when it will actually restart the computer to install. I get it can be annoying sometimes, but I personally don't think it's that big of a deal at the end of the day.</p>
<p>For a home user: - domain not needed. Homegroup and network sharing should be enough. In need there are open source encryption tools VeraCrypt, KeePass, depending on needs. I see no need to buy a Pro license for a home user, especially when some technologies that might interest a typical user, can be replaced with free alternatives. I was with you until you kind of contradict yourself: "For a home user For a 'Home' user, apps from the Store are much safer than x86 super spammed programs!</p>
<p>You might wanna read the article first. Apps in the store does not mean access to apps in the windows store. All versions of Windows 10 have that. It's your own private section that you can use to develop and "publish" apps too.</p>
<p>I agree for the most part, but deferring Windows Updates is a worthwhile Pro feature. I know many people that don't upgrade right away just in case there are issues. Active hours are extended too!</p>
<p>TeamViewer is my favorite for maintenance but for those working in applications from home, Remote Desktop is a far better experience. I guess you don't encrypt your phone and data as well then? Simple answer, no its not worth it. I wish I could by Windows 10 Enterprise, thats worth it, but they dont sell it to individuals. Upgrade from Home to Pro costs around dollars. I needed that for Hyper-V. I do need some domain resources so it's usefull for me at home.</p>
<p>On home, it's mostly registry settings for everything and can be a pain to hunt down if you dont know what your looking for. Who ever down voted me is a cluless moron You really are just a fanboy who has no clue on how it all works With all of the rhetoric of there being 'One Windows' I am always annoyed about the difference between Home and Pro versions, and even more annoyed at the cost of Windows in general.</p>
<p>There should be a consumer version of Windows that is essentially what we know as 'pro' today with a normal MAK key that is tied to the hardware, and then there should be an enterprise version with KMS keys for doing stuff like network deployments. That should be it, and the only difference between the two should be how it is licensed and deployed. And the price! The price is insane! Does MS not see what is happening?</p>
<p>When I was a kid I could not afford Windows, so I did what every kid in the '90s did and pirated it, or got a free win95 key from a forum on the internet.</p>
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<p>RecSys ' ISBN  S2CID  Digital Trends. Will it ever make a comeback? USA Today. London: Telegraph. Archived from the original on January 12,  BBC News. December 4,  Retrieved March 30,  Archived from the original on December 27,  January 30,  Isn't it rather crucial to understand over half of the existing content on the site? Why not just prevent people from adding details to new questions while retaining the details attached to the old ones? Retrieved November 21,  Bots or humans?</p>
<p>Today, it is just another pit of racism on the web. Seemingly every other question is a passive aggressive dig at Black Lives Matter, or an anonymous dog-whistle racist comment on whatever is in the news that day. Good Men Project. Most of the answers that I received completely denied that systematic racism even exists. And of course, the people denying its existence at least the ones with photos were all white people.</p>
<p>The Algemeiner. People's Daily in Chinese. December 18,  Retrieved February 9,  Retrieved July 1,  Business Insider. October 29,  What Quora feature s should be improved or eliminated? Retrieved October 10,  Retrieved April 27,  April 19,  Retrieved August 2,  Retrieved May 5,  Retrieved March 28,  Retrieved September 30,  Retrieved August 22,  Retrieved July 26,  Retrieved September 17,  Retrieved July 16,  Quora Help Center.</p>
<p>Telegraph - UK. Wikimedia Commons has media related to Quora. Wikidata has the property: Quora topic ID P see uses. Categories : Companies based in Mountain View, California Internet properties established in Privately held companies based in California Question-and-answer websites Y Combinator companies Multilingual websites American websites.</p>
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<p>Namespaces Article Talk. Views Read Edit View history. The new system aims at conquering the cloud space as some providers like Digital Ocean are Linux-driven. Not only is. NET Core cross-platform but its different versions can also be installed side by side on the same device. NET that happened to be a more modular framework than its predecessor.</p>
<p>NET Core allows you to build the mobile backend, web apps, and services. NET Core counts many new features including Hot Reload that enables the application of changes to C , Visual Basic, and CSS source files without having to restart or rebuild the app the app is running while you are updating the code. The third implementation is called Xamarin and is used for mobile applications and Mac products. Originally, Xamarin was designed independently from Microsoft and was a proprietary product.</p>
<p>Then Microsoft acquired it in making it a fully open-source branch of the. NET platform. Xamarin uses the Mono runtime and a version of the. To get an elaborate overview of this product, check our Xamarin pros and cons article. All runtimes use a common infrastructure that makes the entire ecosystem work.</p>
<p>It provides runtime components, languages, and compilers. UWP app developers get access to the Microsoft store that charges only 15 percent for non-gaming subscription-based apps, unlike Google Play Store and App Store. NET 5 and. In Microsoft also introduced. NET Core, and Xamarin allowing engineers to use a single base-class library rather than mastering three different ones related to each.</p>
<p>NET implementation. This step unified the ecosystem and brought a higher consistency to reusing components across different platforms. NET development platform is best served with Visual Studio IDE used for building, debugging, and publishing applications across all platforms and devices.</p>
<p>NET, an application virtual machine that manages memory, implements code access security, verifies code safety, and provides execution of threads and code. CLR is what makes the. NET code a managed one. Besides, it allows engineers to design systems with multiple languages, as CLR enables them to communicate and integrate their behaviors.</p>
<p>The runtime checks the needed versions of applied services to ensure that all dependencies are intact and the code works as intended. While nearly identical to CLR in. NET Core cross-platform makeup. The languages that you can use with. NET can be ultimately divided into two major groups: 1 the main officially supported languages by Microsoft, and 2 the rest of the languages that are CLI-compliant.</p>
<p>Main languages. Most of the. All three saw great improvements within the. NET 6 release. C 10 and F 6 were introduced, both aiming at simplifying code and making it more performant. The rest of CLI-compliant languages. CLI means common language infrastructure. NET guide — the general documentation by Microsoft for newbies;. NET Core guide — for those considering transitioning to the Core version, go here;. NET framework — the original.</p>
<p>NET documentation;. Xamarin — the docs for mobile development;. NET foundation — the headquarters of the. NET open-source community;. NuGet — the place to find all. NET tools — our take on the best tools, supported by expert opinion;. NET Core and framework — download it here; and. NET developer community — all about chatter and support with links to social media, forums, blogs, live workshops, and organizations like.</p>
<p>NET foundation. OOP is a development model for breaking down software into smaller pieces that are easier to manage and combine. OOP compartmentalizes data into objects, i. A modular structure helps define the behavior of objects and their interactions without managing object inner attributes. OOP simplifies programming by making the code manageable, easier to test, and respond to recurring issues.</p>
<p>It also eliminates unnecessary programming and generally means less coding for developers. NET makes it possible to reuse code and components, save time, and — subsequently — the cost of development. Caching means keeping data in a temporary storage place to call it faster when needed. The stored data is the result of an earlier information request or the duplicate of information stored in other places that would take longer to retrieve.</p>
<p>The caching system in. NET is robust and simple to use. The Object Cache class allows developers to create a custom cache implementation to be used for improving the performance and scalability of Windows client and server applications. An Integrated Development Environment IDE is a single product for programmers that makes application development easier by providing all the tools needed to write and test software.</p>
<p>Visual Studio is integrated with. NET and provides the features of a language-specific environment. Additionally, Visual Studio Marketplace offers a wide range of editor extensions from Microsoft and other providers to enable team collaboration, continuous integration , third-party connections, cloud development management, etc.</p>
<p>NET 6. The roll-out is the first bit version of the IDE itself and is stuffed with useful features. Among other things, Visual Studio contains IntelliCode — special capabilities that use Machine Learning to complete chunks of code for you, recognize repeated edits, and suggest fixes. NET Core is a cross-platform. Unlike the original. NET framework — which is not completely open —. NET Core has a fully open-source code that ensures that a wide engineering community can continuously contribute to its development.</p>
<p>This enables companies to reach the widest variety of platforms staying within the. NET ecosystem. At the same time, the cross-platform design ensures that the whole. NET community with a large pool of different engineering skillsets shares the experience. One of the important. NET Core features is flexible deployment. It can be installed as a part of your application or require a separate installation. The modular design contemplates including all the dependencies you need. The deployment itself is as easy as copying a folder.</p>
<p>Microsoft showed this feature off, with Powerpoint, Edge and Teams all displayed simultaneously, and it looked effortlessly slick. As such, it became an optional app as an alternative to the likes of Slack. For Windows 11, Teams will play a much bigger part.</p>
<p>The Chat functionality will be built into the main taskbar, making it very easy and quick to send a colleague or friend a message. Microsoft is rebuilding the Microsoft Store from the ground up for Windows 11, introducing a cleaner and more organised layout. Microsoft showed Skyrim as an example, and the improved colours were immediately obvious. DirectX 12 Ultimate will also be supported, ensuring you can get the best possible gaming performance when paired with high-end components.</p>
<p>Editorial independence means being able to give an unbiased verdict about a product or company, with the avoidance of conflicts of interest. On every commit.</p>
<p>Welcome to the future of app testing. Specify a group of testers or create an open beta recruitment page. Connect that group to a branch to automatically send updates whenever code is merged. App Center tracks your builds through every release. Build your app in App Center, and get automatic symbol management for richer reports.</p>
<p>Crash reports are grouped by common cause, highlighting the relevant stack frame so you can locate errors by file and line number. Search specific users' crashes and browse individual reports for event breadcrumbs and custom data attachments.</p>
<p>Developer-friendly analytics make it easy to discover how to improve your apps. See streaming activity for sessions and events immediately, and collect metrics that become more useful over time. Is your audience growing? What devices and operating systems are most common? What features are popular?</p>
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<p>Express editions still exist even in , but "express" editions are now aimed as light platforms for individual targets i. Even then the official Express download site suggests people really should consider Community - why wouldn't you? This is another thing which is HOT nowadays for nerds and multi-platform users. It is not even close to Visual Studio. Relevant blog post blogs. Add a comment. Sorted by: Reset to default. Highest score default Trending recent votes count more Date modified newest first Date created oldest first.</p>
<p>Improve this answer. Peter Mortensen  According to the license terms visualstudio. NB: Generally speaking, Visual Community Edition cannot be used by large companies for commercial projects: visualstudio. Visual Studio Code is cross-platform and fast, while Visual Studio is not fast. John Papa John Papa  If I ignore platform independence for a second do we have any other use case which can motivate me to use VS Code?</p>
<p>Isn't it? You might find a better set of extensions available for Code than for Community. They support different extensibility points, so their extension sets are mutually exclusive. If you use more esoteric less Microsoft-y workflows, you might find the Code extension marketplace more suitable. MassimilianoKraus Good question.</p>
<p>Other editors also debugging atom, for example. I think if IDE's as a do-it-all with a lot of built in extras e. Maybe there is a better explanation on the web somewhere : — John Papa. Show 3 more comments. If you really look at it the most obvious difference is that. NET has been split into two:. Visual Studio Code: It's a lightweight source code editor which can be used to view, edit, run, and debug source code for applications.</p>
<p>It is mainly oriented around files, not projects. It does not have any scaffolding support. It is a competitor of Sublime Text or Atom on Electron. It is based on the Electron framework, which is used to build cross platform desktop application using web technologies.</p>
<p>It does not have support for Microsoft's version control system; Team Foundation Server. It has limited IntelliSense for Microsoft file types and similar features. Visual Studio: As the name indicates, it is an IDE, and it contains all the features required for project development. Like code auto completion, debugger, database integration, server setup, configurations, and so on.</p>
<p>It is a complete solution mostly used by and for. NET related developers. It includes everything from source control to bug tracker to deployment tools, etc. It has everything required to develop. It is widely used on. NET related projects though you can use it for other things. NET ecosystem. At the same time, the cross-platform design ensures that the whole. NET community with a large pool of different engineering skillsets shares the experience. One of the important. NET Core features is flexible deployment.</p>
<p>It can be installed as a part of your application or require a separate installation. The modular design contemplates including all the dependencies you need. The deployment itself is as easy as copying a folder. Another benefit is that you can have multiple. NET Core versions running side by side on the same machine to cover different projects and seamlessly perform deployment tasks.</p>
<p>Since , the. NET ecosystem has been enhanced with a large class library called. NET Standard. NET Core, and Xamarin. These are used to work with common functions such as graphics rendering, database interaction, and manipulations with XML documents.</p>
<p>Prior to. NET Standard, a programmer had to redevelop an application or a library for the new platform and then distribute all the updates across various platforms.</p>
<p>Currently, the library supports all dependent libraries across applications. However, you should check version compatibility to successfully leverage. You may check your product compatibility with.</p>
<p>NET Standard here and track updates on the Microsoft documentation site. For those of you who are wondering just how popular.</p>
<p>NET is, a large community of developers is the answer. Interestingly, it unites engineers from small, midsize, and enterprise-grade companies.</p>
<p>This means that almost any issue can be resolved with the help of community members. NET Core is open source, its libraries, runtime, and compiler are available on GitHub and have many contributions.</p>
<p>NET took fifth place among the most popular web frameworks. Most popular web frameworks according to professional developers. Source: Stack Overflow Survey  At the same time,. NET Framework with. NET Core placed first and second respectively as the most used frameworks beyond web development. NET Core, and. Besides that,. NET Foundation fosters open development and teamwork around the. NET community and commercial developers use it as the main forum for idea exchange.</p>
<p>NET has built-in automatic monitoring. The Windows Web Server strictly monitors web pages and applications that run on it. In case any issues such as memory leaks or infinite loops occur, it immediately alerts about them.</p>
<p>This allows for directly correcting these behaviors and creating new processes. The monitoring ensures higher stability and transparency of. NET applications. Both compilers are highly regarded by the. NET community as Microsoft actively improves them.</p>
<p>Roslyn has been available since  In , it became open source and in was included in Visual Studio. This means that, unlike traditional black-box compilation, with Roslyn, developers can read the compilation data at every stage.</p>
<p>The full transition from the old JIT was completed in with the idea of improved performance. And, generally, engineers agree that RyuJIT is faster. According to. But the point is, Microsoft is really good at what they are doing. Microsoft does its best to further evolve. The transition to.</p>
<p>NET Core was a major step in terms of the ecosystem development, as it embraced cloud and focused on performance improvements. In Microsoft introduced the ML. NET framework for C and F languages. NET 6 are already here with. NET 7 being projected for  If you keep your applications constantly updated with the newest.</p>
<p>NET versions, migrating from. You first change the target framework from. Then you upgrade the rest of the packages within the project and run QA processes to see how everything performs. Unlike the above, the migration from. NET Framework to. NET Core or between some. NET Core versions is not a walk in the park. In case your projects are based on earlier versions like.</p>
<p>NET 5 before making the move towards the latest release is recommended. Even though. NET is considered one of the strongest engineering platforms out there due to extensive infrastructure and proven product development history, it comes with a set of problems.</p>
<p>As we mentioned above,. The support of data-oriented software application development in. Entity is an object-relational mapper ORM that links object-oriented. Another problem is that the framework may eventually be abandoned by Microsoft. On the bright side, you can always choose another ORM alternative, so it remains a debatable point. Mostly, your expenses will be spent on Visual Studio IDE and other additional collaboration and quality assurance services that Microsoft offers to simplify your work.</p>
<p>However, you still can try to become a Microsoft Partner to have a number of subscriptions for free. For non-profit and educational purposes, there is also the free Visual Studio Community. Even though you can use. NET engineering, which also comes with licensing costs. Perhaps, this problem cripples all Microsoft products, not. NET stack only. The newly released products lack proper documentation, support, stability, and are prone to drastic changes.</p>
<p>In one of the AltexSoft projects , our architects decided to use a fresh DocumentDB now Azure Cosmos DB but then were forced to design custom data architecture as the product suffered a number of technical issues that could jeopardize the entire project. NET 6 release is claimed to have 3 years of official support and proper documentation on use and migration. The problem of memory leaks is not uncommon in any technology.</p>
<p>NET is frequently criticized for memory leaks and memory-related issues. NET has a garbage collector for this sort of problem, engineers still have to invest additional efforts into proper resource management. And the leaks keep on growing as the application scales. Fortunately, engineers can find detailed and effective recommendations for detecting leaks in. As always happens, the transition period may be long and difficult.</p>
<p>The applications built with the old. NET Framework outnumber those with. NET Core. In corporate environments, the transition may not be prioritized as much. Nonetheless, these systems need support. Another related problem here is that the mature. NET Framework had many supporting projects around. For example, many developers expressed concerns about abandoning Windows Communication Foundation WCF , a runtime and API set for service-oriented applications used by many.</p>
<p>NET practitioners. Microsoft decided not to port WCF to Core. Instead, they leveraged community effort, as one of. Developers will have to spend a little extra effort to complete a transition. Scott Hunter, from. NET Web Forms,. NET Remoting, and some other services in. You also get a view of Live Tiles, with a look at the weather, your photo album and various Microsoft apps.</p>
<p>The Windows 11 Start Menu instead displays your pinned apps, with a search bar letting you locate your less used programmes. This results in a more personalised experience, even letting you align the taskbar to the left of your screen if you prefer the older layout.</p>
<p>Windows 11 is introducing Snap Layouts, which makes it easier to have multiple windows open at once. Simply pick from one of the different templates, and then your selected windows and apps will automatically snap into place and resize accordingly. Microsoft showed this feature off, with Powerpoint, Edge and Teams all displayed simultaneously, and it looked effortlessly slick.</p>
<p>As such, it became an optional app as an alternative to the likes of Slack. For Windows 11, Teams will play a much bigger part. The Chat functionality will be built into the main taskbar, making it very easy and quick to send a colleague or friend a message. Microsoft is rebuilding the Microsoft Store from the ground up for Windows 11, introducing a cleaner and more organised layout. Microsoft showed Skyrim as an example, and the improved colours were immediately obvious.</p>
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<p>Deep learning is a field with intense computational requirements, and your choice of GPU will fundamentally determine your deep learning experience. But what features are important if you want to buy a new GPU?</p>
<p>How to make a cost-efficient choice? This blog post will delve into these questions, tackle common misconceptions, give you an intuitive understanding of how to think about GPUs, and will lend you advice, which will help you to make a choice that is right for you.</p>
<p>These form the core of the blog post and the most valuable content. You might want to skip a section or two based on your understanding of the presented topics.</p>
<p>I will head each major section with a small summary, which might help you to decide if you want to read the section or not. This blog post is structured in the following way.</p>
<p>First, I will explain what makes a GPU fast. These explanations might help you get a more intuitive sense of what to look for in a GPU. If you use GPUs frequently, it is useful to understand how they work. This knowledge will come in handy in understanding why GPUs might be slow in some cases and fast in others. In turn, you might be able to understand better why you need a GPU in the first place and how other future hardware options might be able to compete.</p>
<p>You can skip this section if you just want the useful performance numbers and arguments to help you decide which GPU to buy. The best high-level explanation for the question of how GPUs work is my following Quora answer:. If we look at the details, we can understand what makes one GPU better than another. This section can help you build a more intuitive understanding of how to think about deep learning performance. This understanding will help you to evaluate future GPUs by yourself.</p>
<p>It is useful to understand how they work to appreciate the importance of these computational units specialized for matrix multiplication. This is a simplified example, and not the exact way how a high performing matrix multiplication kernel would be written, but it has all the basics. To understand this example fully, you have to understand the concepts of cycles. Each cycle represents an opportunity for computation.</p>
<p>However, most of the time, operations take longer than one cycle. Thus it creates a pipeline where for one operation to start, it needs to wait for the number of cycles of time it takes for the previous operation to finish.</p>
<p>This is also called the latency of the operation. Furthermore, you should know that the smallest units of threads on a GPU is a pack of 32 threads — this is called a warp. Warps usually operate in a synchronous pattern — threads within a warp have to wait for each other. All memory operations on the GPU are optimized for warps. The resources of an SM are divided up among all active warps.</p>
<p>For both of the following examples, we assume we have the same computational resources. A memory block in shared memory is often referred to as a memory tile or just a tile. We have 8 SMs with 8 warps each, so due to parallelization, we only need to do a single sequential load from global to shared memory, which takes cycles. To do the matrix multiplication, we now need to load a vector of 32 numbers from shared memory A and shared memory B and perform a fused multiply-and-accumulate FFMA.</p>
<p>Then store the outputs in registers C. Why this is exactly 8 4 in older algorithms is very technical. This means we have 8x shared memory access at the cost of 20 cycles each and 8 FFMA operations 32 in parallel , which cost 4 cycles each. In total, we thus have a cost of:. To do that, we first need to get memory into the Tensor Core. Similarly to the above, we need to read from global memory cycles and store in shared memory.</p>
<p>A single SM has 8 Tensor Cores. So with 8 SMs, we have 64 Tensor Cores — just the number that we need! We can transfer the data from shared memory to the Tensor Cores with 1 memory transfers 20 cycles and then do those 64 parallel Tensor Core operations 1 cycle. This means the total cost for Tensor Cores matrix multiplication, in this case, is:.</p>
<p>Thus we reduce the matrix multiplication cost significantly from cycles to cycles via Tensor Cores. While this example roughly follows the sequence of computational steps for both with and without Tensor Cores, please note that this is a very simplified example. Real cases of matrix multiplication involve much larger shared memory tiles and slightly different computational patterns.</p>
<p>However, I believe from this example, it is also clear why the next attribute, memory bandwidth, is so crucial for Tensor-Core-equipped GPUs. Since global memory is the most considerable portion of cycle cost for matrix multiplication with Tensor Cores, we would even have faster GPUs if the global memory latency could be reduced.</p>
<p>We can do this by either increasing the clock frequency of the memory more cycles per second, but also more heat and higher energy requirements or by increasing the number of elements that can be transferred at any one time bus width.</p>
<p>From the previous section, we have seen that Tensor Cores are very fast. So fast, in fact, that they are idle most of the time as they are waiting for memory to arrive from global memory. Since memory transfers to the Tensor Cores are the limiting factor in performance, we are looking for other GPU attributes that enable faster memory transfer to Tensor Cores. Shared memory, L1 Cache, and amount of registers used are all related.</p>
<p>To understand how a memory hierarchy enables faster memory transfers, it helps to understand how matrix multiplication is performed on a GPU. To perform matrix multiplication, we exploit the memory hierarchy of a GPU that goes from slow global memory to fast local shared memory, to lightning-fast registers. However, the faster the memory, the smaller it is. As such, we need to separate the matrix into smaller matrices. We perform matrix multiplication across these smaller tiles in local shared memory that is fast and close to the streaming multiprocessor SM — the equivalent of a CPU core.</p>
<p>With Tensor Cores, we go a step further: We take each tile and load a part of these tiles into Tensor Cores. Having larger tiles means we can reuse more memory. As such, TPUs can reuse much more memory with each transfer from global memory, which makes them a little bit more efficient at matrix multiplications than GPUs.</p>
<p>We have the following shared memory sizes on the following architectures:. We see that Ampere has a much larger shared memory allowing for larger tile sizes, which reduces global memory access. Thus, Ampere can make better use of the overall memory bandwidth on the GPU memory. The performance boost is particularly pronounced for huge matrices.</p>
<p>The Ampere Tensor Cores have another advantage in that they share more data between threads. This reduces the register usage. Registers are limited to 64k per streaming multiprocessor SM or per thread. Comparing the Volta vs Ampere Tensor Core, the Ampere Tensor Core uses 3x fewer registers, allowing for more tensor cores to be active for each shared memory tile. In other words, we can feed 3x as many Tensor Cores with the same amount of registers.</p>
<p>Overall, you can see that the Ampere architecture is optimized to make the available memory bandwidth more effective by using an improved memory hierarchy: from global memory to shared memory tiles, to register tiles for Tensor Cores. This section is for those who want to understand the more technical details of how I derive the performance estimates for Ampere GPUs.</p>
<p>If you do not care about these technical aspects, it is safe to skip this section. Putting together the reasoning above, we would expect the difference between two Tensor-Core-equipped GPU architectures to be mostly about memory bandwidth. This puts the speedup range between 1. With similar reasoning, you would be able to estimate the speedup of other Ampere series GPUs compared to a Tesla V So in a sense, the benchmark numbers are partially honest, partially marketing numbers.</p>
<p>In general, you could argue that using larger batch sizes is fair, as the A has more memory. Still, to compare GPU architectures, we should evaluate unbiased memory performance with the same batch size. To get an unbiased estimate, we can scale the V and A results in two ways: 1 account for the differences in batch size, 2 account for the differences in using 1 vs 8 GPUs.</p>
<p>I benchmarked the same problem for transformers on my RTX Titan and found, surprisingly, the very same result:  As we parallelize networks across more and more GPUs, we lose performance due to some networking overhead. This means if going from 1x A to 8x A gives you a speedup of, say, 7. Using these figures, we can estimate the speedup for a few specific deep learning architectures from the direct data that NVIDIA provides.</p>
<p>The Tesla A offers the following speedup over the Tesla V Thus, the figures are a bit lower than the theoretical estimate for computer vision. This might be due to smaller tensor dimensions, overhead from operations that are needed to prepare the matrix multiplication like img2col or Fast Fourier Transform FFT , or operations that cannot saturate the GPU final layers are often relatively small. It could also be artifacts of the specific architectures grouped convolution.</p>
<p>The practical transformer estimate is very close to the theoretical estimate. This is probably because algorithms for huge matrices are very straightforward.</p>
<p>I will use these practical estimates to calculate the cost efficiency of GPUs. The estimates above are for A vs V It might be possible that there are unannounced performance degradations in the RTX 30 series compared to the full Ampere A As of now, one of these degradations was found: Tensor Core performance was decreased so that RTX 30 series GPUs are not as good as Quadro cards for deep learning purposes. This was also done for the RTX 20 series, so it is nothing new, but this time it was also done for the Titan equivalent card, the RTX  The RTX Titan did not have performance degradation enabled.</p>
<p>I will update this blog post as information about further unannounced performance degradation becomes available. Other features, such as the new data types, should be seen more as an ease-of-use-feature as they provide the same performance boost as Turing does but without any extra programming required. Ampere allows for fine-grained structure automatic sparse matrix multiplication at dense speeds.</p>
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