Tesla p40 fp16 reddit. The P40 driver is paid for and is likely to be very costly.

Tesla p40 fp16 reddit Q5_K_M. I guess the main question is: Does the Tesla P40's lack of floating-point hamper performance for int8 or int4 While I can guess at the performance of the P40 based off 1080 Ti and Titan X(Pp), benchmarks for the P100 are sparse and borderline conflicting. X16 is faster then X8 and x4 douse not work with p40. 8tflops for the 2080. I was wondering what they do and what I could do with Skip to main content. r/LocalLLaMA. Maybe Note the P40, which is also Pascal, has really bad FP16 performance, for some reason I don’t understand. Old. I have a Dell precision tower 7910 with dual Xeon processors. Usually on the lower side. Theoretically, it will be better. Except for the P100. Llamacpp runs rather poorly vs P40, no INT8 cores hurts it. My main reason for looking into this is due to cost. I always wondered about that. It features 3840 shading units, 240 texture mapping units, We compared two Professional market GPUs: 24GB VRAM Tesla P40 and 12GB VRAM Tesla M40 to see which GPU has better performance in key specifications, benchmark tests, power consumption, etc. About 1/2 the speed at inference. It doesn’t matter what type of deployment you are using. r/pcmasterrace A chip A close button. hello, I run the fp16 mode on P40 when used tensor RT and it can not speed up. as quantization improvements have allowed people to finetune smaller models on just 12gb of vram! meaning consumer Get the Reddit app Scan this QR code to download the app now. But for now it's only for rich people with 3090/4090 Other affordable Got myself an old Tesla P40 Datacenter-GPU (GP102 like GTX1080-silicon but with 24GB ECC vram, 2016) for 200€ from ebay. Log In / Sign Up; Advertise I found a local vendor who has a load of these things, and I plan on grabbing one of these on the cheap. And the fact that the K80 is too old to do anything I wanted to do with it. r/hardware A chip A close button A chip A close button 3090s are faster. I ran all tests in pure shell mode, i. Skip to main content. Title. Got a couple of P40 24gb in my possession and wanting to set them up to do inferencing for 70b models. Works great with ExLlamaV2. The Upgrade: Leveled up to 128GB RAM and two Tesla P40's. But you are looking at close to double the time to train without nvlink. Reply reply SomeOddCodeGuy • Are the P100s simply less popular because they are 16GB instead of 24GB? I see tons of P40 builds but so rarely see P100 builds. I get between 2-6 t/s depending on the model. Please use our Discord server instead of supporting a company that No, it just doesn't support fp16 well, and so code that runs LLMs shouldn't use FP16 on that card. Modded RTX 2080 Ti with 22GB Vram. It may be wrong, but I think the most significant advantage of The Nvidia Tesla P40 isn't 2 gpus glued together. gguf only the rtx3090 (GPU 0) and the CPU. While doing some research it seems like I need lots of VRAM and the cheapest way would be with Nvidia P40 GPUs. It will be 1/64'th that of a normal Pascal card. py and building from source but also runs well. The P40 for instance, benches just slightly worse than a 2080 TI in fp16 -- 22. Yes, you get 16gigs of vram, but that's at the cost of not having a stock cooler (these are built for data centers with constant air flow) and thus if you don't want to fry it, you have to print your own or buy one (a 1080 might fit). 4 iterations per second (~22 minutes per 512x512 image at the same settings). My 12 inch fan with 3d printed duct Preisvergleich für PNY Tesla P40 Produktinfo ⇒ GPU: NVIDIA Tesla P40 • Speicher: 24GB GDDR5X mit ECC-Modus, 384bit, 14. How to force FP32 for video card in Pascal - P40 . What models/kinda speed are you getting? Skip to main content. Curious on this as well. I'm using a Tesla P40. If this is true then Nvidia are But that guide assumes you have a GPU newer than Pascal or running on CPU. And the P40 in FP32 for some reason matched the T4 on FP16, which seemed really odd, given that the T4 has 6 times the performance FP16 as the P40 in FP32. I just installed an M40 into my R730, which has the same power as the P40. Best . But when using models in Transformers or GPTQ format (I tried Transformers, AutoGPTQ, all ExLlama loaders), the performance of 13B models even in quad bit format is terrible, and judging by power The P40 is restricted to llama. As far as pricing goes, 2080 supers are about similar price but with only 8gb of vram Though sli is possible as well. As I'm also GPU: MSI 4090, Tesla P40 Share Add a Comment. Log In / Sign Up; Advertise on Reddit; Shop Collectible Avatars; Get the Reddit app Scan this QR code to download the app now. Anyway, it is difficult to track down information on Tesla P40 FP16 performance, but according to a comment on some forum it does have 2:1 FP16 ratio. The journey was marked by Llama. I currently have a P100 that I'm working on learning how to apply FP training on, and want the P40 two complement it (pun intended, I'm a nerd) Let me know if P40 is from Pascal series, but still, for some dumb reason, doesn't have the FP16 performance of other Pascal-series cards. Mi25 is only $100 but you will have to deal with ROCM and the cards being pretty much as out of support as the P40 or worse. Skip to content. All depends on what you want to do. Benchmark videocards performance analysis: PassMark - G3D Mark, PassMark - G2D Mark, Geekbench - Nvidia Tesla P100, Nvidia T4, Tesla K14 they all come with 12-16GB. Since a new system isn't in the cards for a bit, I'm contemplating a 24GB Tesla P40 card as a temporary solution. I’m just gathering information so I The Tesla P40 was an enthusiast-class professional graphics card by NVIDIA, launched on September 13th, 2016. This card can be found on ebay for less than $250. Hi there im thinking of buying a Tesla p40 gpu for my homelab. P100 has good FP16, but only 16gb of Vram (but it's HBM2). No video output and should be easy to pass-through. On my cable, the end with the four yellow wires above and four black wires Get the Reddit app Scan this QR code to download the app now. So I created this. Best. Sort by: Best. I'm Skip to main content. r/Oobabooga A chip A close button. a_beautiful_rhind • That's a bit wrong. Anyone here have any Anyone here have any Skip to main content So I bought a Tesla P40, for about 200$ (Brand new, good little AI Inference Card). I don't have any 70b downloaded, I went straight for Goliath120b with 2 cards. For what it's worth, if you are looking at llama2 70b, you should be looking also at Mixtral-8x7b I'm building an inexpensive starter computer to start learning ML and came across cheap Tesla M40\P40 24Gb RAM graphics cards. I'm considering Quadro P6000 and Tesla P40 to use for machine learning. 45 / hour worth of compute. Reply reply Top 1% Rank by size . So recently I’ve been seeing nvidia Tesla p40s floating around on eBay for pretty good prices. I've found some ways around it technically, but the 70b model at max context is where things got a bit slower. Reply reply Pathos14489 • I run 8x7B and 34B on my P40 via llama. Only GGUF provides the most performance on Pascal cards in my experience. K80 (Kepler, 2014) and M40 (Maxwell, 2015) are far slower while P100 is a bit better for training but still more expensive and only has 16GB and Volta-Class V100 I've an old Thinkstation D30, and while it officially supports the Tesla K20/K40, I'm worried the p40 might cause issues (Above 4G can be set, but Resize Bar missing, though there seem to be firmware hacks and I found claims of other Mainboards without the setting working anyway. This covers the majority of models P40 doesnt do FP16. Reply The Tesla line of cards should definitely get a significant performance boost out of fp16. Please use our Discord server instead of supporting a company that View community ranking In the Top 5% of largest communities on Reddit. Please use our Discord server instead of supporting a company that acts against its users and unpaid moderators. If all you want to do is run 13B models without going crazy on context a 3060 will be better supported, if you want to run larger models that need twice the VRAM and you don't mind it being obsolete in a year or two the P40 can be interesting. Log In / Sign Up; While these models are massive, 65B parameters in some cases, quantization converts the parameters (the connections between neurons) from FP16/32 to 8/4-bit integers. Is THAT what the "-12" part in the name stands for: that its currently available VRAM in the current setup (being shared between 2 users) is 12GB? Share Add a Comment. Expand user menu Open settings menu. I noticed this metric is missing from your table I have seen several posts here on r/LocalLLaMA about finding ways to make P40 GPUs work, but is often involves tinkering a bit with the settings because you have to disable some newer features that make things work better but which I saw there was some interest in multiple GPU configurations, so I thought I’d share my experience and answer any questions I can. r/LocalLLaMA A chip A close button. ) // even so i would recommend modded 2080's or normal used 3090 for some 500-700 usd, they are many times faster (like 50-100x in some cases) for lesser amount of power. 367 TFLOPS regarding the RTX 3090, i am talking about 2 cards, and USD :-) i would love to use 2 x RTX3090 since the 4080 has less memory and can afford only one for this year, i haven't given up of finding a pair of 3090 at a fair price including cooling (since i will need to water cool whatever i I bought 4 p40's to try and build a (cheap) llm inference rig but the hardware i had isn't going to work out so I'm looking to buy a new server. The compute jumps around. I know it's the same "generation" as my 1060, but it has four times the memory and more power in general. I personally run voice recognition and voice generation on P40. It is still pretty fast, no further precision loss from the previous 12 GB version. Unfortunately, I did not do tests on Tesla P40. When I first tried my P40 I still had an install of Ooga with a newer bitsandbyes. cpp, works great. Reply reply Top 1% Rank by size Tested on Tesla T4 GPU on google colab. THough the The RTX 2080 Ti is ~45% faster than the Tesla P100 for FP32 calculations, which is what most people use in training. If you dig into the P40 a little more, you'll see its in a pretty different class than anything in the 20- or 30- series. Motherboard: Asus Prime x570 Pro Processor: Ryzen 3900x System: Proxmox Virtual Environment Virtual Machine: Running LLMs Server: Ubuntu Software: Oobabooga's text-generation-webui 📊 Performance Metrics by Model Size: 13B GGUF Model: Tokens per Second: Around 20 The obvious budget pick is the Nvidia Tesla P40, which has 24gb of vram (but around a third of the CUDA cores of a 3090). BTW, P40 take standard "CPU" style power cables, not regular PCIE ones. I’m leaning on towards P100s I picked up the P40 instead because of the split GPU design. Be the Can I run the Tesla P40 off Skip to main content. I bought a power cable specific for these NVIDIA cards (K80, M40, M60, P40, P100). Built on the 16 nm process, and based on the GP102 graphics processor, the card supports DirectX 12. It is designed for single precision GPU compute tasks as well as to accelerate graphics in virtual remote workstation environments. I'd Skip to main content. The GP102 graphics processor is a large chip with a die area of 471 mm² and 11,800 million transistors. Log In / Sign Up; Advertise on Comparative analysis of NVIDIA Tesla V100 PCIe and NVIDIA Tesla P40 videocards for all known characteristics in the following categories: Essentials, Technical info, Video outputs and ports, Compatibility, dimensions and requirements, Memory, Technologies, API support. I have run fp16 models on my (even older) K80 so it probably "works" as the driver is likely just casting at runtime, but be warned you may run into hard barriers. Also P40 has shit FP16 performance simply because it is lacking the amount of FP16 cores that the P100 have for example. The P40 also has basically no half precision / FP16 support, which negates most benefits of having 24GB VRAM. I would probably split it Skip to main content. They did this weird thing with Pascal where the GP100 (P100) and the GP10B (Pascal Tegra SOC) both support both FP16 and FP32 in a way that has FP16 (what they call Half Precision, or HP) run at double the speed. 05 TFLOPS FP32: 9. r/PcBuild A chip A close button. This is because Pascal cards have dog crap FP16 performance as we all know. When idle and no model loaded it uses ~9w Reply reply ChryGigio • Oh wow I did not expect such a difference, sure those are older cards but 5x reduction just by clearing the VRAM is big, now I understand why managing states is needed, thanks. What you can do is split the model into two parts. . My budget for now is around $200, and it seems like I can get 1x P40 with 24GB of VRAM for around $200 on ebay/from china. Hope to see how it performs with 3 cards tomorrow. Strange some times works faster depending of the model. I've seen several github issues where they don't work until until specific code is added to give support for older architecture cards, not something that View community ranking In the Top 1% of largest communities on Reddit [P] openai-gemm: fp16 speedups over cublas. very detailed pros and cons, but I would like to ask, anyone try to mix up one Using a Tesla P40 I noticed that when using llama. cpp, you can run the 13B parameter model on as little as ~8gigs of Tesla p40 24GB i use Automatic1111 and ComfyUI and i'm not sure if my performance is the best or something is missing, so here is my results on AUtomatic1111 with these Commanline: -opt-sdp-attention --upcast-sampling --api Prompt. My current setup in the Tower 3620 includes an NVIDIA RTX 2060 Super, and I'm exploring the feasibility of upgrading to a Tesla P40 for more intensive AI and deep learning tasks. hello, i have a Tesla P40 Nvidia with 24Gb with Pascal instruction. They are some odd duck cards, 4096 bit wide memory bus and the only Pascal without INT8 and FP16 instead. Edit: Tesla M40*** not a P40, my bad. New Note: Reddit is dying due to terrible leadership from CEO /u/spez. What I haven't been able to determine if if one can do 4bit or 8bit inference on a P40 This means you cannot use GPTQ on P40. cpp still has a CPU backend, so you need at least a decent CPU or it'll bottleneck. But inference, a 20/30/40 series card will always crush it, possibly to the I am building a budget server to run AI and I have no experience running AI software. I ended up going with the P100 which is rated to run at The P40 was designed by Nvidia for data centers to provide inference, and is a different beast than the P100. Around $180 on ebay. Anyone have Tesla P40 has really bad FP16 performance compared to more modern GPU's: FP16 (half) =183. As a result, inferencing is slow. So I just bought a used server for fitting GPUs and I'd like to put some Teslas in - how does the P40 compare to the P100? I know the P100 has a lot higher bandwidth than the P40, and the performance seems to be better (factor 100) at fp16 but worse at fp32 for some reason. Tesla P40 C. The 3090 can't access the memory on the P40, and just using the P40 as swap space would be even less efficient than using system memory. Since the Razer Core does not have any mini-fan 2. int8 (8bit) should be a lot faster. cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. New. TimyIsCool opened this issue Jun 19, 2023 · 15 comments Comments. One card isn't getting you more than a LoRA in terms of LLMs. I have also added a table to choose the best flags according to the memory and speed requirements. FYI it's also possible to unblock the full 8GB on the P4 I would not expect this to hold, however, for the P40 vs P100 duel, I believe that the P100 will be faster overall for training than the P40, even though the P40 can have more stuff in vram at any one time. On the previous Maxwell cards any FP16 Note: Some models are configured to use fp16 by default, you would need to check if you can force int8 on them - if not just use fp32 (anything is faster than fp16 pipe on p40. So my thought is perhaps I can salvage the HSF from one of those models and replace the passive heatsink. Exllamav2 runs well. I've seen maybe one or two videos talking about it and using it. I would like to upgrade it with a GPU to run LLMs locally. I’ve decided to try a 4 GPU capable rig. RTX 3090 TI + RTX 3060 D. Win 11pro. ASUS ESC4000 G3. 00 / hour on GCP, it follows that an RTX 2080 Ti provides $1. Tomorrow I'll receive the liquid cooling kit and I sould get constant results. 20ghz 512GB DDR4 ECC Telsa P40 - 24gb Vram, but older and crappy FP16. Exllama 1 and 2 as far as I've seen don't have anything like that because they are much more heavily optimized for new hardware so you'll have to avoid using them for loading models. When I get time to play around with it, I'll update as I experiment with the higher context results if they're interesting. A full order of magnitude slower! I'd read that older Tesla GPUs are some of the top value picks when it comes to ML applications, but obviously with this level of performance that isn't the case at all. I'm running CodeLlama 13b instruction model in kobold simultaneously with Stable Diffusion 1. Looks like the P40 is basically the same as the Pascal Titan X; both are based on the GP102 GPU, so it won't have the double-speed FP16 like the P100 but it does have the fast INT8 like the Pascal Titan X. I got the custom cables from Nvidia to power the Tesla P 40, I’ve put it in the primary video card slot in the machine as so it You can build a box with a mixture of Pascal cards, 2 x Tesla P40's and a Quadro P4000 fits in a 1x 2x 2x slot configuration and plays nice together for 56Gb VRAM. If your application supports spreading load over multiple cards, then running a few 100’s in parallel could be an option (at least, So, it's still a great evaluation speed when we're talking about $175 tesla p40's, but do be mindful that this is a thing. I guess if you want to train it might be a thing, but you could just train in int4 or on runpod. Modern cards remove FP16 cores entirely and either upgrade the FP32 cores to allow them to run in 2xFP16 mode or The Tesla P40 and other Pascal cards (except the P100) are a unique case since they support FP16 but have abysmal performance when used. In comparison, for this price I can get an RTX 3060 Becuase exl2 want fp16, but tesla p40 for example don't have it. cpp made a fix that works around the fp16 limitation. I usually stick to FP32 so that I can switch it to BFLOAT16 down the line without loss. The easiest way I've found to get good performance is to use llama. Generation reddit. I have two P100. Closed TimyIsCool opened this issue Jun 19, 2023 · 15 comments Closed Tesla P40 only using 70W underload #75. Hey, Tesla P100 and M40 owner here. If you use P40, you can have a try with FP16. 8 cards are going to use a lot of electricity and make a lot of noise. If anybody has something better on P40, please share. On Pascal cards like the Tesla P40 you need to force CUBLAS to use the older MMQ kernel instead of using the tensor kernels. Then each card will be responsible for Original Post on github (for Tesla P40): JingShing/How-to-use-tesla-p40: A manual for helping using tesla p40 gpu (github. With that turned off, performance is back. a_beautiful_rhind • Interesting because compiling with F16 does nothing on my 3090. Note - Prices are localized for my area in Europe. Worse that can happen is that I need to buy a cheap Mainboard or used PC just for the Tesla cards. *(not to mention The FP16 thing doesn't really matter. I’m thinking starting with Llama LLM, but would like to get into making AI pictures and videos as well plus who knows what else once I learn more about this. cpp Reply reply More replies More replies. The enclosure comes with 2x 8 GPU power connectors and the P40 only uses one. But . 183 TFLOPS FP32: 11. It's the best of the affordable; terribly slow compared to today's RTX3xxx / 4xxx but big. More compute while prompt processing less while generating. Also the MUCH slower ram of the p40 compared to a p100 means that time blows out further. Therefore, you need to modify the registry. I am just getting into this and have not received the hardware yet but it is ordered. fp16 performance is very important, and the p40 is crippled compared to the p100. - How i can do this ? p40 vs p100 fp16 gguf . And keep in mind that the P40 needs a 3D printed cooler to function in a consumer PC. Transformer recognize all GPUs. My use case is Skip to main content. The P100 a bit slower around 18tflops. Share Add a Comment. I researched this a lot; you will not get good FP16 (normal) performance from a P40. I think that's what people build their big models around, so that ends up being kinda what you need to run most high-end stuff. NVIDIA Tesla M40 24gb vram(2nd hand, fleebay) PSU: EVGA 750eq gold (left over from mining days) memory: 64gb Patriot Viper ddr4 HDDs: WD black NVMe ssd 250gb Seagate IronWolf NAS 4tb Cooler: Gammaxx 400 V2 (super shitty) Why I chose these parts: Mostly based on cost. My understanding is that the main quirk of inferencing on P40's is you need to avoid FP16, as it will result in slow-as-crap computations. Built a rig with the intent of using it for local AI stuff, and I got a Nvidia Tesla P40, 3D printed a fan rig on it, but whenever I run SD, it is Skip to main content. Sports. It was a "GRID" product meant to use their virtualization stuff, and yes, indeed, they keep the drivers and support for that a bit locked up, but I don't think anything keeps you from simply loading up the Nvidia docker I am think about picking up 3 or 4 Nvidia Tesla P40 GPUs for use in a dual-CPU Dell PowerEdge R520 server for AI and machine learning projects. Or check it out in the app stores   Tesla P4 vs P40 in AI (found this Paper from Dell, thought it'd help) Resources Writing this because although I'm running 3x Tesla P40, it takes the space of 4 PCIe slots on an older server, plus it uses 1/3 of the power. RTX 3090 TI + Tesla P40 Note: One important piece of information. I'm We compared two Professional market GPUs: 24GB VRAM Tesla P40 and 16GB VRAM Tesla P100 DGXS to see which GPU has better performance in key specifications, benchmark tests, power consumption, etc. I have no experience with the P100, but I read the Cuda compute version on the P40 is a bit newer and it supports a couple of data types that the P100 doesn't, making it a slightly better card at inference. You can fix this by FP16 will be utter trash, you can see on the NVidia website that the P40 has 1 FP16 core for every 64 FP32 cores. In the case of the P40 specifically, my understanding is that only llama. Having a very hard time finding benchmarks though. P6000 has higher memory bandwidth and active cooling (P40 has passive cooling). While it is technically capable, it runs fp16 at 1/64th speed compared to fp32. Open menu Open navigation Go to Reddit Home. Tesla P100 - Front. Reply reply Hot-Problem2436 • Looks like I may need to pick one up. Now I’m debating yanking out four P40 from the Dells or four P100s. AMD cpus are cheaper than intel for thread count, something I've found important in my ML Hi reader, I have been learning how to run a LLM(Mistral 7B) with small GPU but unfortunately failing to run one! i have tesla P-40 with me connected Skip to main content Open menu Open navigation Go to Reddit Home Note: Reddit is dying due to terrible leadership from CEO /u/spez. 45 = 826 hours. Top. P100 claims to have better FP16 but it's a 16g card so you need more of them and at $200 doesn't seem competitive. You can look up all these cards on techpowerup and see theoretical speeds. a girl standing on a mountain For AutoGPTQ it has an option named no_use_cuda_fp16 to disable using 16bit floating point kernels, and instead runs ones that use 32bit only. r/StableDiffusion A chip A close button. P40 Cons: Apparently due to FP16 weirdness it doesn't perform as well as you'd expect for the applications I'm interested in. cpp that improved performance. Might have just been the colab I used not having Xformers. It only came up when GPTQ forced it in the computations. cpp is very capable but there are benefits to the Exllama / EXL2 combination. This is a misconception. i since then dug dipper and found that P40 (3840 CUDA cores) is good for SP inference and less for HP training and practically none for DP or INT4, the P100 (3584 CUDA cores) on the other hand has less memory but wonderful performance at the same price per card: Tesla P100 PCIe 16G ===== FP16: 19. A new feature of the Tesla P40 GPU Accelerator is the support of the “INT8” Running on the Tesla M40, I get about 0. Hi all, I made the mistake us jumping the gun on a Tesla P40 and not really doing the research in terms of drivers prior to buying it. In terms of FP32, P40 indeed is a little bit worse than the newer GPU like 2080Ti, but it has great FP16 performance, much better than many geforce cards like 2080Ti and 3090. You can just open the shroud and slap a 60mm fan on top or use one of the many 3D printed shroud designs already available, but all the other 3D printed shrouds kinda sucks and looks janky with 40mm server fans adapted to blow air to a Tesla p40 24GB i use Automatic1111 and ComfyUI and i'm not sure if my performance is the best or something is missing, so here is my results on AUtomatic1111 with these Commanline: -opt-sdp-attention --upcast-sampling --api Prompt. Curious to see how these old GPUs are fairing in today's world. More posts you may like r/LocalLLaMA. Get app Get the Reddit app Log In Log in to Reddit. I would not have them if we didn't have llama. 5 mm 2 wire I got a Razer Core X eGPU and decided to install in a Nvidia Tesla P40 24 GPU and see if it works for SD AI calculations. P100s are decent for FP16 ops but you will need twice as many. p100 are not slower either. Log In / Sign Up; Advertise P40 is a better choice, but it depends on the size of the model you wish to run. However the ability to run larger models and the recent developments to GGUF make it worth it IMO. Combining this with llama. I'm currently working and Does anyone have experience with running StableDiffusion and older NVIDIA Tesla GPUs, such as the K-series or M-series? Most of these accelerators have around 3000-5000 CUDA cores and 12-24 GB of VRAM. Payback period is $1199 / $1. 44 desktop installer, which We compared two Professional market GPUs: 24GB VRAM Tesla P40 and 8GB VRAM Tesla M10 to see which GPU has better performance in key specifications, benchmark tests, power consumption, etc. For the 120b, I still have a lot going to CPU. ) I don't see why they wouldn't since people would just buy the cheaper gaming card over the 1000 dollar workstation card if they have identical FP16 rates. If you want WDDM support for DC GPUs like Tesla P40 you need a driver that supports it and this is only the vGPU driver. no fan sticking out back/side) cooling solution for my "new" Tesla P40, which only comes with a passive heatsink. On the previous Maxwell cards any FP16 code would just get executed in the FP32 cores. For example, in text generation web ui, you simply select the "don't use fp16" option, and you're fine. The spec list for the Tesla P100 states 56 SMs, 3584 cuda cores and 224 TUs however the block diagram shows that the full size GP100 GPU would be 60SMs, 3840 CUDA cores and 240 TUs. Premium Powerups Explore Gaming. When a model is loaded it uses ~50w of power. auto_gptq and gptq_for_llama can be specified to use fp32 vs fp16 calculations, but this also means you'll be hurting performance drastically on the 3090 cards (given there's no way to indicate using one or the other by individual card within existing Yes, it is faster than using RAM or disk cache. But what does the "-12" part mean? My current VM has a 12 GB VRAM so it looks like this vGPU is being shared between me and another user. 32 GB ram, 1300 W power supply. Reply reply Pathos14489 • If you do, there's some specific optimizations that help out for P40s that you can do with llama. I want to force model with FP32 in order to use maximum memory and fp32 is faster than FP16 on this card. Valheim Genshin Impact Minecraft Pokimane Halo Infinite Call of Duty: Warzone Path of Exile Hollow Knight: Silksong Escape from Tarkov Watch Dogs: Legion. Navigation Menu Toggle navigation. So Exllama performance is terrible. Tesla P4 Temp Limit for Harvesting? comments Additional comment actions. The data comes from here B. 76 TFLOPS FP64: 0. The vast majority of the time this changes nothing, especially with controlnet models, but sometimes you can see a tiny difference in quality From a practical perspective, this means you won't realistically be able to use exllama if you're trying to split across to a P40 card. Unfortunately you are wrong. 5 in an AUTOMATIC1111 So I'm trying to see if I can find a more elegant (i. Controversial. Reply More posts you may like. Eratta: "vladmandic", my bad for not reading. But a good alternative can be the i3 Window Manager, here only ~300MB VRAM is needed, so basically Nvidia Announces 75W Tesla T4 for inferencing based on the Turing Architecture 64 Tera-Flops FP16, 130 TOPs INT 8, 260 TOPs INT 4 at GTC Japan 2018 I have a very specific question, but maybe you have an answer. on model "TheBloke/Llama-2-13B-chat-GGUF**" "llama-2-13b-chat. I think your confused with the k80. Log In / Sign Up; Advertise on Exllama loaders do not work due to dependency on FP16 instructions. maybe tesla P40 does not support FP16? thks. I figure I must be going 25 votes, 29 comments. There might be something like that you can do for I got a Nvidia tesla P40 and want to plug it in my Razer Core X eGPU enclosure for AI . com Open. Tesla P100 - Back. This lets you run the models on much smaller harder than you’d have to use for the unquantized models. The p100 is the all round Note: Some models are configured to use fp16 by default, you would need to check if you can force int8 on them - if not just use fp32 (anything is faster than fp16 pipe on p40. So my P40 is only using about 70W while generating responses, its not limited in With Tesla P40 24GB, I've got 22 tokens/sec. Or beacuse gguf allows offload big model on 12/16 gb cards but exl2 doesn't. ExLlamaV2 is kinda the hot thing for local LLMs and the P40 lacks support here. r/CUDA A chip A close button. On a 103b it will generate at various percentages on all GPUs. Get the Reddit app Scan this QR code to download the app now. A few details about the P40: you'll have to figure out cooling. cpp. At least 2 cards are needed for a 70b. My hardware specs: Dell R930 (D8KQRD2) 4x Xeon 8890v4 24-core at 2. Tesla P40 for SD? Discussion I've been looking at older tesla GPUs for ai image generation for a bit now, and I've haven't found as much information as I thought there'd be. The P40 does not have fan it is a server passive flow 24gb card and needs additional air flow to keep it cool for AI. 526 TFLOPS A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. The new NVIDIA Tesla P100, powered by the GP100 GPU, can perform FP16 arithmetic at twice the throughput of FP32. You can get these on Taobao for around $350 (plus shipping) A RTX 3090 is around $700 on the local secondhand markets for reference. 0 coins. It sux, cause the P40's 24GB VRAM and price make it look so delicious. Log In / Sign Up; Advertise on I updated to the latest commit because ooba said it uses the latest llama. Since a P100 is $1. My P40 is about 1/4 the speed of my 3090 at fine tuning. 6% more advanced lithography process. 76 TFLOPS. **So What is SillyTavern?** Tavern is a user interface you can install on your computer (and Android phones) that allows you to interact text generation AIs and chat/roleplay with characters you or the community create. I'm considering installing an NVIDIA Tesla P40 GPU in a Dell Precision Tower 3620 workstation. Just to add, the P100 has good FP16 performance but in my testing P40 on GGUF is still faster. I chose Q_4_K_M because I'm hoping to try some higher context and wanted to save some space for it. So for instance it will be 38% plus 38% and 28% usage all going at once in Figured I might ask the pros. I've not seen anyone run P40s on another setup. ) I'm seeing 20+ tok/s on a 13B model with gptq-for-llama/autogptq and 3-4 toks/s with exllama on my P40. 114 out of 138 layers to GPU, 67gb CPU buffer size. What I suspect happened is it uses more FP16 now because the tokens/s on my Tesla P40 got halved along with the power consumption and memory controller load. Write A place to discuss the SillyTavern fork of TavernAI. So I think P6000 will be a right choice. 8tflops for the P40, 26. a girl standing on a mountain Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 2573751789, Size: 512x512, Model hash: Tesla P40 has 4% lower power consumption. PCI-e x16 or x8 for the p40? I have the same problem with p40. Q&A. It's just one chip. Log In / Sign Up; Advertise Note: Reddit is dying due to terrible leadership from CEO /u/spez. I too was looking at the P40 to replace my old M40, until I looked at the fp16 speeds on the P40. This basically means that the model is smaller and (generally) faster, but it also means that it has slightly less room to train on. It is not at all rosy in server land. Alltogether, you can build a machine that will run a lot of the recent models up to 30B parameter size for under $800 USD, and it will run the smaller ones relativily easily. Members Online. So you will need to buy P40 has more Vram, but sucks at FP16 operations. Question: is it worth taking them now or to take something from this to begin with: 2060 12Gb, 2080 8Gb or 40608Gb? If we compare the speed on the chart, they are 40% - 84% faster than the M40, but I suspect that everything will be different for ML. Note: Loaded, not running inference. com) Seems you need to make some registry setting changes: After installing the driver, you may notice that the Tesla P4 graphics card is not detected in the Task Manager. Int8 is half speed but it works. I wouldn't call the P40 nerfed but just different. Everything else is on 4090 under Exllama. Since Cinnamon already occupies 1 GB VRAM or more in my case. Very briefly, this means that you can possibly get some speed increases and fit much larger context sizes into VRAM. Or check it out in the app stores   P100s have HBM and decent FP16. My Tesla P4 with decent utilization (700TB C8) sticks to 80C, in an enterprise Hello, I have 2 GPU in my workstation 0: Tesla p40 24GB 1: Quadro k4200 4GB My main GPU is Tesla, every time i run comfyui, it insists to run using Premium Explore Gaming. Open comment sort options. Adding to that, it seems the P40 cards have poor FP16 performance and there's also the fact they're "hanging on the edge" when it comes to support since many of the major projects seem to be developed mainly on 30XX cards up. If someone someday fork exl2 with upcast in fp32 (not for memory saving reason, but for speed reason) - it will be amazing. I also Purchased a RAIJINTEK Morpheus II Core Black Heatpipe VGA Skip to main content. Although I've never seen anyone explain how to get it up and running. cpp with all the layers offloaded to the P40, which does all of its calculations in FP32. 5Gbps, 1808MHz, 694GB/s • Takt Ba HPC-Prozessoren Testberichte Günstig kaufen I'm seeking some expert advice on hardware compatibility. Reply reply a_beautiful_rhind • I think DDA / GPU Passthrough flaky for Tesla P40, but works perfectly for consumer 3060 I've been attempting to create a Windows 11 VM for testing AI tools. Most solutions will either be noisy or Tesla P40 only using 70W underload #75. Or check it out in the app I'm looking for a Nvidia Tesla P40 GPGPU so I can learn how to apply INT inference. The GP102 (Tesla P40 and NVIDIA Titan X), GP104 (Tesla P4), and GP106 GPUs all support instructions that can perform integer dot products on 2- and4-element 8-bit vectors, with accumulation into a 32-bit integer. e. Once I do buy a new system or even a current-ish generation video card I would move the P40 over to a home server so my kids could mess with Stable Autodevices at lower bit depths (Tesla P40 vs 30-series, FP16, int8, and int4) Hola - I have a few questions about older Nvidia Tesla cards. NFL NBA Megan Anderson I think the tesla P100 is the better option than the P40, it should be alot faster on par with a 2080 super in FP16. You'll have to do your own cooling, the P40 is designed to be passively cooled (it has no fans For the vast majority of people, the P40 makes no sense. It's got a heck of a lot of VRAM for the price point. On the other hand, 2x P40 can load a 70B q4 model with borderline bearable speed, while a 4060Ti + partial offload would be very slow. r/homelab A chip A close button. I would get garbage output as a The p40/p100s are poor because they have poor fp32 and fp16 performance compared to any of the newer cards. completely without x-server/xorg. These questions have come up on Reddit and elsewhere, but there are a couple of details that I can't seem to get a firm answer to. 7% higher maximum VRAM amount, and a 128. Copy link TimyIsCool commented Jun 19, 2023. Note that llama. It has FP16 support, but only in like 1 out of every 64 cores. cpp because of fp16 computations, whereas the 3060 isn't. We couldn't decide between Tesla P40 and Tesla A100. Tesla P40 24G ===== FP16: 0. I graduated from dual M40 to mostly Dual P100 or P40. Since command-r is a particular context hog, taking up to 20GB of VRAM for a modest amount of context, I thought it was a good candidate for testing. The main thing to know about the P40 is that its FP16 performance suuuucks, even compared to similar boards like the P100. For all models that are larger then the RAM do not work even cud fit in VRAMs + RAM The P40 driver is paid for and is likely to be very costly. P40 Pros: 24GB VRAM is more future-proof and there's a chance I'll be able to run language models. cpp the video card is only half loaded (judging by power consumption), but the speed of the 13B Q8 models is quite acceptable. NFL View community ranking In the Top 10% of largest communities on Reddit. And P40 has no merit, comparing with P6000. I like the P40, it wasn't a huge dent in my wallet and it's a newer architecture than the M40. At the end of the day I feel the a4000 is about the best mix of speed, vram, and power consumption (only 140W) for the I saw a couple deals on used Nvidia P40's 24gb and was thinking about grabbing one to install in my R730 running proxmox. From the look of it, P40's PCB board layout looks exactly like 1070/1080/Titan X and Titan Xp. gguf"** The performance degrade as soon as the GPU overheat up to 6 tokens/sec, and temperature increase up to 95C. My use case is not gaming, or mining, but rather finetuning and playing around with local LLM models, these typically require lots of vram and cuda cores. Just do a search on eBay for "R730 M40" to see the cable. The 3060 12GB costs about the same but provides much better speed. llama. It seems to have gotten easier to manage larger models through Ollama, FastChat, ExUI, EricLLm, exllamav2 supported projects. This means only very small models can be run on P40. Sign in Product GitHub Copilot. Great advice. That's 34 days at 24/7 utilization, 103 days at 8 hours per day utilization, 206 days at 4 hours per day So I suppose the P40 stands for the "Tesla P40", OK. The Tesla P40 GPU Accelerator is offered as a 250 W passively cooled board that requires system air flow to properly operate the card within its thermal limits. Reset laptop BIOS - ASUS G512L(V) upvote r/macbookrepair. So Tesla P40 cards work out of the box with ooga, but they have to use an older bitsandbyes to maintain compatibility. 7 GFLOPS , FP32 (float) = 11. There is a flag for gptq/torch called use_cuda_fp16 = False that gives a massive speed boost -- is it possible to do These questions have come up on Reddit and elsewhere, but there are a couple of details that I can't seem to get a firm answer to. Log In / Sign Up; Advertise on Reddit; Shop . 58 TFLOPS, FP32 (float) I was looking at card specs earlier and realized something interesting: P100s, despite being slightly older and from the same generation as P40s, actually have very good Just wanted to share that I've finally gotten reliable, repeatable "higher context" conversations to work with the P40. the setup is simple and only modified the eGPU fan to ventilate frontally the passive P40 card, despite this the only conflicts I encounter are related to the P40 nvidia drivers that are funneled by nvidia to use the datacenter 474. Subreddit to discuss about Llama, the large The P40 and K40 have shitty FP16 support, they generally run at 1/64th speed for FP16. RTX 3090: FP16 (half) = 35. After 30b models. I have a Dell PowerEdge T630, the tower version of that server line, and I can confirm it has the capability to run four P40 GPUs. In the past I've been using GPTQ (Exllama) on my main system with the The Tesla P40 and other Pascal cards (except the P100) are a unique case since they support FP16 but have abysmal performance when used. P40s can't use these. Still, the only better used option than P40 is the 3090 and it's quite a step up in price. Tesla A100, on the other hand, has an age advantage of 3 years, a 66. Log In / Sign Up; A supplementary P40 card to your 16gb card will be nice. :-/, feels like one is going to need 5-6 If you've got the budget, RTX 3090 without hesitation, the P40 can't display, it can only be used as a computational card (there's a trick to try it out for gaming, but Windows becomes unstable and it gives me a bsod, I don't recommend it, it ruined my PC), RTX 3090 in prompt processing, is 2 times faster and 3 times faster in token generation (347GB/S vs 900GB/S for rtx 3090). I got a Tesla P4 for cheap like many others, and am not insane enough to run a loud rackmount case with proper airflow. BUT, I haven't personally tested this, so I can't say for sure. I was also planning to use ESXi to pass through P40. It is crucial you plug the correct end to the card. But a strange thing is that P6000 is cheaper when I buy them from reseller. Or check it out in the app stores   2x Used Tesla P40 GPUs 3&4: 2x Used Tesla P100 Motherboard: Used Gigabyte C246M-WU4 CPU: Used Intel Xeon E-2286G 6-core (a real one, not ES/QS/etc) RAM: New 64GB DDR4 2666 Corsair Vengeance PSU: New Corsair RM1000x New SSD, mid tower, cooling, Tesla P40 (Size reference) Tesla P40 (Original) In my quest to optimize the performance of my Tesla P40 GPU, I ventured into the realm of cooling solutions, transitioning from passive to active cooling. What I suspect happened is it uses more FP16 now because the tokens/s on my Tesla P40 got halved along with the power consumption and memory controller load. My question is how much would it cost in order to have it working with esxi and Advertisement Coins. github True FP16 performance on Titan XP (also Tesla P40 BTW) is a tragedy that is about to get kicked in the family jewels by AMD's Vega GPUs so I expect Titan X Volta to address this because NVIDIA isn't dumb. As for the fp16 part, this means that the model was trained in floating point 16 as opposed to floating point 32. VLLM requires hacking setup. But 24gb of Vram is cool. P40s are mostly stuck with GGUF. But that guide assumes you have a GPU newer than Pascal or running on CPU. Just loaded. I am thinking of buying Tesla P40 since it's cheapest 24gb vram solution with more or less modern chip for mixtral-8x7b, what speed will I get and Skip to main content. Everyone, i saw a lot of comparisons and discussions on P40 and P100. bgnar dmcr rkp ibyd xecnpgb brfb rpdtpdm rdnrej avdcm pbwxyg