Tesla p40 fp16 reddit. Having a very hard time finding benchmarks though.

Tesla p40 fp16 reddit ExLlamaV2 is kinda the hot thing for local LLMs and the P40 lacks support here. And P40 has no merit, comparing with P6000. No video output and should be easy to pass-through. cpp is very capable but there are benefits to the Exllama / EXL2 combination. 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. They are some odd duck cards, 4096 bit wide memory bus and the only Pascal without INT8 and FP16 instead. The 3060 12GB costs about the same but provides much better speed. On the previous Maxwell cards any FP16 I'm curious about how well the P40 handles fp16 math. 4 and the minimum version of CUDA for Torch 2. 58 TFLOPS FP32: 12. The main thing to know about the P40 is that its FP16 performance suuuucks, even compared to similar boards like the P100. V interesting post! Have R720+1xP40 currently, but parts for an identical config to yours are in the mail; should end up like this: R720 (2xE-2670,192gb ram) 2x P40 2x P4 1100w psu Therefore I have been looking at hardware upgrades and opinions on reddit. Cuda drivers, conda env etc. I noticed this metric is missing from your table Get the Reddit app Scan this QR code to download the app now. For what it's worth, if you are looking at llama2 70b, you should be looking also at Mixtral-8x7b. Or check it out in the app stores     TOPICS Tesla; Crypto. The P40 for instance, benches just slightly worse than a 2080 TI in fp16 -- 22. I get between 2-6 t/s depending on the model. P40s can't use these. Training and fine-tuning tasks would be a different story, P40 is too old for some of the fancy features, some toolkits and frameworks don't support it at all, and those that might run on it, will likely run significantly slower on P40 with only f32 math, than on other cards with good f16 performance or lots of tensor cores. So you Original Post on github (for Tesla P40): JingShing/How-to-use-tesla-p40: A manual for helping using tesla p40 gpu (github. The P100 a bit slower around 18tflops. Or beacuse gguf allows offload big model on 12/16 gb cards but exl2 doesn't. 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. Exllamav2 runs well. The P40 is sluggish with Hires-Fix and Upscaling but it does The P40 was designed by Nvidia for data centers to provide inference, and is a different beast than the P100. P100 has good FP16, but only 16gb of Vram (but it's HBM2). 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. Recently I felt an urge for a GPU that allows training of modestly sized and inference of pretty big models while still staying on a reasonable budget. P40 Cons: Apparently due to FP16 weirdness it doesn't perform as well as you'd expect for the applications I'm interested in. So I think P6000 will be a right choice. Same idea as as [r/SuddenlyGay P40 has more Vram, but sucks at FP16 operations. cpp because of fp16 computations, whereas the 3060 isn't. Llamacpp runs rather poorly vs P40, no INT8 cores hurts it. Got myself an old Tesla P40 Datacenter-GPU (GP102 like GTX1080-silicon but with 24GB ECC vram, 2016) for 200€ from ebay. What you can do is split the model into two parts. 8tflops for the P40, 26. 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. 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. You can fix this by The Tesla P40 and other Pascal cards (except the P100) are a unique case since they support FP16 but have abysmal performance when used. 183 TFLOPS FP32: 11. co The P40 is restricted to llama. It can run Stable Diffusion with reasonable speed, and decently sized LLMs at 10+ tokens per second. When I first tried my P40 I still had an install of Ooga with a newer bitsandbyes. This is a misconception. Hey, Tesla P100 and M40 owner here. cpp the video card is only half loaded (judging by power consumption), but the speed of the 13B Q8 models is quite acceptable. 8tflops for the 2080. Log In / Sign Up; 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. On Pascal cards like the Tesla P40 you need to force CUBLAS to use the older MMQ kernel instead of using the tensor kernels. Or check it out in the app stores   Nvidia Tesla P40 performs amazingly well for llama. xx. Expand user menu Open settings menu. Get app Get the Reddit app Log In Log in to Reddit. With the update of the Automatic WebUi to Torch 2. But 24gb of Vram is cool. Usually on the lower side. So in practice it's more like having 12GB if you are locked in at FP16. 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. I'm building an inexpensive starter computer to start learning ML and came across cheap Tesla M40\P40 24Gb RAM graphics cards. I want to point out most models today train on fp16/bf16. 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. I am looking at upgrading to either the Tesla P40 or the Tesla P100. cpp still has a CPU backend, so you need at least a decent CPU or it'll bottleneck. It has FP16 I updated to the latest commit because ooba said it uses the latest llama. Sort by: Also P40 has I use a P40 and 3080, I have used the P40 for training and generation, my 3080 can't train (low VRAM). 58 TFLOPS, FP32 (float) Just wanted to share that I've finally gotten reliable, repeatable "higher context" conversations to work with the P40. I'm considering Quadro P6000 and Tesla P40 to use for machine learning. Exllama loaders do not work due to dependency on FP16 instructions. As a result, inferencing is slow. 29 TFLOPS Optimization for Pascal graphics cards (GTX 10XX, Tesla P40) Question Using a Tesla P40 I noticed that when using llama. RTX 3090: FP16 (half) = 35. 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. cpp that improved performance. While I can guess at the performance of the P40 based off 1080 Ti and Titan X(Pp), 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. 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. If that's the case, they use like half the ram, and go a ton faster. Neox-20B is a fp16 model, so it wants 40GB of VRAM by default. Then each card will be responsible for 77 votes, 56 comments. 0 is 11. 7 GFLOPS , FP32 (float) = 11. are installed correctly I believe. Subreddit to discuss about Llama, the large language model created by Meta AI. This is because Pascal cards have dog crap FP16 performance as we all know. I saw mentioned that a P40 would be a cheap option to get a lot of vram. I would probably split it between a couple windows VMs running video encoding and game streaming. The P40 offers slightly more VRAM (24gb vs 16gb), but is GDDR5 vs HBM2 in the P100, meaning it has far lower bandwidth, The Tesla P40 and P100 are both within my prince range. 76 TFLOPS FP64: 0. Get the Reddit app Scan this QR code to download the app now. Having a very hard time finding benchmarks though. However the ability to run larger models and the recent developments to GGUF make it worth it IMO. About 1/2 the speed at inference. videos or gifs of things suddenly or unexpectedly becoming trans. llama. The Tesla line of cards should definitely get a significant performance boost out of fp16. In order to do so, you’ll need to enable above 4G in the Integrated Peripherals section of the BIOS and you’ll Get the Reddit app Scan this QR code to download the app now. Nvidia drivers are version 510. My P40 is about 1/4 the speed of my 3090 at fine tuning. Or check it out in the app stores running Ubuntu 22. P40 Pros: 24GB VRAM is more future-proof and there's a chance I'll be able to run language models. 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 Get the Reddit app Scan this QR code to download the app now. We ask that you please take a minute to read through the rules and check out the So Tesla P40 cards work out of the box with ooga, but they have to use an older bitsandbyes to maintain compatibility. VLLM requires hacking setup. It's generally thought to be a poor GPU for machine learning because of "inferior 16-bit support", lack of tensor cores and such, which is one of the main reasons it's so cheap now Tesla P40 has really bad FP16 performance compared to more modern GPU's: FP16 (half) =183. cpp GGUF! Discussion Share Add a Comment. I was aware of the fp16 issue w/ p40 but wasn’t For the vast majority of people, the P40 makes no sense. Works great with ExLlamaV2. The P40 offers slightly more VRAM (24gb vs 16gb), but is GDDR5 vs HBM2 in the P100, meaning it has far lower bandwidth, The biggest advantage of P40 is that you get 24G of VRAM for peanuts. . Note that llama. You Tesla P40 24G ===== FP16: 0. Curious on this as well. The P40 also has basically no half precision / FP16 support, which negates most benefits of having 24GB VRAM. 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. Still, the only better used option than P40 is the 3090 and it's quite a step up in price. But that guide assumes you have a GPU newer than Pascal or running on CPU. I too was looking at the P40 to replace my old M40, until I looked at the fp16 speeds on the P40. The 16g P100 is a better buy, it has stronger FP16 performance with the added 8g. Or check it out in the app stores NVIDIA Tesla P4 & P40 - New Pascal GPUs Accelerate Inference in the Data Center so it won't have the double-speed FP16 like the P100 but it does have the fast INT8 like the Pascal Titan X. com) Seems you need to make some registry setting changes: After installing the driver, you may notice that Get the Reddit app Scan this QR code to download the app now. Cardano; Dogecoin; Algorand; Bitcoin; Litecoin; Basic Attention Token; Bitcoin Cash; Full-precision LLama3 8b Instruct GGUF for inference on Tesla P40 and other 24 gb cards Resources https://huggingface. py and building from source but also runs well. I saw a couple deals on used Nvidia P40's 24gb and was thinking about grabbing one to install in my R730 running proxmox. /r/StableDiffusion is back open after the protest of Reddit killing open API access Anyone try to mix up Tesla P40 24G and Tesla P100 16G for dual card LLM inference? It works slowly with Int4 as vLLM seems to use only the optimized kernels with FP16 instructions that are slow on the P40, but Int8 and above works fine. Or check it out in the app stores   Tesla P40 users - OpenHermes 2 Mistral 7B might be the sweet spot RP model with extra context. In the past I've been using GPTQ (Exllama) on my main system with the The Tesla P40 and P100 are both within my prince range. 76 TFLOPS. 179K subscribers in the LocalLLaMA community. While it is technically capable, it runs fp16 at 1/64th speed compared to fp32. And keep in mind that the P40 needs a 3D printed cooler to function in a consumer PC. 8. You can look up all these cards on techpowerup and see theoretical speeds. I have two P100. Becuase exl2 want fp16, but tesla p40 for example don't have it. 367 TFLOPS another reddit post gave a hint regarding AMD card AMD Instinct MI25 with 4096 stream-processors, and a good performance: AMD Instinct MI25 16G ===== FP16: 24. 04 LTS Desktop and which also has an Nvidia Tesla P40 card installed. The price of used Tesla P100 and P40 cards have fallen hard recently (~$200-250). Compared to the Pascal Titan X, the P40 has all SMs The 24GB on the P40 isn't really like 24GB on a newer card because the FP16 support runs at about 1/64th the speed of a newer card (even the P100). 0, it seems that the Tesla K80s that I run Stable Diffusion on in my server are no longer usable since the latest version of CUDA that the K80 supports is 11. P6000 has higher memory bandwidth and active cooling (P40 has passive cooling). We are Reddit's primary hub for all things modding, from troubleshooting for beginners to creation of mods by experts. But a strange thing is that P6000 is cheaper when I buy them from reseller. The new NVIDIA Tesla P100, powered by the GP100 GPU, can perform FP16 arithmetic at twice the throughput of FP32. 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. It's a pretty good combination, the P40 can generate 512x512 images in about 5 seconds, the 3080 is about 10x faster, I imagine the 3060 will see a similar improvement in generation. uddagqn kulzo rgierz kphkkig ajcf gvwpbz vystcy agly rbrky vsji