Nvidia p40 llm reddit. The Tesla P40 and P100 are both within my prince range.


  • Nvidia p40 llm reddit Hello, TLDR: Is an RTX A4000 "future proof" for studying, running and training LLM's locally or should I opt for an A5000? Im a Software Engineer and yesterday at work I tried running Picuna on a NVIDIA RTX A4000 with 16GB RAM. But 24gb of Vram is cool. As far as i can tell it would be able to run the biggest open source models currently available. gguf I'm planning to build a server focused on machine learning, inferencing, and LLM chatbot experiments. For my May 16, 2023 · Here, I provide an in-depth analysis of GPUs for deep learning/machine learning and explain what is the best GPU for your use-case and budget. mlc-llm doesn't support multiple cards so that is not an option for me. Cons: Most slots on server are x8. 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. Mar 9, 2024 · I have a few numbers here for various RTX 3090 TI, RTX 3060 and Tesla P40 setups that might be of interest to some of you. I'd like to get a M40 (24gb) or a P40 for Oobabooga and StableDiffusion WebUI, among other things (mainly HD texture generation for Dolphin texture…. falcon-180b-chat. P40 supports Cuda 6. Llama. g. And for $200, it's looking pretty tasty. reading time: 47 minutes. Renting some a100 80gb cards, like in this project. As they are from an old gen, we can find some quite cheap on ebay, what about a good cpu, 128Gb of ram and 3 of them (24Gb each) ? My target is to run something like mistral 7b with a great throughout (30tk/s or more) or even try mistral 8x7b (quantitized I guess), and serve only a few concurent users users (poc/beta test) The P40 was designed by Nvidia for data centers to provide inference, and is a different beast than the P100. That should help with just about any type of display out setup. Would start with one P40 but would like the option to add another later. After I connected the video card and decided to test it on LLM via Koboldcpp I noticed that the generation speed from ~20 tokens/s dropped to ~10 tokens/s. What is your budget (ballpark is okay)? I'm planning to build a server focused on machine learning, inferencing, and LLM chatbot experiments. 1 and that includes the instructions required to run it. Q4_K_M. Currently exllama is the only option I have found that does. Other than using ChatGPT, Stable Diff Hey, Tesla P100 and M40 owner here. This P40 has 3480 CUDA cores: https://resources. From cuda sdk you shouldn’t be able to use two different Nvidia cards has to be the same model since like two of the same card, 3090 cuda11 and 12 don’t support the p40. ExLlamaV2 is kinda the hot thing for local LLMs and the P40 lacks support here. The P40 offers slightly more VRAM (24gb vs 16gb), but is GDDR5 vs HBM2 in the P100, meaning it has far lower bandwidth, which I believe is important for inferencing. The performance of P40 at enforced FP16 is half of FP32 but something seems to happen where 2xFP16 is used because when I load FP16 models they work the same and still use FP16 memory footprint. While doing some research it seems like I need lots of VRAM and the cheapest way would be with Nvidia P40 GPUs. "Pascal" was the first series of Nvidia cards to add dedicated FP16 compute units, however despite the P40 being part of the Pascal line, it lacks the same level of FP16 performance as other Pascal-era cards. Kinda sorta. Dell and PNY ones and Nvidia ones. cpp: Prompt: > Tell me about gravity. BUT there are 2 different P40 midels out there. My budget for now is around $200, and it seems like I can get 1x P40 with 24GB of VRAM for around $200 on ebay/from china. nvidia. Dell and PNY ones only have 23GB (23000Mb) but the nvidia ones have the full 24GB (24500Mb). I also have one and use it for inferencing. A few details about the P40: you'll have to figure out cooling. 24GB is the most vRAM you'll get on a single consumer GPU, so the P40 matches that, and presumably at a fraction of the cost of a 3090 or 4090, but there are still a number of open source models that won't fit there unless you shrink them considerably. Works great with ExLlamaV2. Would the whole "machine" suffice to run models like MythoMax 13b, Deepseek Coder 33b and CodeLlama 34b (all GGUF) The new NVIDIA Tesla P100, powered by the GP100 GPU, can perform FP16 arithmetic at twice the throughput of FP32. I was really impressed by its capabilites which were very similar to ChatGPT. I achieve around 7-8 t/s with ~6k of context. NVIDIA Tesla P40 24GB Proxmox Ubuntu 22. For AMD it’s similar same generation model but could be like having 7900xt and 7950xt without issue. I also have a 3090 in another machine that I think I'll test against. In these tests, I If you're solely looking to build a computer to run LLMs, you'd likely do better on a server board with a TON of 12GB RTX 3060's running at PCIE X4, or better yet, four Nvidia P40's or five Nvidia P100's. P100 has good FP16, but only 16gb of Vram (but it's HBM2). Budget for graphics cards would be around 450$, 500 if i find decent prices on gpu power cables for the server. Uses around 10GB VRAM while inferencing. Hi everyone, I have decided to upgrade from an HPE DL380 G9 server to a Dell R730XD. It works nice with up to 30B models (4 bit) with 5-7 tokens/s (depending on context size). cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. com/en-us-virtualization-and-gpus/p40-datasheet I would like to upgrade it with a GPU to run LLMs locally. What's the performance of the P40 using mlc-llm + CUDA? mlc-llm is the fastest inference engine, since it compiles the LLM taking advantage of hardware specific optimizations. While it is technically capable, it runs fp16 at 1/64th speed compared to fp32. Here's a suggested build for a system with 4 NVIDIA P40 GPUs: Hardware: CPU: Intel Xeon Scalable Processor or AMD EPYC Processor (at least 16 cores) GPU: 4 x NVIDIA Tesla P40 GPUs Motherboard: A motherboard compatible with your selected CPU, supporting at least 4 PCIe x16 slots (e. I think the sweet spot for the P40 would be if a 7B model with 16K context is used. It'll automatically adjust the power state based on if the GPUs are idle or not. A P40 will run at 1/64th the speed of a card that has real FP16 cores. The server already has 2x E5-2680 v4's, 128gb ecc ddr4 ram, ~28tb of storage. Sure, the 3060 is a very solid GPU for 1080p gaming and will do just fine with smaller (up to 13b) models. 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. You can look up all these cards on techpowerup and see theoretical speeds. , ASUS WS C621E SAGE or Supermicro H11DSi) Cost: As low as $70 for P4 vs $150-$180 for P40 Just stumbled upon unlocking the clock speed from a prior comment on Reddit sub (The_Real_Jakartax) Below command unlocks the core clock of the P4 to 1531mhz nvidia-smi -ac 3003,1531 . So lots to spare. I typically upgrade the slot 3 to x16 capable, but reduces total slots by 1. The Tesla P40 and P100 are both within my prince range. Anyone try this yet, especially for 65b? I think I heard that the p40 is so old that it slows down the 3090, but it still might be faster from ram/cpu. In nvtop and nvidia-smi the video card jumps from 70w to 150w (max) out of 250w. I was wondering if adding a used tesla p40 and splitting the model across the vram using ooba booga would be faster than using ggml cpu plus gpu offloading. Est. Here is one game I've played on the P40 and plays quite nicely DooM Eternal is The idea now is to buy a 96GB Ram Kit (2x48) and Frankenstein the whole pc together with an additional Nvidia Quadro P2200 (5GB Vram). Just make sure you have enough power and a cooling solution you can rig up, and you're golden. M40 is almost completely obsolete. Actually, I have a P40, a 6700XT, and a pair of ARC770 that I am testing with also, trying to find the best low cost solution that can also be A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. I've only used Nvidia cards as a passthrough so I can't help much with other types. P40 still holding up ok. If you want faster RP with the P40, this model is worth trying. I too was looking at the P40 to replace my old M40, until I looked at the fp16 speeds on the P40. If the goal is to test a theory, you could go for faster feedback & avoid hardware till your forced too. Not sure where you get the idea the newer card is slower. But you can do a hell of a lot more LLM-wise with a P40. For what it's worth, if you are looking at llama2 70b, you should be looking also at Mixtral-8x7b. If this is going to be a "LLM machine", then the P40 is the only answer. But with Nvidia you will want to use the Studio driver that has support for both your Nvidia cards P40/display out. Q5_K_M quantisation. I've seen people use a Tesla p40 with varying success, but most setups are focused on using them in a standard case. 04 VM w/ 28 cores, 100GB allocated memory, PCIe passthrough for P40, dedicated Samsung SM863 SSD And just to toss out some more data points, here's how it performs: Using llama. P40 has more Vram, but sucks at FP16 operations. This can be really confusing. Running a local LLM linux server 14b or 30b with 6k to 8k context using one or two Nvidia P40s. Bits and Bytes however is compiled out of the box to use some instructions that only work for Ampere or newer cards even though they do not need to be. Very briefly, this means that you can possibly get some speed increases and fit much larger context sizes into VRAM. The difference is the VRAM. 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. But at the moment I don't think there are any based on Pyg. Sep 4, 2024 · Check out the recently released \`nvidia-pstated\` daemon. napeaxg uuua ocusak enijr jnets buymq htlbgf avsvcc usdbs rsfp