- Tesla p40 llama reddit review I have observed a gradual slowing of inferencing perf on both my 3090 and P40 as context length increases. If someone someday fork exl2 with upcast in fp32 (not for memory saving reason, but for speed reason) - it will be amazing. Someone advise me to test compiled llama. With llama. 14 tokens per second) llama_print_timings: eval time = 23827. cpp to take advantage of speculative decoding on llama-server. come talk about Ryzen, Radeon, Zen4, RDNA3, EPYC, Threadripper, rumors, reviews, news and more. But The P40 is supported by the latest Data Center drivers for CUDA 11. ExLlama relies heavily on FP16 math, and the P40 just has The price of used Tesla P100 and P40 cards have fallen hard recently (~$200-250). ) 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. have to edit llama. The original and largest Tesla community on Reddit! An unofficial forum of owners and enthusiasts. news, reviews, and advice on finding the perfect gaming laptop. The Tesla P40 and P100 are both within my prince range. While I can guess at the performance of the P40 based off 1080 Ti and Titan X (Pp), To create a computer build that chains multiple NVIDIA P40 GPUs together to train AI models like LLAMA or GPT-NeoX, you will need to consider the hardware, software, and infrastructure components of your build. cpp on Debian Linux. The Pascal series (P100, P40, P10 ect) is the GTX 10XX series GPUs. 1/72 Airfix P-40 starter kit. If you happen to know about any other free GPU VMs, please do share them in the comments below. 27 Context: 8k The P40 driver is paid for and is likely to be very costly. ML Dual Tesla P40 Rig Case recomendations comments. Becuase exl2 want fp16, but tesla p40 for example don't have it. 94 tokens per second) llama_print_timings: total time = 54691. 70 ms / 213 runs ( 111. 25 votes, 24 comments. Unfortunately you are wrong. r/LocalLLM. Everywhere else, only xformers works on P40 but I had to compile it. 12 GB RAM 80 GB DISK Tesla T4 GPU with 15 GB VRAM This setup is sufficient to run most models effectively. RTX was designed for gaming and media editing. 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. I was also planning to use ESXi to pass through P40. context_params Get the Reddit app Scan this QR code to download the app now. Join our passionate community to stay informed and connected with the latest trends and technologies in the gaming laptop world. This is a HP Z840 with dual Intel Xeon processors. Get the Reddit app Scan this QR code to download the app now. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. The P40 is sluggish with Hires-Fix and Upscaling but it does Ok so here’s what I’ve found in my testing with P40 and P100s. Or beacuse gguf allows offload big model on 12/16 gb cards but exl2 doesn't. py and add: self. Tutorial | Guide In terms of pascal-relevant optimizations for llama. Tesla P40 plus quadro 2000 I want to get help with installing a tesla p40 correctly alongside the quadro so I can still use a display. Join our passionate community Welcome to /r/AMD — the subreddit for all things AMD; come talk about Ryzen, Radeon, Zen4, RDNA3, EPYC, Threadripper, rumors, reviews, news and more. Tensorflow2 minimum cuda 10 support. But for now it's only for rich people with 3090/4090 Was looking for a cost effective way to train voice models, bought a used Nvidia Tesla P40, and a 3d printed cooler on eBay for around 150$ and crossed my fingers. First of all, when I try to compile llama. I understand P40's won't win any speed contests but they are hella cheap, and there's plenty of used rack servers that will fit 8 of them with all the appropriate PCIE lanes and whatnot. /r/GuildWars2 is the primary community for Guild Wars 2 on Reddit. These GPU's a really good for inferencing but forget about trying training/fine-tuning. I've built the latest llama. Subreddit to discuss about Llama, the large language model created by Meta AI. 21 ms / 69 tokens ( 10. 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. Power delivery or temp) P40 isn't very well supported (yet). ADMIN MOD Coolers for Tesla P40 cards . Yes! the P40's are faster and draw less power. I was hitting 20 t/s on 2x P40 in KoboldCpp on the 6 Trying to get a Tesla P40 to run externally. cpp I am asked to set CUDA_DOCKER_ARCH accordingly. Possibly because it supports int8 and that is Hi 3x P40 crew. Wiki. he talk alone or he's talking nonsense Get the Reddit app Scan this QR code to download the app now. not just P40, ALL gpu. 23 ms / 431 runs ( 0. My Tesla p40 came in today and I got right to testing, after some driver conflicts between my 3090 ti and the p40 I got the p40 working with some sketchy cooling. Get the Reddit app Scan this QR code to download the app now yes ggml_init_cublas: CUDA_USE_TENSOR_CORES: no ggml_init_cublas: found 1 CUDA devices: Device 0: Tesla P40, compute capability 6. The server already has 2x E5-2680 v4's, 128gb ecc ddr4 ram, ~28tb of storage. Tesla P40’s lack FP16 for some dang reason, so they tend to suck for training, but there may be hope of doing int8 or maybe int4 IMHO going the GGML / llama-hf loader seems to currently be the better option for P40 users, as perf and VRAM usage seems better compared to AUTOGPTQ. GPU-Z is a useful tool for monitoring. cpp, P40 will have similar tps speed to 4060ti, which is about 40 tps with 7b quantized models. In llama. The Tesla P40 is much faster at GGUF than the P100 at GGUF. cpp you can try playing with LLAMA_CUDA_MMV_Y (1 is default, try 2) and LLAMA_CUDA_DMMV_X (32 is default try 64). cpp it will work. . post kit reviews and discuss the latest kits! And much more! Members Online. Obviously I'm only able to run 65b models on the cpu/ram (I can't compile the latest llama. Budget for graphics cards would I have dual P40's. This post also conveniently leaves out the fact that CPU and hybrid CPU/GPU inference exists, which can run Llama-2-70B much cheaper then even the affordable 2x TESLA P40 option above. 5\_instruct 32b\_q8 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. 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 But a Tesla p40 uses a different driver and cuda 6. Discussion come talk about Ryzen, Radeon, Zen4, RDNA3, EPYC, Threadripper, rumors, reviews, news and more. In the comments section, I will be sharing a sample Colab notebook specifically designed for beginners. At a rate of 25-30t/s vs 15-20t/s running Q8 GGUF models. Question | Help Has anybody tried an M40, and if so, what are the speeds, especially compared to the P40? Llama 2 13B working on RTX3060 12GB with Nvidia Chat with RTX with one edit /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper . Note: Reddit is dying due to terrible leadership from CEO /u/spez. Or check it out in the app stores Tesla P40: llama_print_timings: load time = 702. Tesla M40 vs P40 speed . 18 ms per Get the Reddit app Scan this QR code to download the app now. That is I have a Tesla p40 card. I know it's the same "generation" as my 1060, but it has four times the memory and more power in Tiny PSA about Nvidia Tesla P40 . See r/TeslaLounge for relaxed posting, and user experiences! I sold my Tesla after one year (an honest review) General I bought a Tesla Model 3 last year and I just sold it (well, in Jan) It was an AWD LR. They will both do the job fine but the P100 will be more efficient for training neural networks. Share Add It's slow because your KV cache is no longer offloaded. Most people here don't need RTX 4090s. These results seem off though. Possibly because it supports int8 and that is somehow used on it using its higher CUDA 6. 34 ms per token, 17. 47 ms / 515 tokens ( 58. Please use our Discord server instead of supporting a Model: bartowski/Meta-Llama-3-70B-Instruct-GGUF · Hugging Face Quant: IQ4_NL GPU: 2x Nvidia Tesla P40 Machine: Dell PowerEdge r730 384gb ram Backend: KoboldCPP Frontend: Silly Tavern (fantasy/RP stuff removed replaced with coding preferences) Samplers: Dynamic Temp 1 to 3, Min-P 0. Non-nvidia alternatives still can be difficult to get working, and even more hassle to I'm considering buying a cheap Tesla M40 or P40 for my PC that I also use for gaming, with RTX 2060. System is just one of my old PCs with a B250 Gaming K4 motherboard, nothing fancy Works just fine on windows 10, and training on Mangio-RVC- Fork at fantastic speeds. Discussion First off, do these cards work with nicehash? Discover discussions, news, reviews, and advice on finding the perfect gaming laptop. 1, Smooth Sampling 0. 1. reviews, and advice on finding the perfect gaming laptop. It doesn’t matter what type of deployment you are using. 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 Has anyone attempted to run Llama 3 70B unquantized on an 8xP40 rig? I'm looking to put together a build that can run Llama 3 70B in full FP16 precision. Members Online. 28 tokens per second) llama_print_timings: prompt eval time = 702. In these tests, I I saw that the Nvidia P40 arent that bad in price with a good VRAM 24GB and wondering if i could use 1 or 2 to run LLAMA 2 and increase inference times? I have an RTX 2080 Ti 11GB and TESLA P40 24GB in my machine. No other alternative available from nvidia with that budget and with that amount of vram. The P100 also has dramatically higher FP16 and FP64 performance than the P40. Or check it out in the app stores TOPICS Subreddit to discuss about Llama, the large language model created by Meta AI. This sub-reddit is dedicated to I've seen people use a Tesla p40 with varying success, but most setups are focused on using them in a standard case. Members Online • lunaxoxo. I don't remember the wattage of the PSU at the moment, but I think it is 1185 watt. 39 ms. I'm running Debian 12. Over 30,000 miles on it. it's faster than ollama but i can't use it for conversation. 23 ms per token, 4300. Be a So my P40 is only using about 70W while generating responses, its not limited in any way (IE. x and 12. cpp with "-DLLAMA_CUDA=ON -DLLAMA_CLBLAST=ON -DLLAMA_CUDA_FORCE_MMQ=ON" option in order to use FP32 and acceleration on this old cuda card. I am still running a 10 series GPU on my main workstation, they are still relevant in the gaming world and cheap. /r/AMD is community run and does not represent AMD in any capacity unless specified. 37 ms llama_print_timings: sample time = 100. That's not going to hold you back from using current models, but is important to know going in. Inferencing will slow on any system when there is more context to process. x in Windows and passthrough works for WSL2 using those drivers. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. 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. But the Tesla series are not gaming cards, they are compute nodes. It's a different implementation of FA. Tesla P40 . true. I'm running qwen2. I'm contemplating a 24GB Tesla P40 card as a temporary solution. 18 ms per I use a P40 and 3080, I have used the P40 for training and generation, my 3080 can't train (low VRAM). I loaded my model (mistralai/Mistral-7B-v0. There's a couple caveats though: Tesla P40's aren't as fast as they just have a lot of VRAM. 87 ms per token, 8. 2) only on the P40 and I got around yes, I use an m40, p40 would be better, for inference its fine, get a fan and shroud off ebay for cooling, and it'll stay cooler plus you can run 24/7, don't pan on finetuning though. ccp to enable gpu offloading for ggml due to a weird but but that's unrelated to this post. You'll also Ok so here’s what I’ve found in my testing with P40 and P100s. llama_print_timings: prompt eval time = 30047. I used the paint Welcome to the official subreddit of the PC Master Race / PCMR! All PC-related content is welcome, including build help, tech support, and any doubt one might have about PC ownership. Tesla M40 vs. debian. They work amazing using llama. I have read that the Tesla series was designed with machine learning in mind and optimized for deep learning. Also, you're going to be limited to running GGUF quants, because the Tesla P40 doesn't have sufficiently advanced CUDA for the EXL2 process. org states that both cards use different drivers. kfpftqb qnv pbdzj gbc ydziui entd rjeov pktsmgtg ueo jycjyt