Llama cpp p40 cpp, but I've been running into issues with it not utilizing the GPU's as it keeps loading into RAM and using the CPU. cpp and exllama. Jun 13, 2023 · If you use CUDA mode on it with AutoGPTQ/GPTQ-for-llama (and use the use_cuda_fp16 = False setting) I think you'll find the P40 is capable of some really good speeds that come closer to the RTX generation. 1. There's a couple caveats though: These cards get HOT really fast. 179K subscribers in the LocalLLaMA community. cpp achieves across the M-series chips and hopefully answer questions of people wondering if they should upgrade or not. cpp的主要目标是能够在各种硬件上实现LLM推理，只需最少的设置，并提供最先进的性能。提供1. int8()，AutoGPTQ, GPTQ-for-LLaMa, exllama, llama. This might not play As a P40 user it needs to be said Exllama is not going to work, and higher context really slows inferencing to a crawl even with llama. int4量化是可以跑llama 70B的，如果用bitsandbytes的话稍慢，用llama. I've fit upto 34B models on a single P40 @ 4-bit. Hardware. Other model formats make my card #1 run at 100% and card #2 at 0%. cpp to use as much vram as it needs from this cluster of gpu's? Does it automatically do it? I am following this guide at step 6 Llama. The hardware demands scale dramatically with model size, from consumer-friendly to enterprise-level setups. 14 tokens per second) llama_print_timings: eval time = 23827. cpp officially supports GPU acceleration. 8 t/s on the new WizardLM-30B safetensor with the GPTQ-for-llama (new) cuda branch. cpp loaders. cpp runs them on and with this information accordingly changes the performance modes of installed P40 GPUs. cpp logs to decide when to switch power states. However if you chose to virtualize things like I did with Proxmox, there's more to be done getting everything setup properly. 3-70B-Instruct-GGUF or llama-cpp-python: CMAKE_ARGS="-DLLAMA_CUBLAS=ON -DLLAMA_AVX2=OFF -DLLAMA_F16C=OFF -DLLAMA_FMA=OFF" pip install llama-cpp-python. Initially I was unsatisfied with the p40s performance. I've been working on trying the llama. cpp or its cousins and there is no training/fine-tuning. With my P40, GGML models load fine now with Llama. Apr 30, 2023 · I have a Ryzen 5 2400G, a B450M bazooka v2 motherboard and 16GB of ram. One is from the NVIDIA official spec, which says 347 GB/s, and the other is from the TechpowerUP database, which says 694. Use llama. B. yarn-mistral-7b-128k. I've tried setting the split to 4,4,1 and defining GPU0 (a P40) as the primary (this seems to be the default anyway), but the most layers I can get in GPU without hitting an OOM, however, is 82. 50 ms per token, 5. Jun 9, 2023 · In order to evaluate of the cheap 2nd-hand Nvidia Tesla P40 24G, this is a little experiment to run LLMs for Code on Apple M1, Nvidia T4 16G and P40. cpp Performance testing (WIP) This page aims to collect performance numbers for LLaMA inference to inform hardware purchase and software configuration decisions. Jul 7, 2023 · I have a intel scalable gpu server, with 6x Nvidia P40 video cards with 24GB of VRAM each. Someone advise me to test compiling llama. GPT 3. I plugged in the RX580. Quad Nvidia Tesla P40 on dual Xeon E5-2699v4 (two cards per CPU) Models. cpp, which is optimized for running models on CPUs and GPUs with reduced memory requirements. (Don’t use Ooba) Im wondering if anybody tried to run command R+ on their p40s or p100s yet. In llama. 56 ms / 1640 runs ( 0. cpp to test the LLaMA models inference speed of different GPUs on RunPod, 13-inch M1 MacBook Air, 14-inch M1 Max MacBook Pro, M2 Ultra Mac Studio and 16-inch M3 Max MacBook Pro for LLaMA 3. But the P40 sits at 9 Watts . But now, with the right compile flags/settings in llama. Exllama 1 You seem to be monitoring the llama. 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. I literally didn't do any tinkering to get the RX580 running. cpp now though as I've been learning more today about the FP16 weakness of the P40 Jan 26, 2024 · 你是否想在本地机器上运行强大的语言模型，却苦于复杂的配置和性能问题？本文将带你一步步在 Windows 11 上使用 llama. In this case, the M40 is only 20% slower than the P40. At its core, llama. I rebooted and compiled llama. It can be useful to compare the performance that llama. Feb 25, 2025 · Hello all, I am currently facing an issue with loading EXAONE-3. q5_k_m. cpp is always to use a single GPU which is the fastest one available. Inferencing will slow on any system when there is more context to process. 以前記事にした鯖落ちP40を利用して作った機械学習用マシンですが、最近分析界隈でも当たり前のように使われ始めているLLMを動かすことを考えると、GPUはなんぼあってもいい状況です。 Feb 27, 2025 · llama. cpp and even there it needs the CUDA MMQ compile flag set. ESP32 is a series of low cost, low power system on a chip microcontrollers with integrated Wi-Fi and dual-mode Bluetooth. I think the last update was getting two P40s to do ~5 t/s on 70b q4_K_M which is an amazing feat for such old hardware. Here is the execution of a token using the current llama. 47 ms / 515 tokens ( 58. cpp uses for quantized inferencins. have to edit llama. The only catch is that the p40 only supports CUDA compat 6. Combining this with llama. Hi, something weird, when I build llama. cpp I am asked to set CUDA_DOCKER_ARCH accordingly. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage Styled Lines (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers For multi-gpu models llama. 75 t/s (Prompt processing: P40 - 750 t/s, M40 - 302 t/s) Quirks: I recommend using legacy Quants if possible with the M40. cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. cpp benchmarks on various Apple Silicon hardware. I use it daily and it performs at excellent speeds. What I was thinking about doing though was monitoring the usage percentage that tools like nvidia-smi output to determine activity -- ie: if GPU usage is below 10% for over X minutes, then switch to low power state (and inverse if GPU goes above 40% for more The P40 is restricted to llama. cpp, offering a streamlined and easy-to-use Swift API for developers. cpp is a powerful and efficient Aug 23, 2023 · 部署环境 系统：CentOS-7 CPU: 14C28T 显卡：Tesla P40 24G 驱动: 515 CUDA: 11. At the moment every P40 worldwide running with llama. cpp because of fp16 computations, whereas the 3060 isn't. 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. cpp developer it will be the software used for testing unless specified otherwise. cpp, log output is below, I similarly had issues when using split mode row May 19, 2024 · Saved searches Use saved searches to filter your results more quickly llama_print_timings: prompt eval time = 30047. cpp repo and merge PRs into the master branch Collaborators will be invited based on contributions Any help with managing issues and PRs is very appreciated! With this I can run Mixtral 8x7B GGUF Q3KM at about 10t/s with no context and slowed to around 3t/s with 4K+ context. They could absolutely improve parameter handling to allow user-supplied llama. 以前記事にした鯖落ちP40を利用して作った機械学習用マシンですが、最近分析界隈でも当たり前のように使われ始めているLLMを動かすことを考えると、GPUはなんぼあってもいい状況です。 Sep 30, 2024 · For the massive Llama 3. Be sure to add an aftermarket cooling fan ($15 on eBay), as the P40 does not come with its own. 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. cpp has been even faster than GPTQ/AutoGPTQ. (Note: Do not go older than a P40. Jul 27, 2023 · To partially answer my own question, the modified GPTQ that turboderp's working on for ExLlama v2 is looking really promising even down to 3 bits. 39 ms. To learn more how to measure perplexity using llama. cpp loader with gguf files it is orders of magnitude faster. crashr/gppm – launch llama. Members Online LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b The llama. cpp, read this documentation Contributing Contributors can open PRs Collaborators can push to branches in the llama. Jan 18, 2024 · 文章浏览阅读1. The popular unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF repos are not supported vision yet. gppm will soon not only be able to manage multiple Tesla P40 GPUs in operation with multiple llama. 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. They work amazing using llama. The fastest use of llama. Especially for quant forms like GGML, it seems like this should be pretty straightforward, though for GPTQ I understand we may be working with full 16 bit floating point values for some calculations. 1, so you must use llama. Not that I take issue with llama. cpp using FP16 operations under the hood for GGML 4-bit models? Nov 27, 2024 · You signed in with another tab or window. Still kept one P40 for testing. 11 ms per token, 9184. P6000 is the exact same core architecture as P40 (GP102), so driver installation and compatibility is a breeze. I went with the dual p40's just so I can use Mixtral @ Q6_K with ~22 t/s in llama. cpp would be great. com. cpp process to one NUMA domain (e. The llama Pascal FA kernel works on P100 but performance is kinda poor the gain is much smaller 😟 I use vLLM+gptq on my P100 same as OP but I only have 2 Oct 18, 2023 · 本文讨论了部署 LLaMa 系列模型常用的几种方案，并作了速度测试。 参考Kevin吴嘉文：LLaMa 量化部署包括 Huggingface 自带的 LLM. cpp/kcpp There's also a lot of optimizations in llama. 5-2. Since Cinnamon already occupies 1 GB VRAM or more in my case. Q4_0. Im very budget tight right now and thinking about building a server for inferencing big models like R+ under ollama/llama. 12 tokens per second) llama_perf_context_print: eval time = 667566. You can help this by offloading more layers to the P40. cpp) work well with the P40. It rocks. Jan 15, 2025 · crashr/gppm – launch llama. cpp , it just seems models perform slightly worse with it perplexity-wise when everything else is kept constant vs gptq Jul 12, 2023 · My llama. (I have a couple of my own Q's which I'll ask in a separate comment. I am looking for old graphics cards with a lot of memory (16GB minimum) and cheap type P40, M40, Radeon mi25. The P40 is a LOT faster than an ARM Mac, and a lot cheaper. GGML backends. I keep trying to use the llama. 7. 21 ms / 1622 runs ( 411 77 votes, 56 comments. Fully loaded up around 1. I'm looking to probably do a bifurcation 4 way split to 4 RTX 3060 12GBs pcie4, and support the full 32k context for 70B Miqu at 4bpw. Apr 19, 2024 · For example, inference for llama-2-7b. 2xP40 are now running mixtral at 28 Tok/sec with latest llama. For what it's worth, if you are looking at llama2 70b, you should be looking also at Mixtral-8x7b. 42 ms / 17 tokens ( 195. Someone advise me to test compiled llama. I have 256g of ram and physical 32 cores. But 24gb of Vram is cool. And your integrated Intel GPU certainly isn't supported by the CUDA backend, so LM Studio can't use it. Since I am a llama. I was up and running. Now that it works, I can download more new format models. cpp beats exllama on my machine and can use the P40 on Q6 models. I went to dig into the ollama code to prove this wrong and actually you're completely right that llama. Sep 30, 2024 · For the massive Llama 3. I have a P40. 5) So yea a difference is between llama. 3 GB/s. In terms of pascal-relevant optimizations for llama. I was under the impression both P40 and P100 along with the GTX 10x0 consumer family were really usable only with llama. What this means for llama. 2. Your other option would be to try and squeeze in 7B GPTQ models with Exllama loaders. And for $200, it's looking pretty tasty. You can see some performance listed here. Because of the 32K context window, I find myself topping out all 48GB of VRAM. Good point about where to place the temp probe. cd build Jul 16, 2024 · 文章浏览阅读1. I have dual P40's. Overview Jan 29, 2025 · llama_perf_sampler_print: sampling time = 178. 9. GGUF is a format used by llama. 5 和 DeepSeek 模型，从环境搭建到 GPU 加速，全面覆盖！ Dec 5, 2024 · llama. You signed in with another tab or window. cpp故障：nvcc fatal : Value 'native' is not defined for option 'gpu-architecture'查看gpu-arch修改Makefile，调整MK_NVCCFLAGS差异如下重新编译启动报错ptrace: 不允许的操作. Llama-3. The activity bounces between GPUs but the load on the P40 is higher. Aug 15, 2023 · Hello, I am trying to get some HW to work with llama 2 the current hardware works fine but its a bit slow and i cant load the full models. I had to go with quantized versions event though they get a bit slow on the inference time. You pretty much NEED to add fans in order to get them cooled, otherwise they thermal-throttle and become very slow. Using a Tesla P40 I noticed that when using llama. These results seem off though. 7 cuDNN: 8. 43 tokens per second) llama_perf_context_print: load time = 188784. it is still better on GPU. cpp server example under the hood. Feb 6, 2025 · domani ci provo! Converting a model from Hugging Face to the GGUF format using a Tesla P40 on Windows 10 involves several steps. 前書き. Downsides are that it uses more ram and crashes when it runs out of memory. hi, i have a Tesla p40 card, it's slow with ollama and Mixtral 8x7b. cpp instances, but also to switch them completely independently of each other to the lower performance mode when no task is running on the respective GPU and to the higher performance mode when a task has been started on it. invoke with numactl --physcpubind=0 --membind=0 . py and add: and don't recompile or modify the llama_cpp files For example, with llama. cpp, with a 7Bq4 model on P100, I get 22 tok/s without batching. Im wondering what kind of prompt eval t/sec we could be expecting as well as generation speed. gppm monitors llama. cpp, ExLlama, AutoGPTQ, GPTQ-for-LLaMa, ctransformers 支持多类模型， Llama-2-chat, Alpaca, Vicuna, WizardLM, StableLM等 图形化界面聊天，微调 Operating systems. cpp. I have observed a gradual slowing of inferencing perf on both my 3090 and P40 as context length increases. cpp, koboldcpp, exllama, etc. I think dual p40's is certainly worth it. g. cpp and the advent of large-but-fast Mixtral-8x7b type models, I find that this box does the job very well. The p40 is connected through a PCIE 3. But I'd strongly suggest trying to source a 3090. CUDA. cpp#13282. I just bought another last week. cpp运行4bit量化的Qwen-72B-Chat，生成速度是5 tokens/s左右。 Jun 3, 2023 · I'm not sure why no-one uses the call in llama. Reload to refresh your session. Just installed a recent llama. Training can be performed on this models with LoRA’s as well, since we don’t need to worry about updating the network’s weights. So the difference you're seeing is perfectly normal, there are no speed gains to expect using exllama2 with those cards. But according to what -- RTX 2080 Ti (7. eg. Reply reply You can definitely run GPTQ on P40. nvidia P40 are well supported by llama. 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 HF. hi, I have a Tesla p40 card. Multi GPU usage isn't solid like single. 5k次，点赞19次，收藏20次。接上篇前面的实验，chat. cpp with the P40. gguf -p “I believe the meaning of life is” -n 128 –n-gpu-layers 6 You should get an output similar to the output below: Nov 25, 2023 · Regarding the memory bandwidth of the NVIDIA P40, I have seen two different statements. cpp has something similar to it (they call it optimized kernels? not entire sure). Subreddit to discuss about Llama, the large language model created by Meta AI. For training: P100, though you'd prob be better off in the training aspect utilizing cloud, considering how cheap it is, I've got a p100 coming in end of the month and will see how well it does on fp16 with exllama. cpp is a powerful and efficient I’ve added another p40 and two p4s for a total of 64gb vram. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage Styled Lines (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers Dec 11, 2023 · For my Master's thesis in the digital health field, I developed a Swift package that encapsulates llama. /main -t 22 -m model. 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). Lately llama. 26 介绍 简单好用(当然速度不是最快的)， 支持多种方式加载模型，transformers, llama. cpp的软件来充分发挥CPU性能。 Mar 9, 2024 · GPU 1: Tesla P40, compute capability 6. I added a P40 to my gtx1080, it's been a long time without using ram and ollama split the model between the two card. cpp with LLAMA_HIPBLAS=1. 1 which the P40 is. Here are some screenshots from NSight Systems which show why using CUDA graphs is of benefit. Pascal or newer is required to run 4bit quantizatized models. Example of inference speed using llama. cpp on Debian Linux. cpp for the inferencing backend, 1 P40 will do 12 t/s avg on Dolphin 2. You signed out in another tab or window. 70 ms / 213 runs ( 111. But I found the LCPP doesn't use the cuda int8 on dequantization Inference speed is determined by the slowest GPU memory's bandwidth, which is the P40, so a 3090 would have been a big waste of its full potential, while the P6000 memory bandwidth is only ~90gb/s faster than the P40 I believe. Oct 31, 2024 · В сентябре‑октябре, судя по новостям вышел особенно богатый урожай мультимодальных нейросетей в открытом доступе, в этом посте будем смотреть на Pixtral 12B и LLaMA 32 11B, а запускать их будем на It's slow because your KV cache is no longer offloaded. Which I think is decent speeds for a single P40. cpp, RTX 4090, and Intel i9-12900K CPU For inferencing: P40, using gguf model files with llama. May 19, 2024 · Saved searches Use saved searches to filter your results more quickly llama. But it's still the cheapest option for LLMs with 24GB. Then I cut and paste the handful of commands to install ROCm for the RX580. Tesla P40 C. I updated to the latest commit because ooba said it uses the latest llama. e. InternVL2/InternVL3 Series; LLaMA4 Series, please test with ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF repo, or the model files converted by ggml-org/llama. i talk alone and close. cpp better with: Mixtral 8x7B instruct GGUF at 32k context. After that, should be relatively straight forward. The second is same setup, but with P40 24GB + GTX1080ti 11GB graphics cards. 3B, 7B, and 13B models have been unthoroughly tested, but going by early results, each step up in parameter size is notably more resistant to quantization loss than the last, and 3-bit 13B already looks like it could be a winner. llama-cli version b3188 built on Debian 12. I always do a fresh install of ubuntu just because. This lightweight software stack enables cross-platform use of llama. They were introduced with compute=6. cpp setup now has the following GPUs: 2 P40 24GB 1 P4 8GB. Layer tensor split works fine but is actually almost twice slower. Had mixed results on many LLMs due to how they load onto VRAM. 83 tokens per second (14% speedup). 34 ms per token, 17. gguf. 0 1x). 4 And the P40 is around $200, though it needs some extra work. I’m getting between 7-8 t/s for 30B models with 4096 context size and Q4. cpp or exllama or similar, it seems to be perfectly functional, compiles under cuda toolkit 12. Not much different than getting any card running. My goal is to basically have something that is reasonably coherent, and responds fast enough to one user at a time for TTS for something like home assistant. Pretty sure its a bug or unsupported, but I get 0. cpp is adding GPU support. . I really want to run the larger models. root再次执行，报错。 Contribute to MarshallMcfly/llama-cpp development by creating an account on GitHub. A 13B llama2 model, however, does comfortably fit into VRAM of the P100 and can give you ~20tokens/sec using exllama. cpp instances utilizing NVIDIA Tesla P40 or P100 I have rig with 2x P40, 2xP4 - works very well with llama. cpp in the last few days, and should be merged in the next I'm also seeing only fp16 and/or fp32 calculations throughout llama. 2 and is quite fast on p40s (I'd guess others as well, given specs from nvidia on int based ops), but I also couldn't find it in the official docs for the cuda math API here either: https://docs. Aug 6, 2023 · hashicco. cpp handle it automatically. cpp branch, and the speed of Mixtral 8x7b is beyond insane, it's like a Christmas gift for us all (M2, 64 Gb). Note that llama. Flash Attention implementation for older NVIDIA GPUs without requiring Tensor Cores has come to llama. cpp dev Johannes is seemingly on a mission to squeeze as much performance as possible out of P40 cards. Very briefly, this means that you can possibly get some speed increases and fit much larger context sizes into VRAM. One moment: Note: ngl is the abbreviation of Number of GPU Layers with the range from 0 as no GPU acceleration to 100 as full on GPU Ngl is just number of layers sent to GPU, depending on the model just ngl=32 could be enough to send everything to GPU, but on some big 120 layers monster ngl=100 would send only 100 out of 120 layers. 1, VMM: yes. cpp, continual improvements and feature expansion in llama. 35 to 163. Pros: No power cable necessary (addl cost and unlocking upto 5 more slots) 8gb x 6 = 48gb Cost: As low as $70 for P4 vs $150-$180 for P40 Dec 1, 2023 · 显卡：二手P40 24G x2. The SpeziLLM package, e The missing variable here is the 47 TOPS of INT8 that P40 have. So configure in BIOS a single NUMA node per CPU socket and only use a single CPU I just recently got 3 P40's, only 2 are currently hooked up. 8 t/s for a 65b 4bit via pipelining for inference. So now llama. The only downside of P100 is the high idle power draw, around 30W with nothing going on. cpp, you can run the 13B parameter model on as little as ~8gigs of VRAM. 0 x1 riser card cable to the P40 (yes the P40 is running at PCI 3. This being both Pascal architecture, and work on llama. Hope this helps! Reply reply Mar 29, 2024 · In this connection there is a question: is there any sense to add one more but powerful video card, for example RTX3090, to 1-2 Tesla P40 video cards? If GPU0 becomes this particular graphics card, won't it improve some properties of the inference? Sure, I'm mostly using AutoGPTQ still because I'm able to get it working the nicest, but I believe that llama. cpp's output to recognize tasks and on which GPU lama. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage Styled Lines (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers Jun 14, 2023 · Sorry @JohannesGaessler all I meant was your test approach isn't going to replicate the issue because you're not in a situation where you have more VRAM than RAM. You probably have a var env for that but I think you can let llama. So, what exactly is the bandwidth of the P40? Does anyone know? But it does not have the integer intrinsics that llama. In the interest of not treating u/Remove_Ayys like tech support, maybe we can distill them into the questions specific to llama. completely without x-server/xorg. cpp that improved performance. cpp aimed to squeeze as much performance as possible out of this older architecture like working flash attention. cpp leverages the ggml tensor library for machine learning. cpp really the end of the line? Will anything happen in the development of new models that run on this card? Is it possible to run F16 models in F32 at the cost of half VRAM? If so, would that be useful? Nov 22, 2023 · Description. You switched accounts on another tab or window. RTX 3090 TI + Tesla P40 Note: One important piece of information. 7 Llama-2-13B 13. Restrict each llama. llama. However the ability to run larger models and the recent developments to GGUF make it worth it IMO. 87 ms per token, 8. ) What stands out for me as most important to know: Q: Is llama. cpp is running. cpp burns somebodies money. I’ve tried dual P40 with dual P4 in the half width slots. 9ghz) 64GB DDR4 and a Tesla P40 with 24gb Vram. Everywhere else, only xformers works on P40 but I had to compile it. I ran all tests in pure shell mode, i. Jul 29, 2024 · I have an RTX 2080 Ti 11GB and TESLA P40 24GB in my machine. A probe against the exhaust could work but would require testing & tweaking the GPU This is fantastic information. Linux. cpp: Jul 31, 2024 · Since commit b3188 llama-cli produce incoherent output on multi-gpu system with CUDA and row tensor splitting. cpp CUDA backend. I could still run llama. I would like to run AI systems like llama. cpp MLC/TVM Llama-2-7B 22. 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 saw a lot 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. Q4_K_M on H100-PCIe (with --n-gpu-layers 100 -n 128) the performance goes from 143. P100 has good FP16, but only 16gb of Vram (but it's HBM2). 94 tokens per second) llama_print_timings: total time = 54691. cpp with "-DLLAMA_CUDA=ON -DLLAMA_CLBLAST=ON -DLLAMA_CUDA_FORCE_MMQ=ON" in order to use FP32 and acceleration on this old cuda card. 4B-Instruct on llama. Cost on ebay is about $170 per card, add shipping, add tax, add cooling, add GPU cpu power cable, 16x riser cables. RTX 3090 TI + RTX 3060 D. gppm must be installed on the host where the GPUs are installed and llama. it's faster than ollama but i can't use it for conversation. cpp still has a CPU backend, so you need at least a decent CPU or it'll bottleneck. cpp provides a vast array of functionality to optimize model performance and deploy efficiently on a wide range of hardware. Again, take this with massive salt. This lets you run the models on much smaller harder than you’d have to use for the unquantized models. Reply reply llama_print_timings: prompt eval time = 30047. cpp parameters around here. I've been on the fence about toying around with a p40 machine myself since the price point is so nice, but never really knew what the numbers on it looked like since people only ever say things like "I get 5 tokens per second!" P100 are in practice 2-3x faster then P40. cpp revision 8f1be0d built with cuBLAS, CUDA 12. 1 405B, you’re looking at a staggering 232GB of VRAM, which requires 10 RTX 3090s or powerful data center GPUs like A100s or H100s. Again this is inferencing. cpp repo and merge PRs into the master branch Collaborators will be invited based on contributions Any help with managing issues and PRs is very appreciated! crashr/gppm – launch llama. Unfortunately I can't test on my triple P40 setup anymore since I sold them for dual Titan RTX 24GB cards. Is commit dadbed9 from 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. gguf If you run llama. This is a collection of short llama. Finish 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. You can also use 2/3/4/5/6 bit with llama. hatenablog. Qwen2. If you can reduce your available system ram to 8gb or less (perhaps run a memory stress test which lets you set how many GB to use) to load an approx ~10gb model fully offloaded into your 12GB of vram you should be able to Note the latest versions of llama. Just realized I never quite considered six Tesla P4. No stack. Reply reply Hi, great article, big thanks. cpp now have decent GPU support and has both a memory tester and lets you load partial models (n-layers) into your GPU. /llama-cli -m models/tiny-vicuna-1b. The P4s slow things down but lets me add larger contexts if necessary Reply reply To learn more how to measure perplexity using llama. cpp with scavenged "optimized compiler flags" from all around the internet, IE: mkdir build. cpp 运行 Qwen2. cpp with some fixes can reach that (around 15-20 tok/s on 13B models with autogptq). Works great with ExLlamaV2. cpp flash attention. Both GPUs running PCIe3 x16. Mar 15, 2024 · I believe that LM Studio uses the llama. cpp root folder . May 8, 2023 · Just search eBay for Nvidia P40. cpp in the last few days, and should be merged in the next P40 has more Vram, but sucks at FP16 operations. For 7B models, performance heavily depends on how you do -ts pushing fully into the 3060 gives best performance as expected: Meanwhile on the llama. 5 model level with such speed, locally upvotes · comments Hardware config is Intel i5-10400 (6 cores, 12 threads ~2. cpp servers are a subprocess under ollama. i use this command Aug 12, 2024 · Llama 3. 98 t/s Overclocked M40 - 23. Reply reply Saifl • Actually could get it Aug 28, 2023 · 在选择CPU时，考虑核心数、线程数和计算性能，以确保它能够满足LLaMA模型的需求。 需要注意的是，LLaMA还提供了专为CPU优化的模型，如GGML。如果您更喜欢使用CPU进行推理，您可以选择使用GGML格式的模型文件，并借助名为llama. sh确认是运行在CPU模式下，未启用GPU支持重新编译llama. I don't expect support from Nvidia to last much longer though. From what I understand AutoGPTQ gets similar speeds too, but I haven’t tried. How can I specify for llama. The newer GPTQ-for-llama forks that can run it struggle for whatever reason. Hopefully llama. きっかけは、llama2の13BモデルがシングルGPUでは動かなかったこと。. 5 40. cpp (gguf) make my 2 cards work equally around 80% each. 加起来应该不到4000元. ExLlamaV2 is kinda the hot thing for local LLMs and the P40 lacks support here. cpp on a single M1 Pro MacBook三、用法1、基本用法2、对话模式3、网络服务4、交互模式5、持久互动6、语法约束输出 Safetensor models? Whew boy. I was hitting 20 t/s on 2x P40 in KoboldCpp on the 6 llama. GPU are 3x Nvidia Tesla + 3090 All future commits seems to be affected. cpp I have a Tesla P40 buy from China's second-hand webstore for LM inference. It inferences about 2X slower than exllama from my testing on a RTX 4090, but still about 6X faster than my CPU (Ryzen 5950X). The most excellent JohannesGaessler GPU additions have been officially merged into ggerganov's game changing llama. Do you have any cards to advise me with my configuration? Do you have an idea of the performance with the AI progremma Aug 14, 2024 · 17. cpp without external dependencies. LINUX INSTRUCTIONS: 6. With vLLM, I get 71 tok/s in the same conditions (benefiting from the P100 2x FP16 performance). We run a test query from the llama. 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). 1 8B @ 8192 context (Q6K) P40 - 31. cpp it will work. cpp会快一些。刚试了一下，用llama. Name and Version. First of all, when I try to compile llama. cpp with all cores across both processors your inference speed will suffer as the links between both CPUs will be saturated. cpp seems to run best with all memory in a single NUMA Node as of Q1 2025. 5位、2位、3位、4位、5位 Wait, does exllamav2 support Pascal cards? Broken FP16 on these. 3 21. cpp the video card is only half loaded (judging by power consumption), but the speed of the 13B Q8 models is quite acceptable. 9k次，点赞23次，收藏25次。一、关于 llama. I was hitting 20 t/s on 2x P40 in KoboldCpp on the 6 Hi, great article, big thanks. Llama. cpp。 只有在模型梯度更新时才… Sep 21, 2024 · ggerganov / llama. cpp, vicuna, alpaca in 4 bits version on my computer. not just P40, ALL gpu. May 16, 2024 · Flash Attention implementation for older NVIDIA GPUs without requiring Tensor Cores has come to llama. cpp支持的模型：**Multimodal models:****Bindings:****UI: ****Tools:**二、Demo1、Typical run using LLaMA v2 13B on M2 Ultra2、Demo of running both LLaMA-7B and whisper. On a 7B 8-bit model I get 20 tokens/second on my old 2070. Jun 24, 2024 · For me personally my solution works fine and also offers some other features but beside that yes, full ack. 21 ms llama_perf_context_print: prompt eval time = 3323. and its sitting outside my computer case, casue the 3090 Ti is covering the other pcie 16x slot (which is really only a 8x slot if you look it doesn't have the other 8x PCIE pins) lol. cpp#12402. cpp GGUF is that the performance is equal to the average tokens/s performance across all layers. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. In my experience this fact alone is enough to make me use them an order of magnitude more, my P40 mostly sit idle. There is a reason llama. gppm uses nvidia-pstate under the hood which makes it possible to switch the performance state of P40 GPUs at all. 5 VL Series, please use the model files converted by ggml-org/llama. This supposes ollama uses the llama. It's a different implementation of FA. Using CPU alone, I get 4 tokens/second. The ESP32 series employs either a Tensilica Xtensa LX6, Xtensa LX7 or a RiscV processor, and both dual-core and single-core variations are available. I tried that route and it's always slower. cpp and even getting new features (like flash attention). A fix directly in llama. cpp with the P100, but my understanding is I can only run llama. if your engine can take advantage of it. Combining multiple P40 results in slightly faster t/s than a single P40. I really appreciate the breakdown of the timings as well. Agreed, Koboldcpp (and by extension llama. Oct 2, 2024 · To address this problem, llama. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage Styled Lines (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers We would like to show you a description here but the site won’t allow us. I've been poking around on the fans, temp, and noise. Anyway would be nice to find a way to use gptq with pascal gpus. pjuhhsoorseddbtbsepomjclapltjarlsxcbhipfzvfabqqgvcnd