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. (Note: Do not go older than a P40. Apr 30, 2023 · I have a Ryzen 5 2400G, a B450M bazooka v2 motherboard and 16GB of ram. Q4_K_M on H100-PCIe (with --n-gpu-layers 100 -n 128) the performance goes from 143. cpp会快一些。刚试了一下，用llama. The only downside of P100 is the high idle power draw, around 30W with nothing going on. You signed out in another tab or window. Anyway would be nice to find a way to use gptq with pascal gpus. cpp and even there it needs the CUDA MMQ compile flag set. Your other option would be to try and squeeze in 7B GPTQ models with Exllama loaders. cpp, vicuna, alpaca in 4 bits version on my computer. /main -t 22 -m model. I keep trying to use the llama. cpp better with: Mixtral 8x7B instruct GGUF at 32k context. cpp dev Johannes is seemingly on a mission to squeeze as much performance as possible out of P40 cards. The fastest use of llama. This being both Pascal architecture, and work on llama. Members Online LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b The llama. Is commit dadbed9 from llama. nvidia P40 are well supported by llama. cpp的软件来充分发挥CPU性能。 Mar 9, 2024 · GPU 1: Tesla P40, compute capability 6. Pretty sure its a bug or unsupported, but I get 0. 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. I would like to run AI systems like llama. 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. 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. cpp is adding GPU support. ESP32 is a series of low cost, low power system on a chip microcontrollers with integrated Wi-Fi and dual-mode Bluetooth. Overview Jan 29, 2025 · llama_perf_sampler_print: sampling time = 178. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. You signed in with another tab or window. root再次执行，报错。 Contribute to MarshallMcfly/llama-cpp development by creating an account on GitHub. Im very budget tight right now and thinking about building a server for inferencing big models like R+ under ollama/llama. Subreddit to discuss about Llama, the large language model created by Meta AI. 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. Since Cinnamon already occupies 1 GB VRAM or more in my case. This is a collection of short llama. cpp is a powerful and efficient I’ve added another p40 and two p4s for a total of 64gb vram. 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. LINUX INSTRUCTIONS: 6. not just P40, ALL gpu. In 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 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. py and add: and don't recompile or modify the llama_cpp files For example, with llama. int4量化是可以跑llama 70B的，如果用bitsandbytes的话稍慢，用llama. 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. cpp, you can run the 13B parameter model on as little as ~8gigs of VRAM. cpp and exllama. CUDA. How can I specify for llama. 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. Fully loaded up around 1. cpp, read this documentation Contributing Contributors can open PRs Collaborators can push to branches in the llama. I literally didn't do any tinkering to get the RX580 running. Combining this with llama. 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. Mar 15, 2024 · I believe that LM Studio uses the llama. cpp in the last few days, and should be merged in the next P40 has more Vram, but sucks at FP16 operations. gppm will soon not only be able to manage multiple Tesla P40 GPUs in operation with multiple llama. Aug 6, 2023 · hashicco. cpp: Jul 31, 2024 · Since commit b3188 llama-cli produce incoherent output on multi-gpu system with CUDA and row tensor splitting. cpp the video card is only half loaded (judging by power consumption), but the speed of the 13B Q8 models is quite acceptable. yarn-mistral-7b-128k. I really appreciate the breakdown of the timings as well. Initially I was unsatisfied with the p40s performance. I could still run llama. 34 ms per token, 17. Reload to refresh your session. This lightweight software stack enables cross-platform use of llama. 179K subscribers in the LocalLLaMA community. 5 model level with such speed, locally upvotes · comments Hardware config is Intel i5-10400 (6 cores, 12 threads ~2. Name and Version. 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. cpp revision 8f1be0d built with cuBLAS, CUDA 12. 1, VMM: yes. To learn more how to measure perplexity using llama. Pascal or newer is required to run 4bit quantizatized models. 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. cpp or its cousins and there is no training/fine-tuning. Reply reply llama_print_timings: prompt eval time = 30047. 3 21. gguf If you run llama. GGML backends. Hardware. 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 updated to the latest commit because ooba said it uses the latest llama. cpp seems to run best with all memory in a single NUMA Node as of Q1 2025. 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. int8()，AutoGPTQ, GPTQ-for-LLaMa, exllama, llama. 3 GB/s. In the interest of not treating u/Remove_Ayys like tech support, maybe we can distill them into the questions specific to llama. 98 t/s Overclocked M40 - 23. 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. Not much different than getting any card running. 5位、2位、3位、4位、5位 Wait, does exllamav2 support Pascal cards? Broken FP16 on these. cpp I have a Tesla P40 buy from China's second-hand webstore for LM inference. Flash Attention implementation for older NVIDIA GPUs without requiring Tensor Cores has come to llama. The P40 is a LOT faster than an ARM Mac, and a lot cheaper. cpp that improved performance. 21 ms / 1622 runs ( 411 77 votes, 56 comments. Very briefly, this means that you can possibly get some speed increases and fit much larger context sizes into VRAM. 94 tokens per second) llama_print_timings: total time = 54691. But according to what -- RTX 2080 Ti (7. 9k次，点赞23次，收藏25次。一、关于 llama. cpp, offering a streamlined and easy-to-use Swift API for developers. 5) So yea a difference is between llama. 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. Works great with ExLlamaV2. 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. 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. RTX 3090 TI + Tesla P40 Note: One important piece of information. Good point about where to place the temp probe. 1 8B @ 8192 context (Q6K) P40 - 31. /llama-cli -m models/tiny-vicuna-1b. I’m getting between 7-8 t/s for 30B models with 4096 context size and Q4. 70 ms / 213 runs ( 111. Jan 18, 2024 · 文章浏览阅读1. After that, should be relatively straight forward. I went to dig into the ollama code to prove this wrong and actually you're completely right that llama. I was up and running. 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. This supposes ollama uses the llama. cpp benchmarks on various Apple Silicon hardware. I always do a fresh install of ubuntu just because. (I have a couple of my own Q's which I'll ask in a separate comment. I went with the dual p40's just so I can use Mixtral @ Q6_K with ~22 t/s in llama. Just realized I never quite considered six Tesla P4. The newer GPTQ-for-llama forks that can run it struggle for whatever reason. I have dual P40's. Using CPU alone, I get 4 tokens/second. 0 1x). Sep 30, 2024 · For the massive Llama 3. i talk alone and close. 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. gppm monitors llama. 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. But the P40 sits at 9 Watts . 5 40. it is still better on GPU. cpp because of fp16 computations, whereas the 3060 isn't. I had to go with quantized versions event though they get a bit slow on the inference time. cpp instances utilizing NVIDIA Tesla P40 or P100 I have rig with 2x P40, 2xP4 - works very well with llama. ExLlamaV2 is kinda the hot thing for local LLMs and the P40 lacks support here. 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. RTX 3090 TI + RTX 3060 D. 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. May 8, 2023 · Just search eBay for Nvidia P40. From what I understand AutoGPTQ gets similar speeds too, but I haven’t tried. cpp developer it will be the software used for testing unless specified otherwise. cpp/kcpp There's also a lot of optimizations in llama. 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. In terms of pascal-relevant optimizations for llama. Since I am a llama. cpp loaders. cpp is running. They were introduced with compute=6. cpp branch, and the speed of Mixtral 8x7b is beyond insane, it's like a Christmas gift for us all (M2, 64 Gb). 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. A probe against the exhaust could work but would require testing & tweaking the GPU This is fantastic information. The p40 is connected through a PCIE 3. Q4_0. 5 和 DeepSeek 模型，从环境搭建到 GPU 加速，全面覆盖！ Dec 5, 2024 · 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 tried dual P40 with dual P4 in the half width slots. But now, with the right compile flags/settings in llama. But it's still the cheapest option for LLMs with 24GB. cpp on Debian Linux. I have observed a gradual slowing of inferencing perf on both my 3090 and P40 as context length increases. 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. cpp, which is optimized for running models on CPUs and GPUs with reduced memory requirements. The only catch is that the p40 only supports CUDA compat 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 using FP16 operations under the hood for GGML 4-bit models? Nov 27, 2024 · You signed in with another tab or window. 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. Cost on ebay is about $170 per card, add shipping, add tax, add cooling, add GPU cpu power cable, 16x riser cables. q5_k_m. cpp has something similar to it (they call it optimized kernels? not entire sure). cpp servers are a subprocess under ollama. cpp's output to recognize tasks and on which GPU lama. They work amazing using llama. 4B-Instruct on llama. I have a P40. 8 t/s on the new WizardLM-30B safetensor with the GPTQ-for-llama (new) cuda branch. hatenablog. But I'd strongly suggest trying to source a 3090. At the moment every P40 worldwide running with llama. Oct 2, 2024 · To address this problem, llama. Again this is inferencing. 47 ms / 515 tokens ( 58. I just bought another last week. And your integrated Intel GPU certainly isn't supported by the CUDA backend, so LM Studio can't use it. cpp Performance testing (WIP) This page aims to collect performance numbers for LLaMA inference to inform hardware purchase and software configuration decisions. So, what exactly is the bandwidth of the P40? Does anyone know? But it does not have the integer intrinsics that llama. 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. It rocks. 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). cpp) work well with the P40. cpp beats exllama on my machine and can use the P40 on Q6 models. They could absolutely improve parameter handling to allow user-supplied llama. cpp without external dependencies. I was hitting 20 t/s on 2x P40 in KoboldCpp on the 6 Hi, great article, big thanks. Inferencing will slow on any system when there is more context to process. cpp parameters around here. 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. cpp aimed to squeeze as much performance as possible out of this older architecture like working flash attention. 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. Linux. 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. I ran all tests in pure shell mode, i. Be sure to add an aftermarket cooling fan ($15 on eBay), as the P40 does not come with its own. 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. cpp handle it automatically. it's faster than ollama but i can't use it for conversation. cpp or exllama or similar, it seems to be perfectly functional, compiles under cuda toolkit 12. 50 ms per token, 5. cpp (gguf) make my 2 cards work equally around 80% each. cd build Jul 16, 2024 · 文章浏览阅读1. Both GPUs running PCIe3 x16. What this means for llama. Reply reply You can definitely run GPTQ on P40. llama. For what it's worth, if you are looking at llama2 70b, you should be looking also at Mixtral-8x7b. 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. 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. cpp for the inferencing backend, 1 P40 will do 12 t/s avg on Dolphin 2. I really want to run the larger models. cpp it will work. cpp I am asked to set CUDA_DOCKER_ARCH accordingly. 35 to 163. 5k次，点赞19次，收藏20次。接上篇前面的实验，chat. 83 tokens per second (14% speedup). It's a different implementation of FA. I think dual p40's is certainly worth it. 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. Hi, something weird, when I build llama. Example of inference speed using llama. cpp with LLAMA_HIPBLAS=1. With my P40, GGML models load fine now with Llama. hi, I have a Tesla p40 card. Exllama 1 You seem to be monitoring the llama. completely without x-server/xorg. 7 Llama-2-13B 13. So now llama. 43 tokens per second) llama_perf_context_print: load time = 188784. I am looking for old graphics cards with a lot of memory (16GB minimum) and cheap type P40, M40, Radeon mi25. Reply reply Hi, great article, big thanks. 87 ms per token, 8. 21 ms llama_perf_context_print: prompt eval time = 3323. cpp and the advent of large-but-fast Mixtral-8x7b type models, I find that this box does the job very well. Multi GPU usage isn't solid like single. Just installed a recent llama. Then I cut and paste the handful of commands to install ROCm for the RX580. I've been working on trying the llama. You pretty much NEED to add fans in order to get them cooled, otherwise they thermal-throttle and become very slow. 9ghz) 64GB DDR4 and a Tesla P40 with 24gb Vram. 42 ms / 17 tokens ( 195. 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. However the ability to run larger models and the recent developments to GGUF make it worth it IMO. 14 tokens per second) llama_print_timings: eval time = 23827. You switched accounts on another tab or window. Use llama. Not that I take issue with llama. Because of the 32K context window, I find myself topping out all 48GB of VRAM. Hopefully llama. cpp, continual improvements and feature expansion in llama. 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. Layer tensor split works fine but is actually almost twice slower. Here is the execution of a token using the current llama. Restrict each llama. 7 cuDNN: 8. 1 which the P40 is. So the difference you're seeing is perfectly normal, there are no speed gains to expect using exllama2 with those cards. llama-cli version b3188 built on Debian 12. GGUF is a format used by llama. Note that 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. Qwen2. cpp officially supports GPU acceleration. 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. Someone advise me to test compiled llama. 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. It inferences about 2X slower than exllama from my testing on a RTX 4090, but still about 6X faster than my CPU (Ryzen 5950X). gppm must be installed on the host where the GPUs are installed and llama. On a 7B 8-bit model I get 20 tokens/second on my old 2070. 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. cpp with some fixes can reach that (around 15-20 tok/s on 13B models with autogptq). cpp CUDA backend. cpp uses for quantized inferencins. 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. invoke with numactl --physcpubind=0 --membind=0 . I've fit upto 34B models on a single P40 @ 4-bit. Jul 7, 2023 · I have a intel scalable gpu server, with 6x Nvidia P40 video cards with 24GB of VRAM each. cpp achieves across the M-series chips and hopefully answer questions of people wondering if they should upgrade or not. 5-2. Tesla P40 C. cpp with scavenged "optimized compiler flags" from all around the internet, IE: mkdir build. 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. ) What stands out for me as most important to know: Q: Is llama. We run a test query from the llama. You can help this by offloading more layers to the P40. 前書き. I have 256g of ram and physical 32 cores. In this case, the M40 is only 20% slower than the P40. P6000 is the exact same core architecture as P40 (GP102), so driver installation and compatibility is a breeze. cpp now have decent GPU support and has both a memory tester and lets you load partial models (n-layers) into your GPU. 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. cpp#12402. You probably have a var env for that but I think you can let llama. Quad Nvidia Tesla P40 on dual Xeon E5-2699v4 (two cards per CPU) Models. cpp setup now has the following GPUs: 2 P40 24GB 1 P4 8GB. 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. You can also use 2/3/4/5/6 bit with llama. I rebooted and compiled llama. The activity bounces between GPUs but the load on the P40 is higher. May 19, 2024 · Saved searches Use saved searches to filter your results more quickly llama. I don't expect support from Nvidia to last much longer though. 4 And the P40 is around $200, though it needs some extra work. cpp#13282. Jun 24, 2024 · For me personally my solution works fine and also offers some other features but beside that yes, full ack. cpp MLC/TVM Llama-2-7B 22. 2xP40 are now running mixtral at 28 Tok/sec with latest llama. A 13B llama2 model, however, does comfortably fit into VRAM of the P100 and can give you ~20tokens/sec using exllama. 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. 加起来应该不到4000元. Using a Tesla P40 I noticed that when using llama. eg. Feb 25, 2025 · Hello all, I am currently facing an issue with loading EXAONE-3. I was hitting 20 t/s on 2x P40 in KoboldCpp on the 6 llama. 1. cpp still has a CPU backend, so you need at least a decent CPU or it'll bottleneck. Everywhere else, only xformers works on P40 but I had to compile it. 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. cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. 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. Combining multiple P40 results in slightly faster t/s than a single P40. I was under the impression both P40 and P100 along with the GTX 10x0 consumer family were really usable only with llama. It can be useful to compare the performance that 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. This lets you run the models on much smaller harder than you’d have to use for the unquantized models. cpp, RTX 4090, and Intel i9-12900K CPU For inferencing: P40, using gguf model files with llama. cpp with the P40. 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 loader with gguf files it is orders of magnitude faster. Jul 29, 2024 · I have an RTX 2080 Ti 11GB and TESLA P40 24GB in my machine. cpp HF. 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 with the P100, but my understanding is I can only run llama. 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. have to edit llama. I've been poking around on the fans, temp, and noise. (Don’t use Ooba) Im wondering if anybody tried to run command R+ on their p40s or p100s yet. Downsides are that it uses more ram and crashes when it runs out of memory. cpp burns somebodies money. There is a reason llama. Which I think is decent speeds for a single P40. 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。. I plugged in the RX580. GPU are 3x Nvidia Tesla + 3090 All future commits seems to be affected. P100 has good FP16, but only 16gb of Vram (but it's HBM2). cpp provides a vast array of functionality to optimize model performance and deploy efficiently on a wide range of hardware. Unfortunately I can't test on my triple P40 setup anymore since I sold them for dual Titan RTX 24GB cards. 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. May 16, 2024 · Flash Attention implementation for older NVIDIA GPUs without requiring Tensor Cores has come to llama. com. hi, i have a Tesla p40 card, it's slow with ollama and Mixtral 8x7b. cpp 运行 Qwen2. cpp server example under the hood. cpp runs them on and with this information accordingly changes the performance modes of installed P40 GPUs. 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. cpp has been even faster than GPTQ/AutoGPTQ. There's a couple caveats though: These cards get HOT really fast. 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. First of all, when I try to compile llama. 7. The SpeziLLM package, e The missing variable here is the 47 TOPS of INT8 that P40 have. The most excellent JohannesGaessler GPU additions have been officially merged into ggerganov's game changing llama. cpp, with a 7Bq4 model on P100, I get 22 tok/s without batching. 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). 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. But 24gb of Vram is cool. cpp flash attention. The second is same setup, but with P40 24GB + GTX1080ti 11GB graphics cards. The hardware demands scale dramatically with model size, from consumer-friendly to enterprise-level setups. gguf. 5 VL Series, please use the model files converted by ggml-org/llama. Had mixed results on many LLMs due to how they load onto VRAM. With vLLM, I get 71 tok/s in the same conditions (benefiting from the P100 2x FP16 performance). cpp, koboldcpp, exllama, etc. 2. g. Now that it works, I can download more new format models. GPT 3. cpp would be great. Lately llama. These results seem off though. Im wondering what kind of prompt eval t/sec we could be expecting as well as generation speed. 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. 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. cpp with all cores across both processors your inference speed will suffer as the links between both CPUs will be saturated. Agreed, Koboldcpp (and by extension llama. 8 t/s for a 65b 4bit via pipelining for inference. 11 ms per token, 9184. 9. Llama-3. B. 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. A fix directly in llama. crashr/gppm – launch llama. Here are some screenshots from NSight Systems which show why using CUDA graphs is of benefit. gppm uses nvidia-pstate under the hood which makes it possible to switch the performance state of P40 GPUs at all. One is from the NVIDIA official spec, which says 347 GB/s, and the other is from the TechpowerUP database, which says 694. 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. The popular unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF repos are not supported vision yet. In my experience this fact alone is enough to make me use them an order of magnitude more, my P40 mostly sit idle. Other model formats make my card #1 run at 100% and card #2 at 0%. if your engine can take advantage of it. You can see some performance listed here. sh确认是运行在CPU模式下，未启用GPU支持重新编译llama. cpp process to one NUMA domain (e. 12 tokens per second) llama_perf_context_print: eval time = 667566. cpp logs to decide when to switch power states. Someone advise me to test compiling llama. No stack. 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. Llama. cpp is always to use a single GPU which is the fastest one available. cpp root folder . 1, so you must use llama. 39 ms. 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. . 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. e. cpp GGUF is that the performance is equal to the average tokens/s performance across all layers. 56 ms / 1640 runs ( 0. 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. i use this command Aug 12, 2024 · Llama 3. Again, take this with massive salt. 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. 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. 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. Still kept one P40 for testing. 0 x1 riser card cable to the P40 (yes the P40 is running at PCI 3. 75 t/s (Prompt processing: P40 - 750 t/s, M40 - 302 t/s) Quirks: I recommend using legacy Quants if possible with the M40. Apr 19, 2024 · For example, inference for llama-2-7b. I tried that route and it's always slower. 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. cpp and even getting new features (like flash attention). cpp。 只有在模型梯度更新时才… Sep 21, 2024 · ggerganov / llama. At its core, llama. However if you chose to virtualize things like I did with Proxmox, there's more to be done getting everything setup properly. And for $200, it's looking pretty tasty. Jan 15, 2025 · crashr/gppm – launch llama. I use it daily and it performs at excellent speeds. cpp leverages the ggml tensor library for machine learning. clal zfkg zynl ndks tsjb rabp amsdg gpyv tovg jvsuj