Exllama p40 SuperHOT for example relies upon Exllama for proper support of the extended context. Still, the only better used option than P40 is the 3090 and it's quite a step up in price. Hence, the ownership of bind-mounted directories (/data/model and /data/exllama_sessions in the default docker-compose. If you're running 7B or 13B models, a single P100 would be fine. gz. I think if they're far enough apart in versions, that pytorch will start spewing errors about how it expected one card type and got another. 5-16K -GGUF than Oobabooga does with TheBloke/vicuna-13B-v1. A single 4090 processing a LLM filling up its full VRAM or so (33b 4-bit model) can do about ~30-40 tokens per second with exllama, and yet dual 4090s that have filled up their VRAM (65b 4-bit model) are running at like half that speed with ~15-20 tokens per second. 3,855. 0bpw-h6-exl2 With 70b q6_K and 7b q8_0 on 3x P40 the performance it 3. Jun 19, 2023 · I'm seeing 20+ tok/s on a 13B model with gptq-for-llama/autogptq and 3-4 toks/s with exllama on my P40. 0 x1 riser card cable to the P40 (yes the P40 is running at PCI 3. All reactions I have a 3090 and P40 and 64GB ram and can run Meta-Llama-3-70B-Instruct-Q4_K_M. But it does not have the integer intrinsics that llama. md at master · turboderp/exllama Exllama: 9+ t/s, ExllamaV2 1. But when using models in Transformers or GPTQ format (I tried Transformers, AutoGPTQ, all ExLlama loaders), the performance of 13B models even in quad bit format is terrible, and judging by power consumption, more than a third of the GPU is not utilized. 1 on the P40. 4 makes it x10 slower (from 17sec to 170sec to generate synthetic data of a hospital discharge note) ExLlama是一个基于Python/C++/CUDA的独立实现,针对4位GPTQ权重进行了优化,旨在提高现代GPU上的运行速度和内存效率。该项目支持 The official API server for Exllama. 7 cuDNN: 8. I put 12,6 on the gpu-split box and the average tokens/s is 17 with 13b models. cpp is not off the table - on it. 1) so hopefully it also solves it for @ilikenwf!. 4. It's doable with blower style consumer cards, but still less than ideal - you will want to throttle the power usage. A 13B llama2 model, however, does comfortably fit into VRAM of the P100 and can give you ~20tokens/sec using exllama. cpp和Exllama的情况。 “🤨 Aren’t there output quality differences between EXL2 and GGUF with GGUF being slightly better?” The official API server for Exllama. Feb 7, 2025 · Exllama doesn’t make sense for single user / single GPU for most people. Except it requires even higher compute. gguf at an average of 4 tokens a second. ExLlama 是一个独立的 Python/C++/CUDA 实现，专为使用 4 位 GPTQ 权重的 Llama 模型设计，旨在在现代 GPU 上实现快速和内存高效。 免责声明：该项目正在进行中，但尚未完成！ 硬件要求. cpp models with a context length of 1. Please see the Provided Files table above for per-file compatibility. With ExLlama's speed and memory efficiency, I would imagine that a 3-bit 13B model (or 2-bit if really needed) could be quite viable for those of us with less VRAM. 5 times faster than ExllamaV2. You should probably start with smaller models first because the P40 is a very slow card compared to modern cards. ExLlama是一种基于Llama架构的推理引擎，主要用于处理大规模语言模型。与vLLM相比，ExLlama在架构上有所不同，但同样专注于大规模语言模型的推理。在性能上，vLLM通过PagedAttention等技术，可能在某些场景下表现更优。 Sep 8, 2023 · I think V2 is in the works. A P40 using GGUF would be fine. I don't know if the 3090 and 4090 are that far apart I confirm I disabled exLlama/v2 and did not check FP16. There's a third option: this is Pascal, and therefore should compute using fp32, not fp16, internally. cpp GGUF models run on my P6000, but its not fast by any stretch of the imagination. Instead, check out text-generation-webui, it will let you stand up a model on your cards. For nvlink it's faster than exllama. CUDA compute on the 3060 is 8. May 23, 2023 · Note the latest versions of llama. 我在 RTX 4090 和 RTX 3090-Ti 显卡上开发。 Jun 22, 2023 · So far, loading a 3-bit 13B model is possible with AutoGPTQ, but it OOMs around 1k tokens. Again this is inferencing. A place to discuss the SillyTavern fork of TavernAI. Alternatively a P100 (or three) would work better given that their FP16 performance is pretty good (over 100x better than P40 despite also being Pascal, for unintelligible Nvidia reasons); as well as anything Turing/Volta or newer Aug 23, 2023 · 部署环境 系统：CentOS-7 CPU: 14C28T 显卡：Tesla P40 24G 驱动: 515 CUDA: 11. You really don't want these push pull style coolers stacked right against each other. Strange some times works faster depending of the model. Seems like he's thinking about a 48GB config which is a popular high end VRAM break point. If you have two full pci-e 16x slots (not available on consumer Mainboards) with two rtx 3080, it will depend only on drivers and multi gpu supporting the models loader. 2 and cuda117, but updating to 0. M40 is almost completely obsolete. Though at that level, you don't really need exllama speeds. 1. 3. Sometimes users will split models across two 24GB CUDA GPUs. One last thing, I thing I noticed that the perceived quality with ExLlama is way less than AutoGPTQ. 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. In a month when i receive a P40 i´ll try the same for 30b models, trying to use 12,24 with exllama and see if it works. This overwrites the attributes related to the ExLlama kernels in the quantization config of the config. Web UI for ExLlamaV2 You need to enable JavaScript to run this app. While using the standard fp16 version, both platforms perform fairly comparably. Jun 3, 2023 · If P40 will not work with exllama, could somebody advise if oobabooga/GPTQ-for-LLaMa would work? If not CUDA, maybe there are good options for i9-13900K with 128G DDR5? NOTE: by default, the service inside the docker container is run by a non-root user. - exllama/doc/TODO. But it's still the cheapest option for LLMs with 24GB. Power delivery or temp) So my P40 is only using about 70W while For example if you use an Nvidia card, you'd be able to add a cheap $200 p40 for 24gb of vram right? Then you'd be able to split whatever much you could to your main GPU and the rest to the p40. Weights can be stored as fp16, it's just that this architecture has weirdly limited fp16 flops. All performance results are specific to the system they were measured on - for the hardware, operating system, and software - including versions of installed libraries which are being rapidly updated. 6 tokens/s. 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. 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 Dec 11, 2023 · What is the most optimal configuration and which loader for nvidia 3060ti +tesla p40? Now I have achieved at least some work in exllama with a maximum speed of 1. cpp uses for quantized inferencins. P40s can't use these. I’ve found that combining a P40 and P100 would result in a reduction in performance to in between what a P40 and P100 does by itself. P40 still holding up ok. I take a little bit of issue with that. I couldn't try GPTQ-for-LLaMa as it keeps giving errors when loading a model. Everything else is on 4090 under Exllama. Power delivery or temp) So my P40 is only using about 70W while Sep 7, 2023 · Describe the bug Using main branch with commit 4affa08, When I choose 3090, it's about 15 token/s, but when I use p40, it's only has 0. May 22, 2023 · It seems to work on my setup (also Cuda 12. NET 推出的代码托管平台，支持 Git 和 SVN，提供免费的私有仓库托管。目前已有超过 1200万的开发者选择 Gitee。 ExLlamaV2 is a fast inference library that enables the running of large language models (LLMs) locally on modern consumer-grade GPUs. Details for the file exllama-0. 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. In Open WebUI there is an option for another host via OpenAI format. 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. It inferences about 2X slower than exllama from my testing on a RTX 4090, but still about 6X faster than my CPU (Ryzen 5950X). For all models that are larger then the RAM do not work even cud fit in VRAMs + RAM. X16 is faster then X8 and x4 douse not work with p40. 5 GB and fits fully into shared VRAM. While parallel community efforts such as GPTQ-for-LLaMa, Exllama and llama. I agree, to be fair with ooba I just use exllama with 2x4090 and the results are pretty impressive. Got myself an old Tesla P40 Datacenter-GPU (GP102 like GTX1080-silicon but with 24GB ECC vram, 2016) for 200€ from ebay. 53-x64v3-xanmod1 system: "Linux May 23, 2023 · The more VRAM the better if you'd like to run larger LLMs. 4 60. gpu_peer_fix = True model = ExLlama(config) cache = ExLlamaCache(model) tokenizer = ExLlamaTokenizer(tokenizer_model_path) generator = ExLlamaGenerator(model, tokenizer, cache) SGLang has been deployed at large scale, serving trillions of tokens in production every day. 5-16K -GPTQ. Jun 29, 2023 · ExLlama really doesn't like P40s, all the heavy math it does is in FP16, and P40s are very very poor at FP16 math. Jun 8, 2023 · Sorry forget to check model_init file, I adapted the config now it is working. 48 tokens/s Noticeably, the increase in speed is MUCH greater for the smaller model running on the 8GB card, as The ExLlama kernels are only supported when the entire model is on the GPU. Does this mean that when I get my p40, I won't gain anything much in speed for 30b models using exl2 insted of GGUF and maybe even lose out? Yes. The P40’s require EPS12V power, so you’ll need an 8-pin PCIe to 8-pin EPS12V per card. A fast inference library for running LLMs locally on modern consumer-class GPUs - Releases · turboderp-org/exllamav2 CUDA_VISIBLE_DEVICES=1 will limit textgenwebui to only seeing your P40 (this assumes your P40 comes up as the 2nd card in the system) I'd be very curious about the tokens/sec you're getting with exllama or exllama_hf loaders for typical Q/A (small) and long-form chat (large) contexts (say, 200-300 tokens and 1800-2000 tokens). Expected it to slow down but I was thinking might be like 4-5 tokens. The P100 also has dramatically higher FP16 and FP64 performance than the P40. That's all done in webui with its dedicated configs per model now though. I also have a 3090 in another machine that I think I'll test against. remove tokens that exceed the max_seq_len Issues · turboderp First timer building a new (to me) rig for LLM inference, fine-tuning, etc. This makes running 65b sound feasible. The topmost GPU will overheat and throttle massively. Try 8 and 16threads, hopefully you have that much, if not, go get your extra 4090 back or buy a 3090 or P40 to supplemen Thank you for the information, I may look into swapping this 4090 for two 3090 or something else at some point. ExLlama is compatible with Llama models in 4-bit. 9. My understanding is that turboderp would like to have exllama running on p40 efficiently in particular for example. ” 亮点：对比了单GPU情况下llama. MLC uses group quantization, which is the same algorithm as llama. cpp。 The most popular performant desktop llm runtimes are pure GPU (exLLAMA) or GPU + CPU (llama. This makes the models directly comparable to the AWQ and transformers models, for which the cache is not preallocated at load time. 3 120. Aug 2, 2023 · File details. I was hoping to add a third 3090 (or preferably something cheaper/ with more vram) one day when context lengths get really big locally but if you have to keep context on each card that will really start to limit things. 0 1x). It has FP16 support, but only in like 1 out of every 64 cores. I use KoboldCPP with DeepSeek Coder 33B q8 and 8k context on 2x P40 I just set their Compute Mode to compute only using: > nvidia-smi -c 3 And the Processing Prompt do feel much faster with full context. There is a flag for gptq/torch called use_cuda_fp16 = False that gives a massive speed boost -- is it possible to do something similar in exllama? The Tesla P40 is much faster at GGUF than the P100 at GGUF. Aug 1, 2023 · いや本当にchatGPTww. Maybe exllama does this for the P40, but not the 10x0? Wikipedia has these numbers for single/double/half precision. Username checks out; this probably will not help you for your use case. llama. 最後に. 1 again I can't remember, but that was important for some reason. Model: Xwin-LM-70B-V0. . The value of use_exllama will be overwritten by disable_exllama passed in GPTQConfig or stored in your config file. 1 which the P40 is. 5t/s for 34b GPTQ model. The Quad P100 is now running TabbyAPI with Exllama2, serving OpenAI API format. Llama 2 has 4k context, but can we achieve that with AutoGPTQ? I'm probably going to give up on my P40 unless a solution for context is found. PCI-e x16 or x8 for the p40? I have the same problem with p40. I don't expect support from Nvidia to last much longer though. nope, old Exllama still ~2. 现在启动text-generation-webui，并按照下面的说明下载和运行模型。ExLlama应该在第一次加载模型时构建其内核。 Have you tried GGML with CUDA acceleration? You can compile llama. 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 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. Web UI for ExLlamaV2 LLM Inference benchmark. Some additional info on the 70B model: The main thing to know about the P40 is that its FP16 performance suuuucks, even compared to similar boards like the P100. This means you cannot use GPTQ on P40. 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. cpp now have decent GPU support and has both a memory tester and lets you load partial models (n-layers) into your GPU. TLDR ; Issue : LM Studio gives much better results with TheBloke/vicuna-13B-v1. I would really like to see benchmarks with more realistic items users might have. 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. It might have been that it was CUDA 6. 1. 2+, disable the ExLlama kernel in GPTQConfig. cpp HF. You will have to stick with gguf models. Also - importing weights from llama. I didn't try to see what is missing from just commenting the warning out, but I will. Transformers especially has horribly inefficient cache management, which is a big part of As it stands, with a P40, I can't get higher context GGML models to work. The ExLlama kernel is activated by default when users create a GPTQConfig object. sh). 25 t/s (ran more than once to make sure it's not a fluke) Ok, maybe it's the max_seq_len or alpha_value, so here's a test with the default llama 1 context of 2k. Aug 12, 2023 · ExLlama. 63 t/s which is only ~half of what I get with regular Aug 12, 2023 · ExLlama. I have a Quadro P6000 that I am going to sell, so I can get into a higher CUDA compute. It is trusted and adopted by a broad range of leading enterprises and institutions, including xAI, NVIDIA, AMD, Google Cloud, Oracle Cloud, LinkedIn, Cursor, Voltage Park, Atlas Cloud, DataCrunch, Baseten, Nebius, Novita, InnoMatrix, RunPod, Stanford, UC Berkeley, UCLA, ETCHED, Jam & Tea Studios . Ok, maybe it's the fact I'm trying llama 1 30b. I can run the 70b 3bit models at around 4 t/s. Runs fast as this with 0. Exllama v2. Reply reply More replies More replies P-40 does not have hardware support for 4 bit calculation (unless someone develops port to run 4 bit x 2 on int8 cores/instruction set). cpp implement quantization methods strictly for the Llama architecture, AutoGPTQ gained popularity through its smooth coverage of a wide range of transformer architectures. Many of us are also being patient, continuing to presume that open source code running quantized transformer models will become more efficient on p40 cards once some of the really smart people involved get a moment to poke at it. File metadata But the edge for it would be on something like P40, where you can't have GPTQ with act order + group size and are limited from the higher BPW. Its subjective but I thought my compatriots would like to know about that command. py -d /path_to_my_model/ -gs 24,24 from my understanding the parameter -gs (gpu split) should tell exllama to split the model between the two cards, using 24 GB on each one. Aug 9, 2024 · ExLlamaV2 是目前运行大型语言模型（LLMs）最快的库，通过优化 GPTQ 算法和引入新的量化格式 EXL2，显著提升了推理速度和灵活性。。EXL2 格式支持多种量化精度，并允许在模型内部和层之间混合使用不同的精度，从而在保持模型性能的同时减少资源占 There was a time when GPTQ splitting and ExLlama splitting used different command args in oobabooga, so you might have been using the GPTQ split arg in your bat which didnt split the model for the exllama loader. I personally run voice recognition and voice generation on P40. yml file) is changed to this non-root user in the container entrypoint (entrypoint. 2. The P40 is definitely my bottleneck. It is designed to improve performance compared to its predecessor, offering a cleaner and more versatile codebase. A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. If exllama doesn't work anyways, then maybe this is a non issue after flash-attn adds turing support. cpp). If you’re doing inference on a CPU with AutoGPTQ 0. Inference for me is bottlenecked by my GPU and CPU RAM bandwidth. So Exllama performance is terrible. cpp is very capable but there are benefits to the Exllama / EXL2 combination. At a rate of 25-30t/s vs 15-20t/s running Q8 GGUF models. reddit. the m/p40 series 1080 and items like 1660s 3080/90/4080/90 is unrealistic for most users [need a standard telemetry tool for ai] I have a rtx 4070 and gtx 1060 (6 gb) working together without problems with exllama. Here's a recent writeup on the LLM performance you can expect for inferencing (training speeds I assume would be similar): https://www. Personally I gave up on using GPTQ with the P40 because Exllama - with its superior perf and vram efficiency compared to other GPTQ loaders - doesn't work. 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. The main thing to know about the P40 is that its FP16 performance suuuucks, even compared to similar boards like the P100. Tried out all the loading methods and different quants too. Old Nvidia P40 (Pascal 24GB) cards are easily available for $200 or less and would be easy/cheap to play. I split models between a 24GB P40, a 12GB 3080ti, and a Xeon Gold 6148 (96GB system ram). cpp on a P40, not entirely sure why (probably uses fp16 under the hood somewhere I'm guessing). 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. Therefore they cannot use the Exllama loaders and AWQ / EXL2 models. In the past I've been using GPTQ (Exllama) on my main system with the 3090, but this won't work with the P40 due to its lack of FP16 instruction acceleration. It's the first open-source LLM to score an mlc-llm doesn't support multiple cards so that is not an option for me. GPTQ/Autogptq perform much better on Pascal though. CUDA compute is 6. This is wrong. cpp or exllama. Check out airoboros 7b maybe for a starter. You get access to vLLM, exllama, Triton and more with >7 CUDA compute. Huggingface Text Generation Inference (TGI) is compatible with all GPTQ models. It will still be FP16 only so it will likely run like exllama. 1-4. Aug 1, 2023 · For a simple test, I simply ran the chat example that comes with exllama: python3 example_chatbot. json file. model_path = model_path config. I wonder what speeds someone would get with something like a 3090 + p40 setup. Transformer recognize all GPUs. Oct 31, 2024 · 文章浏览阅读378次，点赞3次，收藏10次。ExLlamaV2的本地运行为开发者提供了一个快速、隐私友好的模型推理方案。ExLlamaV2 GitHub 仓库LangChain 官方文档Hugging Face 模型仓库。 Sep 10, 2024 · 与ExLlama的区别. Any Pascal card except the P100 will run badly on exllama/exllamav2. Let's try with llama 2 13b. max_seq_len = 2048 config. I can't get Superhot models to work with the additional context because Exllama is not properly supported on p40. Hello everyone! I've been experimenting with deploying a model using two platforms: vLLM and TGI. ExLlamaV2: The Fastest Library to Run LLMs – Maxime Labonne For VRAM tests, I loaded ExLlama and llama. There may be more performance optimizations in the future, and speeds will vary across GPUs, with slow CPUs still being a potential bottleneck: Oct 3, 2023 · I have a P40 lying around here somewhere, I'll load it up and give it a try. But when using either ooba/Kobold + exllama + SillyTavern, the non-stop is kinda an issue, I guess with ooba you can stop the generation before anything weird happens since it streams the text. 0. They were introduced with compute=6. Aug 23, 2023 · The AutoGPTQ library enables users to quantize 🤗 Transformers models using the GPTQ method. Use use_exllama instead and specify the version with exllama_config. Mar 18, 2024 · TL; DR Run the Smaug-72B Large Language Model locally at 5 tokens/second for under $800 using Ubuntu Linux, Ollama, and two Nvidia Tesla P40 GPUs. cpp and llama-cpp-python with CUBLAS support and it will split between the GPU and CPU. cpp partial offloading. 1 model. Jun 7, 2024 · If the p40 performance isn't basically free I wouldn't bother, GGUF performance is good enough for that specific card, exllamav2 should just stay as the SOTA for SOTA cards rather than bend over backwards to get tiny amounts of gains from ancient cards lol, was just curious The quants and tests were made on the great airoboros-l2-70b-gpt4-1. When I load a 65b in exllama across my two 3090tis, I have to set the first card to 18gb and the second to the full 24gb. set_auto_map('16,24') config. Some have run it at reasonably usable speeds using three or four p40 and server hardware for less than two grand worth of parts, but that's a hacked together solution on old and rapidly out dated hardware and not for everyone (support for those older cards is spotty). cpp, or P100 and exllama, and you're locked in. Any process on exllama todo "Look into improving P40 performance"? env: kernel: 6. 0 where P40 is 6. Flame my choices, recommend me a different way, and any ideas on benchmarking 2x P40 vs 2x P100? Discussion Hello! Some quick tests to compare performance with ExLlama V1. 量化大型语言模型(llm)是减少这些模型大小和加快推理速度的最流行的方法。在这些技术中，GPTQ在gpu上提供了惊人的性能。与非量化模型相比，该方法使用的VRAM几乎减少了3倍，同时提供了相似的精度水平和更快的生成… Dec 14, 2024 · ChatGPT生成的文章摘要 这篇博客记录了作者在家中使用Pascal显卡运行大型模型时遇到的挑战和解决方案。随着本地大型模型性能的提升，作者选择使用vllm库进行推理。然而，作者遇到了多个技术难题，需要自行编译vllm和PyTorch，以支持Pascal架构的显卡。编译过程中，作者深入研究了显卡不 May 8, 2025 · ExLlama-v2 support# ExLlama is a Python/C++/CUDA implementation of the Llama model that is designed for faster inference with 4-bit GPTQ weights. Possibly because it supports int8 and that is somehow used on it using its higher CUDA 6. Auto GPTQ is slower, gobbles up VRAM and much context blows past the vram limit. **So What is SillyTavern?** Tavern is a user interface you can install on your computer (and Android phones) that allows you to interact text generation AIs and chat/roleplay with characters you or the community create. People stuff the biggest models that will fit into the collective RAM + VRAM pool, up to ~48GB for llama 70B. Gitee. Using disable_exllama is deprecated and will be The most popular performant desktop llm runtimes are pure GPU (exLLAMA) or GPU + CPU (llama. OAI compatible, lightweight, and fast. Currently exllama is the only option I have found that does. Not sure where you get the idea the newer card is slower. Pascal cards will perform poorly with exllama because fp16 performance is slow on that generation (I have a P40) and exllama only does fp16. ExLlama w/ GPU Scheduling: Three-run average = 22. This means only very small models can be run on P40. Use this flag CUDA_VISIBLE_DEVICES=x to choose devices Is there an existing issue for t Aug 1, 2023 · いや本当にchatGPTww. Among these techniques, GPTQ delivers amazing performance on GPUs. - theroyallab/tabbyAPI Exllama heavily uses fp16 calculations and AutoGPTQ is unbearably slow compared to llama. Feb 9, 2025 · 整体是非常便宜的配置，rtx3090太贵了，现在价格在800 - 1200美元之间，p40现在400美元，p100也贵得离谱。 附上图片链接： Imgur 、 Imgur 、 Imgur 、 Imgur 。 这是一个相对成本效益高的配置，总成本仍低于1个RTX3090，还为以后升级更好的显卡留有空间。 Hi, great article, big thanks. My takeaway was, P40 and llama. The p40 is connected through a PCIE 3. Aug 1, 2023 · I split models between a 24GB P40, a 12GB 3080ti, and a Xeon Gold 6148 (96GB system ram). 2 Apr 23, 2024 · 本文对Meta发布的LLAMA 3 70B指令微调模型在单个NVIDIA RTX 3090显卡上进行了速度基准测试。结果显示，使用IQ2量化方案的模型表现最佳，每秒可生成12. 今回は爆速レスポンス体験としてのexllama + GPTQを試してみました。本当に早くなる、、今回はtext-generation-webui上でしか試せませんでしたが、普通に推論として動く方法も模索していきたい。 Nov 20, 2023 · Quantizing Large Language Models (LLMs) is the most popular approach to reduce the size of these models and speed up inference. Contribute to ninehills/llm-inference-benchmark development by creating an account on GitHub. If you apply the peer access patch it even does direct transfers on linux. 37. Recently I felt an urge for a GPU that allows training of modestly sized and inference of pretty big models while still staying on a reasonable budget. tar. The Tesla P40 and P100 are both within my prince range. As for the performance, it seems to be about the same, maybe a bit slower than the Cuda branch of GPTQ, though this is mainly because I'm heavily single-core CPU bound + as you said, probably don't benefit much from improvements aimed at newer GPU architectures either. I know with my 3070 and P40, I ended up needing to pass options to get loaders to ignore one or the other before I could get anything to work right. Not sure if it will have support for P40 but then again, you have llama. int8()，AutoGPTQ, GPTQ-for-LLaMa, exllama, llama. Those were done on exllamav2 exclusively (including the gptq 64g model) and the bpws and their VRAM reqs are (mostly to just load, without taking in mind, the cache and the context): 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。 Dec 22, 2023 · Using disable_exllama is deprecated and will be removed in version 4. 43个token，远超其他量化方案。文章还对不同参数设置下的性能进行了对比分析。 Jul 21, 2023 · Depends on gpu model, electrical pci-e slots and cpu, I think. 6. I'll stick with exllama for my use case. So, using GGML models and the llama_hf loader, I have been able to achieve higher context. So my P40 is only using about 70W while generating responses, its not limited in any way(IE. 3~0. Exllama loaders do not work due to dependency on FP16 instructions. Is there a anyway to do it faster? (flags --sdp-attention --rwkv-cuda-on --no_use_cuda_fp16 --xformers) I can't remember the exact reason, but something about P100 was bad/unusable for llama. I think some "out of the box" 4k models would work but I haven't tried them and there aren't many suitable for RP. com/r/LocalLLaMA/comments/13n8bqh/my Quad P40 runs Open WebUI and Ollama locally. gguf only the rtx3090 (GPU 0) and the CPU. But . cpp that is all FP32 and I can run Q5KM and Q6 quants on it. Nov 28, 2023 · Progress on the rewrite for older cards (Like the P40) #279 opened Sep 8, 2023 by TimyIsCool. It's the most capable local model I've used, and is about 41. config = ExLlamaConfig(model_config_path) config. P40s basically can't run this. Tesla P40 performance is still very low, only using 80W underload. com（码云） 是 OSCHINA. Python 957 114 turboderp-org/ exui turboderp-org/exui Public. fpfzxhhvuggsoqehcgmqbfvmutaltbfrqdjdbyvwnawsxdfmaqtxt