Exllama p40 Or as a 3rd GPU for 2xP40 to speed up Exllama heavily uses fp16 calculations and AutoGPTQ is unbearably slow compared to llama. 22x longer than ExLlamav2 to process a 3200 tokens prompt. It's so dramatic that running a 3. Tried out all the loading methods and different quants too. P100s are slightly better than P40 at SD in terms of speed but not resolution. 3 120. If you apply the peer access patch it even does direct transfers on linux. You will have to stick with gguf models. 4. Someone advise me to test compiled llama. Actually Exllama might be considerably slow because of the poor fp16 performance on the p40, so you'd likely use llama. But it does not have the integer intrinsics that llama. It should be much faster then llama. Unless you are processing a lot of data with local LLMs, it is good enough for many use cases. On llama. -cpe 2 -l 4096 (e. Again this is inferencing. Draft model: TinyLlama-1. true. But . Exllama doesn't work, but other implementations like AutoGPTQ support this setup just fine. Power delivery or temp) 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 problem is most likely that the CUDA code that I wrote has not been optimized for this use case. I just bit the bullet and got a second 3090ti (used), but you could try a tesla p40 for 200. The ExLlama tests uses the code in this PR, and the llama. cpp is the slowest, taking 2. And yea, Fantastic work! I just started using exllama and the performance is very impressive. - turboderp/exllama Give yourself the salon treatment with Como se Llama? from OPI. /main -m dolphin-2. It inferences about 2X slower than exllama from my testing on a RTX 4090, but still about 6X faster than my CPU (Ryzen 5950X). compress_pos_emb is for models/loras trained with RoPE scaling. SuperHOT for example relies upon Exllama for proper support of the extended context. 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. Except it requires even higher compute. I picked an older commit before flash-attn was baked in (72b4ab4) and ran it. Thanks to new kernels, it's optimized for (blazingly) fast inference. Depends on what you're doing. 3090 Ti and a P40, for a total of 48GB of You signed in with another tab or window. Compile would fail. And whether ExLlama or Llama. 11 votes, 28 comments. For that model, you'd launch with -cpe 4 -l 8192 (or --compress_pos_emb 4 --length 8192), possibly reducing length if you're VRAM limited and start OOMing once context has grown enough. Tesla P40 users - High 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, RTX 3090 supports FP16, whereas the p40 supports it virtually, so it's 20 times slower or slower (exllama) for the p40. Any Pascal card except the P100 will run badly on exllama/exllamav2. ). 2K loves, 172 comments, 378 shares, Facebook Watch Videos from Daddy Yankee: #Definitivamente https://youtu. 10 vs 4. md at master · turboderp/exllama I'll pass :) I have 3090 + 3x P40, and like it quite well. cpp is very capable but there are benefits to the Exllama / EXL2 combination. By the hard work of kingbri, Splice86 and turboderp, we have a new API loader for LLMs using the exllamav2 loader! This is on a very alpha state, so if you want to test it may be subject to change and such. The undocumented NvAPI function is called for this purpose. On the other hand, 2x P40 can load a 70B q4 model with borderline bearable speed, while a 4060Ti + partial offload would be very slow. gguf -n 1024 -ngl 100 --prompt "create a christmas poem with 1000 words" -c 4096. Reply reply Tesla P40 users - High context is achievable with GGML models + llama_HF loader I am thinking of buying Tesla P40 since it's cheapest 24gb vram solution with more or less modern chip for mixtral-8x7b, what speed will I get and what quantization? Also I am worried about context. KoboldCPP uses GGML files, it runs on your CPU using RAM -- much slower, but getting enough RAM is much cheaper than getting enough VRAM to hold big models. P40s can't use these. 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 On paper with a single P40 you should be able to run this quantized version of Mixtral with 20gb VRAM dolphin-mixtral:8x7b-v2. The easiest way I've found to get good performance is to use llama. You should probably start with smaller models first because the P40 is a very slow card compared to modern cards. Q6_K. Bits and Bytes however is compiled out of the box to use some instructions that only work for Ampere or newer cards even though they do not need to be. De-quantizing the weights on the fly is cheap compared to the memory access and should pipeline just fine, with the CUDA cores I have a Tesla p40 card. cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. sh). But it should still be faster than llama. Also getting slow TGI GPTQ speed on 4bit 128g quants. View full answer . (16GB (or 12GB, try to avoid those ones)) work with exllama, whereas all of the other Pascal cards do not. 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. cpp that is all FP32 and I can run Q5KM and Q6 quants on it. This means you cannot use GPTQ on P40. cpp, and ExLlamaV2. I had to go with quantized versions event though they get a bit slow on the inference time. cpp tests use the code in this PR. mlc-llm doesn't support multiple cards so that is not an option for me. cpp when it came to the processing before generation. Use this flag CUDA_VISIBLE_DEVICES=x to choose devices Is Maxime Labonne - ExLlamaV2: The Fastest Library to Run LLMs I keep seeing P40’s do these work okay out of the box with llama. 2 to meet cuda12. 3090s are hitting over 18. env file if using docker compose, or the EXL2 is the fastest, followed by GPTQ through ExLlama v1. MLC uses group quantization, which is the same algorithm as llama. Does it work with cheap cards like P40 or AMD? They're not selling themselves. This approach works on both Linux and Windows. cpp to plugging into PyTorch/Transformers the way that AutoGPTQ and GPTQ-for-LLaMa do, but it's still primarily fast because it doesn't do that. Seems like he's thinking about a 48GB config which is a popular high end VRAM break point. cpp with all the layers offloaded to the P40, which does all of its calculations in FP32. 4090: 33. 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. cpp, or P100 and exllama, and you're locked in. x Skip to content Supports multiple text generation backends in one UI/API, including Transformers, llama. P40/4090 mix will cut the t/s down to like 8 on empty context. M40 seems that the author did not update the kernel compatible with it, I also asked for help under the ExLlama2 author yesterday, I do not know whether the author to fix this compatibility problem, M40 and 980ti with the same architecture core computing power 5. Still, the only better used option than P40 is the 3090 and it's quite a step up in price. They were introduced with compute=6. Easy money Share Add a Comment. 6 tokens/s. They are working on fixing this, Useless for old (like p40/1080Ti) GPU. with exllama (15-20 t/s). 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. I think it would be more productive to try to identify the bottlenecks in other (Q) I agree, if properly priced they would be a much better option compared to a P40. AutoGPTQ runs fine regardless of exllama kernel disabled or not; ExLlama runs fine ExLlama 2 runs fine and also doesn't output the warning about flash-attn 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. SL-Stone opened this issue Dec 24, 2023 · 5 comments Closed 2 tasks done OP's tool is really only useful for older nvidia cards like the P40 where when a model is loaded into VRAM, the P40 always stays at "P0", the high power state that consumes 50-70W even when it's not actually in use (as opposed to Number 1: Don't use GPTQ with exllamav2, IIRC it will actually be slower then if you used GPTQ with exllama (v1) And yes, there is definitely a difference in speed even when fully offloaded, sometimes it's more then twice as slow as exllamav2 for me. (p40, p100, etc. cpp in a while, so it may be different now. With regular exllama you can't change as many generation settings, this is why the quality was worse. Downsides are that it uses more ram and crashes when it runs out of memory. The P40 was fun and it was like playing with a little piece of history but I've moved beyond it quite quickly. 1) so hopefully it also solves it for @ilikenwf!. My understanding is that turboderp would like to have exllama running on p40 efficiently in particular for example. env file if using docker compose, or the Now I’m debating yanking out four P40 from the Dells or four P100s. An example is SuperHOT Within SillyTavern using oobabooga's api and any model loaded with exllama, the full allowed response length is used up within the context no matter how many actual tokens are used. It is designed to improve performance compared to its predecessor, offering a cleaner and more versatile codebase. De-quantizing the weights on the fly is cheap compared to the memory access and should pipeline just fine, with the CUDA cores It achieves about a third of the speed of ExLlama, but also running on models that take up three times as much VRAM. set_auto_map("10,24") Which return the following error: Exception ha Another issue is that GPTQ on ExLlama is limited to 4 bit quants, as soon as we consider what happens if the user wants to go either side of that then GPTQ is just not going to be present. Check the TGI version and make sure it’s using the exllama kernels introduced in v0. An OAI compatible exllamav2 API that's both lightweight and fast - theroyallab/tabbyAPI I found it, but when I was just looking for P40 it showed me GPUs with lower amount of memory. Actually, I have a P40, a 6700XT, and a An OAI compatible exllamav2 API that's both lightweight and fast - tabbyAPI/main. The P40 offers slightly more VRAM (24gb vs 16gb), but is GDDR5 vs HBM2 in the P100, meaning it has far lower bandwidth, which I believe is important for inferencing. I think I saw someone say that I have to stick with GGUF and can't run exllama on a p40 due to poor FP16 calcs. 11) while being Tesla P40 users - OpenHermes 2 Mistral 7B might be the sweet spot RP model with extra context. I'm wondering if it makes sense to have nvidia-pstate directly in llama. 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. So, using GGML models and the yes, I use an m40, p40 would be better, for inference its fine, get a fan and shroud off ebay for cooling, and it'll stay cooler plus you can run 24/7, don't pan on finetuning though. 100K views, 3. after installing exllama, it still says to install it for me, but it works. 4 60. Make sure to also set Truncate the prompt up to this length to 4096 under Parameters. I haven't merged them yet but they will be in the 1. My takeaway was, P40 and llama. A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. On ExLlama/ExLlama_HF, set max_seq_len to 4096 (or the highest value before you run out of memory). Thing is I´d like to run the bigger models, so I´d need at least 2, if not 3 or 4, 24 GB cards. Exllama, from its inception, has been made for users with 1-2 commercial graphics cards, lacking in batching and the ability to compute in parallel. yml file) is changed to this non-root user in the container entrypoint (entrypoint. X16 is faster then X8 and x4 douse not work with p40. I loaded my model (mistralai/Mistral-7B-v0. 37 tokens/sec; I'm puzzled by some of the benchmarks in the README. cpp are ahead on the technical level depends what sort of I top out at almost 9 for the P40 with low context and around 5-6 with something like 2k. 3 seconds (IMPORT FAILED): D: Just use a loader that supports it like llama. I'm not sure about the too old part, since this can maybe be mitigated, but with the current prices the P40 is the better choice. So if the limit is 400 tokens and the reply is "Hi" 400 tokens are still used. Navigation Menu They claim to be as fast or faster than exllama on NVIDIA GPU, and they also claim to have equivalent speed using ROCm on AMD GPU. Maybe someone else can comment. Reply reply kurwaspierdalajkurwa Got myself an old Tesla P40 Datacenter-GPU (GP102 like GTX1080-silicon but with 24GB ECC vram, 2016) for 200€ from ebay. Discussion but only in like 1 out of every 64 cores. Here's a The Tesla P40 and P100 are both within my prince range. Though, I've struggled to see improved performance using things like Exllama on the P40 when Exllama has a dramatic performance increase on my 3090's. If you're using linux you can pull them all out and mix and match whenever you feel like The more VRAM the better if you'd like to run larger LLMs. Total system cost with 2KW PSU, was around £2500. Curate this topic Add this topic to your repo To associate your repository with the exllama topic, visit your repo's landing page and select "manage topics ExLlama w/ GPU Scheduling: Three-run average = 22. cpp uses for quantized inferencins. Therefore they cannot use the Exllama loaders and AWQ / EXL2 models. My P40 still seems to choke unless I use Exllama - exllama is a memory-efficient tool for executing Hugging Face transformers with the LLaMA models using quantized weights, enabling high-performance NLP tasks on modern GPUs while minimizing memory usage gppm will soon not only be able to manage multiple Tesla P40 GPUs in operation with multiple llama. For $150 you can't complain too much and that perf scales all the way to falcon sizes. Closed 2 tasks done. Expected it to slow down but I was thinking might be like 4-5 tokens. I'll build a list and run the tests and post results here. P40/P100)?. Import times for custom nodes: 0. It seems to work on my setup (also Cuda 12. The text was updated successfully, but these errors were encountered: AutoGPTQ/GPTQ have to maintain compatibility for stuff like P40, Which brings to the P40. The P100 also has dramatically higher FP16 and FP64 performance than the P40. for 33B on 24GB VRAM, which OOMs around 3400-3600 tokens anyway), but you shouldn't do that, 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. But it's still the cheapest option for LLMs with 24GB. Check out Llama. It'll also not be as fast as a 3090. ExLlamaV2 is a fast inference library that enables the running of large language models (LLMs) locally on modern consumer-grade GPUs. Reply reply More replies More replies. hi, i have a Tesla p40 card, it's slow with ollama and Mixtral 8x7b. Until I added a link from lib to lib64 it was unable to find the cuda libs. 3,855. . Everything else is on 4090 under Exllama. Contribute to Zuellni/ComfyUI-ExLlama-Nodes development by creating an account on GitHub. what's giving more performance right now a p100 running exllama2/fp16 or p40 running whatever it is it runs? also can you mix them for inferencing like you see people doing on If you have some fast GPU and you need a cheap extra 16gb for exllama or SD, yea. So Exllama performance is terrible. Hopefully more details about how it works 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. For nvlink it's faster than exllama. 4 Exllama doesn't look like it'll be supported for a while, so sadly P40 users aren't going to benefit from the lower VRAM usage, but IMO a 13B model that can make use of 8k context running at 9-10 t/s is very useable for RP at least. ExLlama can use SDPA instead of matmul attention. However, the exllama's were far surpassing llama. Yeah, I wouldn't want to sit next to it. 1 which the P40 is. Some have run it at reasonably usable speeds using three or four p40 and server hardware for less than two grand worth of parts, Hi! Recently, I've had an issue with batch inference and filled n a bug that has been resolved: #253 The solution is: model = exllama_set_max_input_length(model, 4096) but when I load a model from the Hugging Face and try to change the i A fast inference library for running LLMs locally on modern consumer-class GPUs - exllamav2/README. g. P40 is cheap for 24GB and I use it daily. TensorRT-LLM, AutoGPTQ, AutoAWQ, HQQ, and AQLM are also supported but you need to install them manually. 1. Currently exllama is the only option I have found that does. 1B-1T-OpenOrca-GPTQ. There is a flag for gptq/torch called use_cuda_fp16 = False that gives a massive speed boost -- is it possible to do Better P40 performance is somewhere on the list of priorities, but there's too much going on right now. cpp beats exllama on my machine and can use the P40 on Q6 models. Looking at the specs the 3090 is 2X the speed of the MI100 with FP32 operations but the MI100 is 4. Make sure to grab the right version, matching your platform, Python version (cp) and CUDA version. 000 context, what speed will be then? Or may be I just should upgrade to 64GB of RAM and stay on RTX 3050? ExLlama nodes for ComfyUI. What if I will have 10. 4bpw model at Q6 seems more coherent than 4bpw at Q4. For That's amazing what can do the latest version of text-generation-webui using the new loader Exllama-HF! I can load a 33B model into 16,95GB of VRAM! 21,112GB of VRAM with AutoGPTQ!20,07GB of VRAM with Exllama. Exllama 1 and 2 as far as I've seen don't have anything like that because they are much more heavily optimized for new hardware so you'll have to avoid using them for loading models. It's a very attractive card for the obvious reasons if it can be made to perform well. The Quad P100 is now running TabbyAPI with Exllama2, serving OpenAI API format. I’m leaning on towards P100s because of the insane speeds in 64G you can get, I think, some decent quants of 103b and even 120b. I have a rtx 4070 and gtx 1060 (6 gb) working together without problems with exllama. P100s can't do RVC at all. I think you're right about <48GB quants being ok for 70B. The prompt processing speeds of load_in_4bit and AutoAWQ are not P40 Como Se Llama Intelli-Gel Duo by OPI features a rich, burgundy-red shade with long-lasting wear, perfect for adding a bold touch to your manicure. llama. Some @pineking: The inference speed at least theoretically is 3-4x faster than FP16 once you're bandwidth-limited, since all that ends up mattering is how fast your GPU can read through every parameter of the model once per token. 1 i think). 1K likes, 1. to be clear, all i needed to do to install was git clone exllama into repositories and restart the app. md at master · turboderp/exllamav2 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. That is definitely a problem I avoided rather than solved. gguf only the rtx3090 (GPU 0) and the CPU. I was worried the p40 & 3090ti combo would be too slow (plus I have 4 monitors and needed the video out) but I'm getting 11. You'll also Exllama, from its inception, has been made for users with 1-2 commercial graphics cards, lacking in batching and the ability to compute in parallel. 1 and that includes the instructions required to run it. This means only very small models can be run on P40. A few details about the P40: you'll have to figure out cooling. I've just discovered that (exllama's) Q6 cache seems to improve Yi 200K's long context performance over Q4. it's faster than ollama but i can't use it for conversation. Latest bec6c9 25 t/s 34t/s thoughts? [BUG] Try using vLLM for Qwen-72B-Chat-Int4, got NameError: name 'exllama_import_exception' is not defined #856. I even think I could run Falcon 180B on this, with one card worth of offload to my 7950x. 1 model. Finally, if you'd like to see speed comparisons of P40 vs P100 then let me know what you'd like to see. This makes running 65b sound feasible. Though at that level, you don't really need exllama speeds. Test kernel stuff is also out of date as the paths are wrong. Model: TheBloke_guanaco-33B-GPTQ. I'm not sure why this is yet. (I have not found the exact point at which it starts, necessarily). 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. PCI-e x16 or x8 for the p40? I have the same problem with p40. Your CPU can do better than that using AVX, so it's not surprising you get very bad performance. I read the P40 is slower, but I'm not terribly concerned by speed of the response. Exllama: 9+ t/s, ExllamaV2 1. It sounds like a good solution. In Open WebUI there is an option for another host via 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. This may because you installed auto_gptq using a pre-build wheel on Windows, in which exllama_kernels are not compiled. P40s basically can't run this. Though, I haven't tried llama. cpp was probably at least 10 seconds at 2048 I've been fighting to get multi-GPU working all evening here. Exllama v2. 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. Glad I 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. - exllama/doc/TODO. 4bpw-h6-exl2. If the p40 performance isn't basically free I wouldn't bother, A fast inference library for running LLMs locally on modern consumer-class GPUs - Releases · turboderp/exllamav2 Alright, I ran some tests on a P40. Not sure if it will have support for P40 but then again, you have llama. For example exllama - currently the fastest library for 4bit inference - does not work on P40 because it does not have support for required operations or smth. cpp (enabled only for specific GPUs, e. I'd rather get a good reply slower than a fast less accurate one due to running 🦙 Running ExLlamaV2 for Inference. Releases are available here, with prebuilt wheels that contain the extension binaries. SDPA uses upcasting in the fused attention kernel which prevents the overflow and at least Qwen2-7B seems to be working without flash-attn. Be the first to comment Nobody's responded to this post yet. Old Nvidia P40 (Pascal 24GB) cards are easily available for $200 or less and would be easy/cheap to play. Quad P40 runs Open WebUI and Ollama locally. To use exllama_kernels to further speedup Cannot import D:\CGI\Comfy\ComfyUI\custom_nodes\ComfyUI-ExLlama-Nodes module for custom nodes: DLL load failed while importing exllamav2_ext: The specified procedure could not be found. Currently it’s got 4x p100’s and 4x p40’s in it that get a lot of use for non-llm AI, so not sure I’m willing to tinker around with half the devices even if the compute cores is better. cpp quite well, and GPTQ models through other loaders I have a P40 in a R720XD and for cooling I used attached some fans I pulled from a switch with some teflon tape on the intake side of the P40 housing and use an external 12v One has a pair of MI100s and the other has a 3090 and P40. gpu_peer_fix = True config. Model: Xwin-LM-70B-V0. You signed out in another tab or window. I've got a bug filed on that, but it's not yet clear to me whether this is an intentional dependency. Removing the heat sink and all that doesn’t scare me but why do you need to do that? I don't intend for this to be the standard or anything, just some reference code to get set up with an API (and what I have personally been using to work with exllama) Following from our conversation in the last thread, it seems like there is NOTE: by default, the service inside the docker container is run by a non-root user. Is there an existing issue for this? I have searched the existing issues Current Behavior 我目前是使用 P40 来部署 6B 和 6B-int4 模型 Llama-2 has 4096 context length. I personally run voice recognition and voice generation on P40. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. But 3090 for 30/33b models achieves 'good enough' speeds, esp. Basically, we want P40 supports Cuda 6. WARNING:Exllama kernel is not installed, reset disable_exllama to True. Exllama did not let me load some models that should fit to 28GB even if I separated it like 10GB on one and 12 GB on another despite all my attempts. I dunno if the vming makes a difference, if it's good, it shouldn't (6. The quants and tests were made on the great airoboros-l2-70b-gpt4-1. Does this mean Exllama 2 lowers memory requirements for models? I would really like to see benchmarks with more realistic items users might have. In that case totally get the P40 (or two), though be mindful it's a Pascal-era card and might become unsupported by something at some point. Using a Tesla P40 I noticed that when using llama. Reload to refresh your session. To disable this, set RUN_UID=0 in the . Add your thoughts and get the conversation going. ExLlamaV2 is a library designed to squeeze even more performance out of GPTQ. For GPTQ tests, I used models with groupsize 128 and no desc_act, which are the ones that are widely used. nvidia-pstate reduces the idle power consumption (and temperature in result) of server Pascal GPUs. The "HF" version is slow as molasses. Maybe it would be better to buy 2 P100s, it might fit in 24+32 and you'll preserve exllama support. the m/p40 series 1080 and items like 1660s 3080/90/4080/90 is unrealistic for My Tesla p40 came in today and I got right to testing, after some driver conflicts between my 3090 ti and the p40 I got the p40 working with some sketchy cooling. I wonder what speeds someone would get with something like a 3090 + p40 setup. i'm pretty sure thats just a hardcoded message. A P40 using GGUF would be fine. Exllama loaders do not work due to dependency on FP16 instructions. Skip to content. cpp the video card is only half loaded (judging by power consumption), (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. My setup is 2x3090, 1xP40 and A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. If you'd used exllama with workstation GPUs, older workstation GPUs (P100, P40) colab, AMD could you share results? (I have a P40) and exllama only does fp16. I can't get Superhot models to work with the additional context because Exllama is not properly supported on p40. he talk alone or he's talking nonsense This is running on 2x P40's, ie: . Reply More posts you may like. @turboderp , could you summarise the known (and unknown) parts of this issue, so that I'm planning to do a lot more work on support for the P40 specifically. At one point, I had 3 GPUs in my machine and the third one was on a GPU riser connected to my motherboard via a USB connector which was essentially PCIe 1x and it was generating AI images at roughly the same speed as when I had it directly plugged in to the PCIe 16x slot. cpp on a P40, not entirely sure why (probably uses fp16 under the hood somewhere I'm guessing). As a 3rd GPU for 2x3090 it's great. @pineking: The inference speed at least theoretically is 3-4x faster than FP16 once you're bandwidth-limited, since all that ends up mattering is how fast your GPU can read through every parameter of the model once per token. cpp is not off the table - on it. I did a test on the latest commit (77545c) and bec6c9 on h100 with 30b model and I can see stable performance degradation. From what I've seen 4090 achieves better t/s than 3090. If you're running 7B or 13B models, a single P100 would be fine. GPTQ/Autogptq perform much better on Pascal though. 224GB total, 32 cores, 4 GPUs, water cooled. 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. To date I have various Dell Poweredge R720 and R730 with mostly dual GPU configurations. cpp HF. Thanks! Share Add a Comment. Hence, the ownership of bind-mounted directories (/data/model and /data/exllama_sessions in the default docker-compose. 5 t/s with exllama (would be even faster if I NOTE: by default, the service inside the docker container is run by a non-root user. Auto GPTQ is slower, gobbles up VRAM and much context blows past the vram limit. I think some "out of the box" 4k models would work but I A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. 63 t/s which is only ~half of what I get with regular inference. With 70b q6_K and 7b q8_0 on 3x P40 the performance it 3. Here are some benchmarks from my initial testing today using the included benchmarking script (128 tokens, 1920 token prompt). 2 release. be/uFvgO2NAM04 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. 0bpw-h6-exl2. 9. 2) only on the P40 and I got around 12-15 tokens per second with 4bit quantization and double quant active. Just need to spend a little time on cooling/adding fans since it's a datacenter card. Transformer recognize all GPUs. Or use 3x16 for 70b in exllama and then 1 P100 for SD or TTS. 4096+. cpp or exllama. Rhind brought up good points that already brought to my attention I was making some mistakes and have been working on remedying the issues. I highly doubt it. 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): So my P40 is only using about 70W while generating responses, its not limited in any way(IE. Here is the traceback (that's what this is called, right?) i P40 Como Se Llama Intelli-Gel by OPI offers a rich, burgundy-red shade with long-lasting wear, perfect for a bold and sophisticated manicure. You switched accounts on another tab or window. 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. 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. It’s been the best density per buck I’ve found since many 4U configurations that can handle 3, Also, I started out with KoboldCpp, but moved over to ooba wtih exllama, and I think I saw the self conversation more frequently with KoboldCpp than with ooba with default settings. cpp since it doesn't work on exllama at reasonable speeds. cpp/llamacpp_HF, set n_ctx to 4096. for models that i can fit into VRAM all the way (33B models with a 3090) i set the layers to 600. 1-4. I clicked at the offer and had to pick the right one. cpp and AutoGPTQ, just make sure the whole model fits on your VRAM. cpp with "-DLLAMA_CUDA=ON -DLLAMA_CLBLAST=ON -DLLAMA_CUDA_FORCE_MMQ=ON" in order to use FP32 and acceleration on this Add a description, image, and links to the exllama topic page so that developers can more easily learn about it. (They've been updated since the linked commit, but they're still puzzling. I didn't try to see what is missing from just commenting the warning out, but I will. GGUF edging everyone out with it's P40 support, good performance at the high end, and also CPU inference for the low end. Especially since you have a near identical setup to me. Maybe exllama does this for the P40, but not the 10x0? Wikipedia has these numbers for single/double/half precision. 13 votes, 34 comments. P40s can run GGUF models through llama. Exllama is for GPTQ files, it replaces AutoGPTQ or GPTQ-for-LLaMa and runs on your graphics card using VRAM. Theoretically, this works for other My M40 24g runs ExLlama the same way, 4060ti 16g works fine under cuda12. 32G of memory will be limiting. 5-q3_K_L You would just replace “mistral” in the second command with the above. TLDR: trying to determine if six P4 vs two P40 is better for 2U form factor. 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. This happens when I try to use a context size over 2048, e. ; Automatic prompt formatting using Jinja2 templates. It will have to be with llama. It will still be FP16 only so it will likely run like exllama. Before that, we need to copy essential config files from the base_modeldirectory to the new quant directory. 4? No idea otherwise. Buy this amazing product online now to experience salon-quality results at home! ExLlama_HF uses the logits from ExLlama but replaces ExLlama's sampler with the same HF pipeline used by other implementations, so that sampling parameters are interpreted the same and more samplers are supported. 2 So one should use single precision or get only 60 GFlops. I'll stick with exllama for my use case. Tesla P40 users - High context is achievable with GGML models + llama_HF loader. 7-mixtral-8x7b. - Releases · turboderp/exllama You're not relying on the cache during training, and all of what ExLlama does to be able to produce invididual tokens quickly is largely irrelevant. Some instructions that are going around say you can use e. Reply reply yzgysjr • • Edited . 3~0. Results. If anybody has something better on P40, please share. cpp instead. Being relatively new to the scene compared to some of you, I don't know if I'm shooting myself in the foot by not by looking at building something that won't support exllama moving forward. Exllama has at most a 5 second delay with 4096 context length, and llama. Also - importing weights from llama. 5x 4090s, 13900K (takes more VRAM than a single 4090) Model: ShiningValiant-2. Someone advise me to test compiling llama. Now that our model is quantized, we want to run it to see how it performs. Strange some times works faster depending of the model. I don't expect support from Nvidia to last much longer though. You may try instruct setting in UI as they work better with some models for Q&A's. Crucially, you must also match the prebuilt I think V2 is in the works. cpp or exllama Exl? Are you limited on bitrate or other options? Why should I buy a 3090 instead I know why I should get a P40 wow so affordable. AWQ/GPTQ use a different library so it will be slow. cpp. Either way, I've been trying to use exllama with a LoRA, and it works until the following lines are added: config. A 13B llama2 model, however, does comfortably fit into VRAM of the P100 and can 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. ExLlama is closer than Llama. py at main · theroyallab/tabbyAPI ExLlama nodes for ComfyUI. I also have a 3090 in another machine that I think I'll test against. ) LLama-2 70B groupsize 32 is shown to have the lowest VRAM requirement (at 36,815 MB), but wouldn't we expect it to be the highest? The perplexity also is barely better than the corresponding quantization of LLaMA 65B (4. ; OpenAI-compatible API with Chat and Completions endpoints – see examples. This has all been changed in So, P40s have already been discussed, and despite the nice 24GB chunk of VRAM, unfortunately aren't viable with ExLlama on account of the abysmal FP16 performance. I could separate models less than 12GB As it stands, with a P40, I can't get higher context GGML models to work. 48 tokens/s Noticeably, the increase in speed is MUCH greater for the smaller model running on the 8GB card, as opposed to the 30b model running on the 24GB card. ExLlama also works, but depending on context size, it seems to me that AutoGPTQ is For multi-gpu models llama. That allows me to run text generation and Automatic1111 at the same time using one single graphic card. For all models that are larger then the RAM do not work even cud fit in VRAMs + RAM. Sort by P100s can use exllama and other FP16 things. I put 12,6 on the gpu-split box and the average tokens/s is 17 with 13b models.
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