Tesla p40 exllama reddit Unanswered. 3. So the end result would remain Disclaimer: I'm just a hobbyist but here's my two cents. I've ran LLaMA 2 on 64GB RAM and a GTX 1050 Ti. It's hard to say what's "better" because the kernels can be rewritten to support FP32 ops, but for some reason a bunch of these devs gave up on it. Around 1. Still, the only better used option than P40 is the 3090 and it's quite a step up in price. P100s will work with exllama. 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. Safetensors are just a packaging format for weights, because the original way to distribute weights depended on the inclusion of arbitrary Python code, which is kind of a major security risk. The P40 offers slightly more VRAM (24gb vs 16gb), but is GDDR5 vs HBM2 in the P100, meaning it has far lower bandwidth, use the following search parameters to narrow your results: subreddit:subreddit find submissions in "subreddit" author:username find submissions by "username" All you should need to do is set up IOMMU and download the correct drivers, see the docs and PCI/ (e) wiki from my post above. unfortunately 1024batch size goes slightly over 24gb, and 16k ctx is too big as well. 2) only on the P40 and I got around Just wanted to share that I've finally gotten reliable, repeatable "higher context" conversations to work with the P40. 32G of memory will be limiting. ##### Welp I got myself a Tesla P40 from ebay and got it working today. I'm successfully running it in KoboldCPP on my P40. I've been running a P40 in a 4x slot and while it's probably slower I can't say it's noticeably slower. 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). Best bet is using llama. If any devs are interested let me know. cpp or koboldcpp. cpp/llamacpp_HF, set n_ctx to 4096. I can run the 70b 3bit models at around 4 t/s. 6-mixtral-8x7b. on model "TheBloke/Llama-2-13B-chat-GGUF**" "llama-2-13b-chat. I would suggest to either get an exl2 quant of some smaller model (7B?) and load that into VRAM or if you want to use 30B+ models, get a GGUF quant and run it with CPU using llamacpp. Got myself an old Tesla P40 Datacenter-GPU (GP102 like GTX1080-silicon but with 24GB ECC vram, 2016) for 200€ from ebay. The journey was marked by 4090/3090 here, biggest challange was finding a way to fit them together haha, but after going through like 3 3090 including a blower one (CEX UK return policy lol) i found a evga ftw x3 ultra that is small enough to pair with my 4090 in a x8/x8, also had them on another mb and 3090 was in the pci-e 4 /x4 slot and didnt notice much of a slowdown, I'd guess 3090/3090 is same. An example is SuperHOT 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. P40s you'll be stuck with llama. Only problem is that it has no video output or fan, it's meant for a server which I have, but that's the main problem. 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 just really hate using NVidia. If they could get ExLlama optimized for the P40 and get even 1/3 of the speeds they're getting out of the newer hardware, I'd go the P40 route without a second thought. SuperHOT for example relies upon Exllama for proper support of the extended context. **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. 5 GB and fits fully into shared VRAM. The Tesla M40 and M60 are both based on Maxwell, but the Tesla P40 is based on Pascal. I would love to run a bigger context size without sacrificing the split mode = 2 performance boost. Or check it out in the app stores Can I share the actual vram usage of a huge 65b model across several P40 24gb cards? Can I have those several cards all sharing the processing, so the actual speed is more similar to one much more expensive card? main: seed = 1686595827 ggml_init_cublas: Tesla P40 (Size reference) Tesla P40 (Original) In my quest to optimize the performance of my Tesla P40 GPU, I ventured into the realm of cooling solutions, transitioning from passive to active cooling. Internet Culture (Viral) Amazing Check the TGI version and make sure it’s using the exllama kernels introduced in v0. And GGUF Q4/Q5 makes it quite incoherent. I've been playing in runpod to get an idea how multi smaller cards scale vs one larger card so I have something to compare the P40's to and then I can decide to stick or twist on the P40's. Possibly slightly slower than a 1080 Ti due to ECC memory. I don't want ANYONE to buy a P40 for over 180$ (They are The Tesla P40 and P100 are both within my prince range. Super slow. I split models between a 24GB P40, a 12GB 3080ti, and a Xeon Gold 6148 (96GB system ram). 1. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will So I'm relatively new to this and have a couple of P40's on the way from Ebay. went with 12,12 and that was horrible. I did buy a second card to test this out to compare to my 3090 + P40 setup. auto_gptq and gptq_for_llama can be specified to use fp32 vs fp16 calculations, but this also means you'll be hurting performance drastically on the 3090 cards (given there's no way to indicate using one or the other by individual card within existing Note the latest versions of llama. Search on EBay for Tesla p40 cards, they sell for about €200 used. My current setup in the Tower 3620 includes an NVIDIA RTX 2060 Super, and I'm exploring the feasibility of upgrading to a Tesla P40 for more intensive AI and deep learning tasks. 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. And you probably won’t even be that happy with it. cpp now have decent GPU support and has both a memory tester and lets you load partial models (n-layers) into your GPU. Valheim; Genshin Impact; Decrease cold-start speed on inference (llama. There might be something like that you can do for loaders that are using CUBLAS also but i'm not sure how to do that. Make sure to also set Truncate the prompt up to this length to 4096 under Parameters. I can go up to 12-14k context size until vram is completely filled, the speed will go down to about 25-30 tokens per second. 179K subscribers in the LocalLLaMA community. I loaded my model (mistralai/Mistral-7B-v0. Gaming Using a Tesla P40 I noticed that when using llama. Get the Reddit app Scan this QR code to download the app now. You will have to stick with gguf models. But in the end, the models that use this are the 2 AWQ ones and the load_in_4bit one, which did not make it into the VRAM vs perplexity frontier. cpp, koboldcpp, ExLlama, etc. 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. 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. Usually on the lower side. 53-x64v3-xanmod1 system: "Linux Mint 21. This library sounds like a benefit for my humble hardware. When I first tried my P40 I still had an install of Ooga with a newer bitsandbyes. For immediate help and problem solving, please join us at Tesla P40 performance is still very low, only using 80W underload. Works fine for me. I got a P100 to load the low 100s and have exllama work. GGUF edging everyone out with it's P40 support, good performance at the high end, and also CPU inference for the low end. I wonder what speeds someone would get with something like a 3090 + p40 setup. I would really like to see benchmarks with more realistic items users might have. The difference in performance for GPUs running on x8 vs x16 is fairly small even with the latest cards. Motherboard: Asus Prime x570 Pro Processor: Ryzen 3900x System: Proxmox Virtual Environment Virtual Machine: Running LLMs Server: Ubuntu Software: Oobabooga's text-generation-webui 📊 Performance Metrics by Model Size: 13B GGUF Model: Tokens per Second: Around 20 For multi-gpu models llama. Mind that it uses an older architecture and not everything might work of require fiddling. Any Pascal card except the P100 will run badly on exllama/exllamav2. cpp and llama-cpp-python with CUBLAS support and it will split between the GPU and CPU. Just a mild bump. 24GB 3090/4090 + 16GB Tesla P100 = 70B (almost)? #203. Both GPTQ and exl2 are GPU only formats meaning inference cannot be split with the CPU and the model must fit entirely in VRAM. It might have been that it was CUDA 6. If you want more than one P40 though you should probably look at better CPUs for the passthrough. A place to discuss the SillyTavern fork of TavernAI. You can get a p40 for cheap, but then you’ll spend a bunch of money trying to get it powered and cooled. cpp when it came to the processing before generation. - Jupyter notebook: how to use it it still needs loras & more parameters, i will add that when i'll have some time. 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). 4 makes it x10 slower (from 17sec to 170sec to generate synthetic data of a hospital discharge note) Pascal cards will perform poorly with exllama because fp16 performance is slow on that generation (I have a P40) and exllama only does fp16. This means you cannot use GPTQ on P40. 4. after installing exllama, it still says to install it for me, but it works. I put 12,6 on the gpu-split box and the average tokens/s is 17 with 13b models. Most people here don't need RTX 4090s. Total system cost with 2KW PSU, was around £2500. I was considering making a LLM from scratch on a pair of Tesla M40 24GB cards I have sitting around. Closed TimyIsCool opened this issue Jun 19, 2023 · 15 comments Closed Tesla P40 only using 70W underload #75. Hello, for every person looking for the use of Exllama with Langchain & the ability to stream & more , here it is : - ExllamaV2 LLM: the LLM itself. cpp offloaded and then it won't sequentially use the GPUs and give you more than 2t/s. Small caveat: This requires the context to be present on both GPUs (AFAIK, please correct me if this not mlc-llm doesn't support multiple cards so that is not an option for me. ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can be quantised to fractional bits per weight. it will let you stand up a model on your cards. 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. 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. Training and fine-tuning tasks would be a different story, P40 is too old for some of the fancy features, some toolkits and frameworks don't support it at all, and those that might run on it, will likely run significantly slower on P40 with only f32 math, than on other cards with good f16 performance or lots of tensor cores. Have you tried GGML with CUDA acceleration? You can compile llama. It's so dramatic that running a 3. Reply reply Fuzzytech • I've been fighting Tesla; Crypto. Falcon 180B GPTQ Model on Multi-GPU Setup with RunPod I avoided d/l the GPTQ model because exllama doesn't support it (or P40) and multi GPU for autoGPTQ is absolute shit. Again this is inferencing. Q4_0 quant, 12288 ctx, 512 batch size. Tiny PSA about Nvidia Tesla P40 . I tried to get an AMD v620 working under linux but AMD will not release the drivers to anyone not named Amazon or Google apparently. 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 Hi all, I got ahold of a used P40 and have it installed in my r720 for machine-learning purposes. And whether ExLlama or Llama. gguf"** The performance degrade as soon as the GPU overheat up to 6 tokens/sec, and temperature increase up to 95C. Maybe 6 with full context. It was $200. The main thing to know about the P40 is that its FP16 performance suuuucks, even compared to similar boards like the P100. This community is for the FPV pilots on Reddit. You need exllama's 8-bit cache and 3-4bpw for all that context. The K80 is a generation behind that, as I understand it, and is mega at risk of not working, which is why you can find K80's with 24GB VRAM (2x12) for $100 on ebay. Trouble getting Tesla P40 working in Windows Server 2016. 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. I'm not sure what that means exactly but my impression is that it is exactly a 'rope' 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. I did a quick test with 1 active P40 running dolphin-2. TimyIsCool opened this issue Jun 19, 2023 · 15 comments Tesla P40 is a Pascal architecture card with the full die enabled. The Outputs in exllama2 are really different compared to exllama1. P40s can't use these. I just feel im overlooking something here =) The latest model for RP usage, that i've found, namely TheBloke_Xwin-MLewd-13B-v0. For $150 you can't complain too much and that perf scales all the way to falcon sizes. Now I’m debating yanking out four P40 from the Dells or four P100s. Exllama doesn't work, but other implementations like AutoGPTQ support this setup just fine. They are made to go in server racks, and they don’t even come with fans because of that. 2 Vict Tesla P40 only using 70W underload #75. Gaming. I'll pass :) I have 3090 + 3x P40, and like it quite well. It should be fine in exllama and llama. Around $180 on ebay. You will need a fan adapter for cooling and an adapter for the power plug. For llama. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. 0 where P40 is 6. i'm pretty sure thats just a hardcoded message. Question | Help Has anybody tried an M40, and if so, what are the speeds, especially compared to the P40? Same vram for half the price sounds like a great bargain, but it would be great if anybody here with an M40 could benchmark speeds. It's obviously a work in progress but it's a fantastic project and wicked fast 👍 Because the user-oriented side is straight python is much easier to script and you can just read the code to understand what's going on. This issue caused some people to opportunistically claim that the webui is "bloated", "adds an overhead", and Get the Reddit app Scan this QR code to download the app now. Ask the community and try to help others with their problems as 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. Especially since you have a near identical setup to me. (Code 10) Insufficient system resources exist to complete the API . It has FP16 support, but only in like 1 out of every 64 cores. cpp is very capable but there are benefits to the Exllama / EXL2 combination. Help Hi all, I made the mistake us jumping the gun on a Tesla P40 and not really doing the research in terms of drivers prior to buying it. Log In / Sign Up; Advertise on Reddit; Shop Collectible Avatars; Get the Reddit The Upgrade: Leveled up to 128GB RAM and two Tesla P40's. a girl standing on a mountain Upvote for exllama. You may try instruct setting in UI as they work better with some models for Q&A's. offloaded 29/33 layers to GPU I think ExLlama (and ExLlamaV2) is great and EXL2's ability to quantize to arbitrary bpw, and its incredibly fast prefill processing I think generally makes it the best real-world choice for modern consumer GPUs, however, from testing on my workstations (5950X CPU and 3090/4090 GPUs) llama. Server recommendations for 4x tesla p40's . Actually it is also very easy to do in current exllama, I already implemented it and it works very well without the filters, but with the filters there is currently no "going back" if you want to change a generated token. I know 4090 doesn't have any more vram over 3090, but in terms of tensor compute according to the specs 3090 has 142 tflops at fp16 while 4090 has 660 tflops at fp8. But the P40 sits at 9 Watts unloaded and unfortunately 56W loaded but idle. There are multiple frameworks (Transformers, llama. 2-GPTQ, works really well - and has some residual coding knowledg as well. People seem to consider them both as about equal for the price / performance. not getting a super huge jump with the bigger models yet. Llama-2 has 4096 context length. I’ve decided to try a 4 GPU capable rig. ASUS ESC4000 G3. Everything else is on 4090 under Exllama. 1 again I can't remember, but that was important for some reason. Open menu Open navigation Go to Reddit Home. Reply Get the Reddit app Scan this QR code to download the app now. Or check it out in the app stores the exllama's were far surpassing llama. Choose the r720 due to explicit P40 mobo support in the Dell manual plus ample cooling (and noise!) from r720 fans. cpp, and Stable Diffusion. 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. The Real Housewives of Atlanta; The Bachelor; Sister Wives; and 25-30 with 13b with Exllama. 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 is a sub Reddit for people seeking a Spot Me boost on Chime. 4bpw model at Q6 seems more coherent than 4bpw at Q4. Currently exllama is the only option I have found that does. I didn't try to see what is missing from just commenting the warning out, but I will. I graduated from dual M40 to mostly Dual P100 or P40. GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. the best two budget options are still Tesla P40 and Geforce 3090. Internet Culture (Viral) A 3x Tesla P40 setup still streams at reading speed running 120b models. How to set up CodeLlama on Exllama I've been trying to set up various extended context models on Get the Reddit app Scan this QR code to download the app now. Internet Culture (Viral) Amazing leagues ahead of older versions, especially on an actually supported card like a MI100 on linux. So Exllama performance is terrible. FYI it's also possible to unblock the full 8GB on the P4 and Overclock it to run at 1500Mhz instead of the stock 800Mhz How much faster would adding a tesla P40 be? I don't have any nvidia cards. 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. You can run SDXL on the P40 and expect about 2. cpp in a while, so it may be different now. cpp are ahead on the technical level depends what sort of use case you're considering. It will still be FP16 only so it will likely run like exllama. The P40 is sluggish with Hires-Fix and Upscaling but it does Get the Reddit app Scan this QR code to download the app now. As openai API gets pretty expensive with all the inference tricks needed, I'm looking for a good local alternative for most of inference, saving gpt4 just for polishing final results. Subreddit to discuss about Llama, the large language model created by Meta AI. gguf at an average of 4 tokens a second. Probably still going to just stick with P40's for $150 each giving 24 GB of VRAM. With the tesla cards the biggest problem is that they require Above 4G decoding. Question | Help As in the title is it worth the upgrade, I’m just looking for a performance boost Would it be useful to offer one of the A100 boards for any dev teams ( ie exllama, llama cpp, etc )? I was going to possibly suggest a three month rotation, passing it onto the next dev team after times up. V interesting post! Have R720+1xP40 currently, but parts for an identical config to yours are in the mail; should end up like this: R720 (2xE-2670,192gb ram) 2x P40 2x P4 1100w psu I'm developing AI assistant for fiction writer. I have a rtx 4070 and gtx 1060 (6 gb) working together without problems with exllama. Even the MI100 having 32 GB for $900, used 3090's for $700, or new 7900 XTX's for $900, I just don't see the multiples of benefit for using those cards over a P40. for models that i can fit into VRAM all the way (33B models with a 3090) i set the layers to 600. As a result, inferencing is slow. 77 votes, 56 comments. r/LocalLLaMA A chip A close button. On llama. cpp since it doesn't work on exllama at reasonable speeds. Reply reply Recent-Nectarine2540 • yes it works with exllama -- using a 4090 internal, 3090 external in a razer core x Reddit iOS Reddit Android Reddit Premium About Reddit Advertise Blog Careers Press. If you have a spare pcie slot that is at least 8x lanes and your system natively supports Resizable Bar ( ≥ Zen2/Intel 10th gen ) then the most cost effective route would be to get a Tesla p40 on eBay for around $170. This device cannot start. Who knows. I use a Tesla m40 (older slower, 24 GB vram too) for Rendering and ai models. I'm seeking some expert advice on hardware compatibility. to be clear, all i needed to do to install was git clone exllama into repositories and restart the app. From the look of it, P40's PCB board layout looks exactly like 1070/1080/Titan X and Titan Xp I'm pretty sure I've heard the pcb of the P40 and titan cards are the same. Coolers for Tesla P40 cards Discussion Are there any GTX or Quadro cards with coolers i can transplant onto the Tesla P40 with no or minimal modification, I'm wondering if Maxwell coolers like the 980TI would work if i cut a hole for the power connector. This means only very small models can be run on P40. I am still running a 10 series GPU on my main workstation, they are still relevant in the gaming world and cheap. cpp, exllama) Question | Help I have an application that requires < 200ms total inference time. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. cpp was probably at least 10 seconds at 2048 context size. Or check it out in the app stores TOPICS. I have suffered a lot with out of memory errors and trying to stuff torch. 0 16x lanes, 4GB decoding, to locally host a 8bit 6B parameter AI chatbot as a personal project. Internet Culture (Viral) Amazing Or LoneStriker for exl2 quants for exllama v2. The "HF" version is slow as molasses. compared to YT videos I've seen it seems like the "processing" time is short but my response is slow to return, sometimes with pauses in between words. Get app Get the Reddit app Log In Log in to Reddit. Members Dear fellow redditeers I have a question re inference speeds on a headless Dell R720 (2x Xeon CPUs / 20 physical cores, 192 Gb DDR-3 RAM) running Ubuntu 22. if you input a huge prompt or a chat with a long history it can take ~10 seconds before the model starts outputting. Any process on exllama todo "Look into improving P40 performance"? env: kernel: 6. 9. 2 I think or was it 2. A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. /r/StableDiffusion is ExLlama shouldn't care either way as long as both the internal and external GPU are recognized by the NVIDIA driver. A few details about the P40: you'll have to figure out cooling. Tesla P40 users - High context is achievable with GGML models + llama_HF loader 25 votes, 24 comments. The Pascal series (P100, P40, P10 ect) is the GTX 10XX series GPUs. But 11 votes, 28 comments. The P40 is definitely my bottleneck. I just installed a Tesla P40 on my homelab, it supposedly has the performance of 1080 with its 24gb VRAM. empty_cache() everywhere to prevent memory leaks. 1 model. I get between 2-6 t/s depending on the model. Please use our Discord server instead of supporting a company that acts against its users and unpaid moderators. Q4_K_M. try to get a cheap used Tesla P40 instead. @turboderp , could you summarise the known (and unknown) parts of this issue, so that I have been researching a replacement GPU to take the budget value crown for inference. Its stupid fast compared to GPTQ and just like GGUF it supports various compression levels, from 2 to 8 bit. c++ I can achieve about ~50 tokens/s with 7B q4 gguf models. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. Then did 7,12 for my split. I'd like to spend less than ~$2k but would be willing to spend more on a better server if it allowed for upgrades in the future. We would like to show you a description here but the site won’t allow us. This post also conveniently leaves out the fact that CPU and hybrid CPU/GPU inference exists, which can run Llama-2-70B much cheaper then even the affordable 2x TESLA P40 option above. cpp For P100 you will be overpaying though and need more of them. r/LocalLLM. 4it/s at 512x768. The P40 offers slightly more VRAM (24gb vs 16gb), but is GDDR5 vs HBM2 in the P100, meaning it has far lower bandwidth, 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. Internet Culture (Viral) Amazing; Animals & Pets; Cringe & Facepalm; Funny; Interesting; Memes; Oddly Satisfying; Single Tesla P40 vs Single Quadro P1000 . 0bpw Get the Reddit app Scan this QR code to download the app now. This makes running 65b sound feasible. Telsa P40 - 24gb Vram, but older and crappy FP16. You should probably start with smaller models first because I use a P40 and 3080, I have used the P40 for training and generation, my 3080 can't train (low VRAM). The github repo simply mentions about the UI which uses exllama but how can I replace the huggingface transformer with this? GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. I have a Tesla m40 12GB that I tried to get working over eGPU but it only works on motherboards with Above 4G Decoding as a bios setting. 4? No idea otherwise. I could implement that in exllama but myself but might need to ask turboderp how it could be done efficiently 38 votes, 19 comments. cpp actually edges out ExLlamaV2 for inference speed (w/ a q4_0 beating out a 3. I confirm I disabled exLlama/v2 and did not check FP16. P40s basically can't run this. View community ranking In the Top Splitting layers between GPUs (the first parameter in the example above) and compute in parallel. cpp I just tell it to put all layers on GPU and pass both GPUs into Get the Reddit app Scan this QR code to download the app now. With Tesla P40 24GB, I've got 22 tokens/sec. Am in the proces of setting up a cost-effective P40 setup with a cheap refurb Dell R720 rack server w/ 2x xeon cpus w/ 10 physical cores each, 192gb ram, sata ssd and P40 gpu. Exllama has at most a 5 second delay with 4096 context length, and llama. Valheim; Genshin Impact; Now for Exllama, GPTQ and AWQ, they're pretty much just loaders for their respective model types, and that's where 'rope' comes in. Exllama does the magic for you. I have the drivers installed and the card shows up in nvidia-smi and in tensorflow. (Pre-Exllama) CPU GPU TPS 13900K 4090 100** (25 @ 30B) i9 /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will Yes! the P40's are faster and draw less power. a girl standing on a mountain I still oom around 38000 ctx on qwen2 72B when I dedicate 1 p40 to the cache with split mode 2 and tensor splitting the layers to 2 other p40's. View community ranking In the Top 5% of largest communities on Reddit. EyeDeck asked this question in Q&A. You can get these on Taobao for around $350 (plus shipping) Oh was gonna mention Xformers should work on RTX 2080s and Tesla T4s - it's a bit more involved to add Xformers in though - HF does allow SDPA directly now since Pytorch 2. Except it requires even higher compute. llama. cpp with mixtral in my experience, i. The model isn't trained on the book, superbooga creates a database for any of the text you give it, you can also give it URLs and it will essentially download the website and create the database using that information, and it queries the database whenever you ask Also, you won't be able to load a 30B model into 10GB VRAM using Exllama. Though, I haven't tried llama. I know I'm a little late but thought I'd add my input since I've done this mod on my Telsa P40. Regardless, it still looks like it may be viable, I've just discovered that (exllama's) Q6 cache seems to improve Yi 200K's long context performance over Q4. Is ExLlama supported? I've tried to install ExLlama and use it through KoboldAI but it doesn't seem to P40 and A100 are enterprise hardware. However it's likely more stable/consistent especially at higher I can't remember the exact reason, but something about P100 was bad/unusable for llama. gguf. Log In / Sign Up; Advertise on Reddit; Shop Collectible Avatars; Get the Reddit app Scan this QR code to download the app now. It's a pretty good combination, the P40 can generate 512x512 images in about 5 seconds, the 3080 is about 10x faster, I imagine the 3060 will see a similar improvement in generation. 2 and cuda117, but updating to 0. cpp beats exllama on my machine and can use the P40 on Q6 models. because tesla's game performance would be shit. My daily driver is a RX 7900XTX in my pc. So a 4090 fully loaded doing nothing sits at 12 Watts, and unloaded but idle = 12W. e. From a practical perspective, this means you won't realistically be able to use exllama if you're trying to split across to a P40 card. Discussion I bought 4 p40's to try and build a (cheap) llm inference rig but the hardware i had isn't going to work out so I'm looking to buy a new server. It sort of get's slow at high contexts more than EXL2 or GPTQ does though. cpp the video card is only half loaded (judging by power consumption), but the speed of the 13B Q8 models is quite acceptable. Tesla p40 24GB i use Automatic1111 and ComfyUI and i'm not sure if my performance is the best or something is missing, so here is my results on AUtomatic1111 with these Commanline: -opt-sdp-attention --upcast-sampling --api Prompt. With my setup, intel i7, rtx 3060, linux, llama. 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 Note: Reddit is dying due to terrible leadership from CEO /u/spez. I have a 3090 and P40 and 64GB ram and can run Meta-Llama-3-70B-Instruct-Q4_K_M. The Real Housewives of Atlanta; The Bachelor; Sister Wives; using exllama you can get 160 tokens/s in 7b model and 97 tokens/s in 13b model while m2 max has only 40 tokens/s in 7b model and 24 tokens/s in 13b /r/StableDiffusion is back open after the protest . 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. Q5_K_M. exlla The new NVIDIA Tesla P100, powered by the GP100 GPU, can perform FP16 arithmetic at twice the throughput of FP32. Running solely on the P40 seems a wee bit slower, but that could also just be because it's not in a full 16x PCIe slot. One other speed thing is that sometimes context loading takes a bit for the P40 on llama. Members Online Currently I am making API calls to the huggingface llama-2 model for my project and am getting around 5t/s. I've been using the A2000 6GB cluster in runpod as it's the weakest card. Expand user menu Open settings menu. Tomorrow I'll receive the liquid cooling kit and I sould get constant results. Downsides are that it uses more ram and crashes when it runs out of memory. You would also need a cooling shroud and most likely a pcie 8 pin to cpu (EPS) power connector if your PSU doesn't have an extra. View community ranking In the Top 5% GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. I can't get Superhot models to work with the additional context because Exllama is not properly supported on p40. They are equivalent to llama. ) Tesla; Crypto. cuda. I would probably split it between a couple windows VMs running video encoding and game streaming. 00it/s for 512x512. Subreddit to discuss about locally run View community ranking In the Top 5% of largest communities on Reddit. 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. I'm running a handful of P40s. 04 LTS Desktop and which also has an Nvidia Tesla P40 card installed. But the Tesla series are not gaming cards, they are compute nodes. Tesla M40 vs P40 speed . ) Some support multiple quantization formats, others require a specific format. The GP102 (Tesla P40 and NVIDIA Titan X), GP104 (Tesla P4), and GP106 GPUs all support instructions that can perform integer dot products on 2- and4-element 8-bit vectors, with accumulation into a 32-bit integer. I saw a couple deals on used Nvidia P40's 24gb and was thinking about grabbing one to install in my R730 running proxmox. But still looking forward to results of how it compares with exllama in random, occasional and long context tasks. It inferences about 2X slower than exllama from my testing on a RTX 4090, but still about 6X faster than my CPU (Ryzen 5950X). This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API changes, which break third-party apps and moderation tools. 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): 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. The quantization of EXL2 itself is more complicated than the other formats so that could also be a factor. 224GB total, 32 cores, 4 GPUs, water cooled. compress_pos_emb is for models/loras trained with RoPE scaling. Tesla; Crypto. ExLlama and exllamav2 are inference engines. They also use a different power connector. . But when using models in Transformers or GPTQ format (I tried Exllama loaders do not work due to dependency on FP16 instructions. Can I run the Tesla P40 off the Quadro drivers and it should all work together? New to the GPU Computing game, sorry for my noob question (searching didnt help much) Share Add a Comment GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. I only need ~ 2 tokens of output and have a large high-quality dataset to fine ExLlama uses way less memory and is much faster than AutoGPTQ or GPTQ-for-Llama, running on a 3090 at least. Look into superbooga extension for oobabboga, I've given it entire books and it can answer any questions I throw at it. A batch of 2 512x768 images with R-ESRGAN 4x+ upscaling to 1024x1536 took 2:48. 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 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. If someone has the right settings I would be grateful. Crypto. 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] Hi reader, I have been learning how to run a LLM(Mistral 7B) with small GPU but unfortunately failing to run one! i have tesla P-40 with me connected to VM, couldn't able to find perfect source to know how and getting stuck at middle, would appreciate your help, thanks in advance You'll get somewhere between 8-10t/s splitting it. Members Online. 250w power consumption, no video output. I personally run voice recognition and voice generation on P40. ExLlama is so blazing fast and produced coherent results, idk. Modded RTX 2080 Ti with 22GB Vram. View community ranking In the Top 10% of largest communities on Reddit. Internet Culture (Viral) switched over to EXllama and read I needed to put in a split for the cards. Internet Culture (Viral) Amazing; Animals & Pets; Cringe & Facepalm; Funny; Interesting; Memes; Oddly Writing this because although I'm running 3x Tesla P40, it takes the space of 4 PCIe slots on an older server, plus it uses 1/3 of the power. I also have a 3090 in another machine that I think I'll test against. 24 gb ram, Titan x (Pascal) Performance. Internet Culture (Viral) Amazing; Animals & Pets I ran the old version in exllama, I guess I should try it in v2 as well. Internet Culture (Viral) Amazing; Animals & Pets Is there a specific way to split the model across the two 3090s with exllama? I have nvlink enabled and working on my pair of 3090s. cpp partial offloading. On ExLlama/ExLlama_HF, set max_seq_len to 4096 (or the highest value before you run out of memory). Internet Culture (Viral) Amazing GPUs 1&2: 2x Used Tesla P40 GPUs 3&4: 2x Used Tesla P100 Motherboard: Used Gigabyte C246M-WU4 P100s can use exllama and other FP16 things. I even think I could run Falcon 180B on this, with one card worth of offload to my 7950x. done: https: Tesla P40 users - High context is achievable with GGML models + llama_HF loader I’m looking for some advice about possibly using a Tesla P40 24GB in an older dual 2011 Xeon server with 128GB of ddr3 1866mhz ecc, 4x PCIE 3. The Real Housewives of Atlanta; The Bachelor; Sister Wives; and all HF loaders are now faster, including ExLlama_HF and ExLlamav2_HF. What CPU you have? Because you will probably be offloading layers to the CPU. true. As it stands, with a P40, I can't get higher context GGML models to work. Or check it out in the app stores ML Dual Tesla P40 Rig Case recomendations comments. Cardano; Dogecoin; Algorand; Bitcoin; Litecoin; Basic Attention Token; Bitcoin Cash; Television. So Tesla P40 cards work out of the box with ooga, but they have to use an older bitsandbyes to maintain compatibility. 1 - So if I have a model loaded using 3 RTX and 1 P40, but I am not doing anything, all the power states of the RTX cards will revert back to P8 even though VRAM is maxed out. The Telsa P40 (as well as the M40) have mounting holes of 58mm x 58mm distance. I understand that it can be improved by using exllama but can't find any code samples on how to do that. So it will perform like a 1080 Ti but with more VRAM. It will have to be with llama. I have the two 1100W power supplies and the proper power cable (as far as I understand). 24GB 3090/4090 + 16GB Tesla P100 = 70B (almost)? Also I have seen one report that P100 performance is acceptable with ExLlama (unlike P40), though mixing cards from different generations can be sketchy. That's 64g of FP16 using Agreed on the transformers dynamic cache allocations being a mess. A reddit dedicated to the profession of Computer System Administration. I'm considering installing an NVIDIA Tesla P40 GPU in a Dell Precision Tower 3620 workstation. I tried it myself last week with an old board and 2 gpu but an old gtx1660 + Nvidia tesla m40 Exllama seems to be 20 tokens/s on a 4090 + 3090Ti, so 2x3090 maybe should be faster since it can be used with NVLink. Cardano Dogecoin Algorand Bitcoin Litecoin Basic Attention Token Bitcoin Cash. Uses a smidge over 22GB. In the past I've been using GPTQ (Exllama) on my main system with the The Tesla P40 and P100 are both within my prince range. It's the most capable local model I've used, and is about 41. The quants and tests were made on the great airoboros-l2-70b-gpt4-1. Terms & Policies With exllama you can go faster, and to full context, but exllama is still in early development / so no GUI or API yet: https: Its been a month, but get a tesla p40, its 24gb vram for 200 bucks, but don't sell your gpu. Isn't that almost a five-fold advantage in favour of 4090, at the 4 or 8 bit precisions typical with local LLMs? ExLlama is closer than Llama. Runs fast as this with 0. It seems to have gotten easier to manage larger models through Ollama, FastChat, ExUI, EricLLm, exllamav2 supported projects. I noticed the outputs were quite different in Get the Reddit app Scan this QR code to download the app now.
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