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Tesla p40 exllama reddit.


Tesla p40 exllama reddit My understanding is that turboderp would like to have exllama running on p40 efficiently in particular for example. 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). You can pack more lower precision into it, I think, but the final op has to be done like that. Exllama loaders do not work due to dependency on FP16 instructions. Weights can be stored as fp16, it's just that this architecture has weirdly limited fp16 flops. So it will perform like a 1080 Ti but with more VRAM. However it's likely more stable/consistent especially at higher resolutions since it has more than enough vram for modern games. P40s you'll be stuck with llama. 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. 0 is 11. 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. Simultaneously, unseen, a Linux VM has booted up alongside it, with its own dedicated P40 cards for AI work. cpp. Rhind brought up good points that already brought to my attention I was making some mistakes and have been working on remedying the issues. It's definitely powerful for a production system (especially those designed to handle many similar requests) where their currenct benchmark being designed for. Their aim is to produce a cryptocurrency called Pi and an ecosystem in which to use it. so for a start, i'd suggest focusing on getting a solid processor and a good amount of ram, since these are really gonna impact your Llama model's performance. The P100 also has dramatically higher FP16 and FP64 performance than the P40. Early Pascal (P100) runs at a 2:1 FP16:FP32 ratio, which is great on paper for ExLlama. Mar 9, 2024 · I have a few numbers here for various RTX 3090 TI, RTX 3060 and Tesla P40 setups that might be of interest to some of you. 2 and cuda117, but updating to 0. 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. 0 where P40 is 6. You will have to stick with gguf models. com with the ZFS community as well. At a rate of 25-30t/s vs 15-20t/s running Q8 GGUF models. It will still be FP16 only so it will likely run like exllama. Text generation web ui is slower then using exllama v2 because of all the gradio overhead. x and 12. However they have more limited cuda support and don't support FP16 in hardware. 3 120. You need 3 P100s vs the 2 P40s. I tried it myself last week with an old board and 2 gpu but an old gtx1660 + Nvidia tesla m40 was too much for the board. As a result, inferencing is… The main thing to know about the P40 is that its FP16 performance suuuucks, even compared to similar boards like the P100. Then, in Windows, I have Windows Terminal set up to give me a linux shell by sshing to the linux machine BY DEFAULT, with ssh ids copied, such that clicking the windows terminal/shell icon gets me a Linux shell. 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. It sort of get's slow at high contexts more than EXL2 or GPTQ does though. It might have been that it was CUDA 6. That being said, you can get pretty reasonable performance out of a 200 dollar P40, if you can source those, and that gets you 24GB to play with. 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. We would like to show you a description here but the site won’t allow us. Any third party runs full boar. after installing exllama, it still says to install it for me, but it works. 77 votes, 56 comments. true. It inferences about 2X slower than exllama from my testing on a RTX 4090, but still about 6X faster than my CPU (Ryzen 5950X). Jia et al 2018 {Tencent} (2048 Tesla P40 GPUs) Tesla P40 is much much better than RTX Tesla T10-8 in normal performance. It only really swings positively in the direction of AMD when these newer Radeon and Instinct cards fall further in price. ie. I don't know if the 3090 and 4090 are that far apart Have you tried GGML with CUDA acceleration? You can compile llama. 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] May 23, 2023 · Note the latest versions of llama. 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. Most people here don't need RTX 4090s. One P40, two P4's in a Dell Poweredge R720 with 128 gigs of RAM and 2TB's of storage (2 Crucial MX550s). Tesla P40 is a Pascal architecture card with the full die enabled. 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. Runs fast as this with 0. But still looking forward to results of how it compares with exllama in random, occasional and long context tasks. it took only 9. 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 More generally, for inference, the amount that has to be transferred depends on the model and is the number of values (e. There's a third option: this is Pascal, and therefore should compute using fp32, not fp16, internally. 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. Nicolas Kokkalis and his wife, Dr. cpp the video card is only half loaded (judging by power consumption), but the speed of the 13B Q8 models is quite acceptable. Auto GPTQ is slower, gobbles up VRAM and much context blows past the vram limit. It has FP16 support, but only in like 1 out of every 64 cores. 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. cpp partial offloading. Its been a month, but get a tesla p40, its 24gb vram for 200 bucks, but don't sell your gpu. 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. Please use our Discord server instead of supporting a company that acts against its users and unpaid moderators. Some BIOS only have the "Above 4G decoding" option and Resizable BAR is enabled automatically when its selected. 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. As a reminder, exllamav2 added mirostat, tfs and min-p recently, so if you used those on exllama_hf/exllamav2_hf on ooba, these loaders are not needed anymore. But the P40 sits at 9 Watts unloaded and unfortunately 56W loaded but idle. cpp It should be still higher. Enjoy! Reply reply Tesla P40 is much much better than RTX Tesla T10-8 in normal performance. INFO:Loaded the model in 104. A few details about the P40: you'll have to figure out cooling. Loading: Much slower than GPTQ, not much speed up on 2nd load. **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. 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. Again this is inferencing. For example, if I get 120FPS in a game with Tesla P40, then I get something like 70FPS is RTX T10-8. In this case however you need to make sure your system can support it properly. While using the standard fp16 version, both platforms perform fairly comparably. I use a P40 and 3080, I have used the P40 for training and generation, my 3080 can't train (low VRAM). 58 seconds. I'm unclear of how both CPU and GPU could be saturated at the same time. ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can be quantised to fractional bits per weight. 4 and the minimum version of CUDA for Torch 2. Got myself an old Tesla P40 Datacenter-GPU (GP102 like GTX1080-silicon but with 24GB ECC vram, 2016) for 200€ from ebay. outputs from activation functions) at the end of the layer you make the cut at times the size of the data type. A place to discuss the SillyTavern fork of TavernAI. I have old versions of gptq-for-llam I've heard of Tesla cards not being recognized when those options are unavailable. Disclaimer: I'm just a hobbyist but here's my two cents. This means you cannot use GPTQ on P40. 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. 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 GGUF edging everyone out with it's P40 support, good performance at the high end, and also CPU inference for the low end. r P100s will work with exllama. The quants and tests were made on the great airoboros-l2-70b-gpt4-1. Or check it out in the app stores ExLlama_HF : WizardLM-1. View community ranking In the Top 10% of largest communities on Reddit ExLlama, and transformers perplexities. 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. 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. Then, I followed YakuzaSuske's suggestion and installed visual studio build tools 2022. The P40 is sluggish with Hires-Fix and Upscaling but it does Dec 1, 2024 · A comparison table of NVIDIA GPU FP16 performance, specifically compiled for ExllamaV2 users. 2 I think or was it 2. 04 on seperate SSD's. I've seen people use a Tesla p40 with varying success, but most setups are focused on using them in a standard case. Just wanted to share that I've finally gotten reliable, repeatable "higher context" conversations to work with the P40. 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. 2) only on the P40 and I got around 12-15 tokens per second with 4bit quantization and double quant active. x in Windows and passthrough works for WSL2 using those drivers. 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. That's all done in webui with its dedicated configs per model now though. I personally run voice recognition and voice generation on P40. Usually run 30B models quantized to 4 bit but I've mucked around with the 3 bit quantized Falcon-40B model (although it runs incredibly slowly atm). 1. GPUs 1&2: 2x Used Tesla P40 GPUs 3&4: 2x Used Tesla P100 Motherboard: Used Gigabyte C246M-WU4 CPU: Used Intel Xeon E-2286G 6-core (a real one, not ES/QS/etc) RAM: New 64GB DDR4 2666 Corsair Vengeance PSU: New Corsair RM1000x New SSD, mid tower, cooling, yadda yadda. The quantization of EXL2 itself is more complicated than the other formats so that could also be a factor. Getting two Nvidia Tesla P40 or P100 GPUs, along with a PCIe bifurcation card and a short riser cable and 3d-printing both a mounting solution that would place them at a standoff distance from the mobo, as well as an airduct that would funnel air from the front 140MM fan through both of them (and maybe a pull-fan at the exhaust). Now that Llama. 0, it seems that the Tesla K80s that I run Stable Diffusion on in my server are no longer usable since the latest version of CUDA that the K80 supports is 11. This means only very small models can be run on P40. Budget for graphics cards would be around 450$, 500 if i find decent prices on gpu power cables for the server. r/LocalLLM. Get the Reddit app Scan this QR code to download the app now. 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. 4 makes it x10 slower (from 17sec to 170sec to generate synthetic data of a hospital discharge note) I can't remember the exact reason, but something about P100 was bad/unusable for llama. Turing/Volta also run at a 2:1 ratio, and Ampere and Lovelace/Hopper are both just a 1:1 ratio. edit subscriptions. 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. 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. Hi, great article, big thanks. But in RTX supported games, of course RTX Tesla T10-8 is much better. llama. With the update of the Automatic WebUi to Torch 2. Memory bandwidth is the speed at which vram can communicate with cuda cores, so for example if you take 13b model in 4bit you get about 7gb of vram, then cuda cores need to process all these 7gb and output single token. Especially since you have a near identical setup to me. for storage, a ssd (even if on the smaller side) can afford you faster data retrieval. The P40 is definitely my bottleneck. Possibly because it supports int8 and that is somehow used on it using its higher CUDA 6. The new NVIDIA Tesla P100, powered by the GP100 GPU, can perform FP16 arithmetic at twice the throughput of FP32. 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. 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 loaded my model (mistralai/Mistral-7B-v0. The Tesla P40 and P100 are both within my prince range. Except it requires even higher compute. < llama-30b FP32 2nd load INFO:Loaded the model in 68. 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. The difference in performance for GPUs running on x8 vs x16 is fairly small even with the latest cards. 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 /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. 2 Victoria" cuda: cuda_11. Possibly slightly slower than a 1080 Ti due to ECC memory. All the cool stuff for image gen really needs a newer GPU unless you don't mind waiting. If you're willing to spend the time in dependency hell, which I'm lead to understand is looking less hellish every day, I'd pick the card with more memory any day. g. cpp now have decent GPU support and has both a memory tester and lets you load partial models (n-layers) into your GPU. only then it can be used as input, then 7gb for second token, 7gb for third, etc. The P40 is supported by the latest Data Center drivers for CUDA 11. I'm not sure about exact example for equivalent but I can tell some FPS examples. A tesla P4 is a pretty wonderful GPU that can get you some impressive performance if you manage to successfully put it into WDDM mode for proper boost behavior. 5 t/s with exllama (would be even faster if I had pcie 4) so you'd probably be fine with a p40. Or check it out in the app stores ML Dual Tesla P40 Rig Case recomendations comments. This was to be expected. Mind that it uses an older architecture and not everything might work of require fiddling. Exllama loaders are ineffective, basically. The problem is, even if you passthrough successfully and have a tesla P4 within the VM, you have two problems. 105K subscribers in the LocalLLaMA community. Diffusion speeds are doable with LCM and Xformers but even compared to the 2080ti it is lulz. Aug 6, 2023 · my subreddits. because tesla's game performance would be shit. 8. 04 LTS Desktop and which also has an Nvidia Tesla P40 card installed. Not sure of the difference other than a couple more cuda cores 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. Use exllama_hf as the loader with a 4-bit GPTQ model and change the generation parameters to the "Divine Intellect" preset in oobabooga's text-generation-webui. Any Pascal card except the P100 will run badly on exllama/exllamav2. cpp offloaded and then it won't sequentially use the GPUs and give you more than 2t/s. Or check it out in the app stores &nbsp; Server recommendations for 4x tesla p40's . You should be comparing speed directly running exllama v2 python script from cli - I personally mostly stay away from ui recently and keep myself entertained with interactions using cli. Still, the only better used option than P40 is the 3090 and it's quite a step up in price. Int8 is terrible on the P40. Jun 3, 2023 · tested chatbot one performance core of CPU (CPU3) is 100% (i9-13900K) other 23 cores are idle P40 is 100%. Chengdiao Fan. Hello everyone! I've been experimenting with deploying a model using two platforms: vLLM and TGI. 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. Best bet is using llama. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. However, 15 tokens per second is a bit too slow and exllama v2 should still be very comparable to llama. Optimization for Pascal graphics cards (GTX 10XX, Tesla P40) Question Using a Tesla P40 I noticed that when using llama. 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. The short answer is a lot! Using "q4_0" for the KV cache, I was able to fit Command R (35B) onto a single 24GB Tesla P40 with a context of 8192, and run with the full 131072 context size on 3x P40's. Goodevening from Europe, I have been dabbeling with my rig for the past days to get a working GPU-accelarated chatmodel. cpp officially supports GPU acceleration. Currently I have the following: an AMD 5600x, an AMD RX 5700XT, 32Gb RAM, both windows 10 and Ubuntu 22. After playing with both a p40, and a p41, my p41 was noticeably faster. 3,855. Any process on exllama todo "Look into improving P40 performance"? env: kernel: 6. cpp, or P100 and exllama, and you're locked in. Currently exllama is the only option I have found that does. 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. This table helps users select suitable GPU models. cpp is very capable but there are benefits to the Exllama / EXL2 combination. I confirm I disabled exLlama/v2 and did not check FP16. 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. 4 60. 0 16x lanes, 4GB decoding, to locally host a 8bit 6B parameter AI chatbot as a personal project. < llama-30b-4bit 2nd load 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've been running a P40 in a 4x slot and while it's probably slower I can't say it's noticeably slower. Maybe exllama does this for the P40, but not the 10x0? Wikipedia has these numbers for single/double/half precision. Reply More posts from r/LocalLLaMA I avoided d/l the GPTQ model because exllama doesn't support it (or P40) and multi GPU for autoGPTQ is absolute shit. P40s can't use these. You will receive exllama support. I can run the 70b 3bit models at around 4 t/s. E. It has to be mostly FP32 ops. For immediate help and problem solving, please join us at https://discourse. Note: Reddit is dying due to terrible leadership from CEO /u/spez. The server already has 2x E5-2680 v4's, 128gb ecc ddr4 ram, ~28tb of storage. < llama-30b FP16 2nd load INFO:Loaded the model in 39. popular-all-random-usersAskReddit-pics-funny-movies-gaming-worldnews-news-todayilearned-nottheonion-explainlikeimfive-mildlyinteresting-DIY SuperHOT for example relies upon Exllama for proper support of the extended context. Members Online So Tesla P40 cards work out of the box with ooga, but they have to use an older bitsandbyes to maintain compatibility. Single slot + no power connector makes it even better for homelab use. I would really like to see benchmarks with more realistic items users might have. Gemma 7b has 18 layers and each layer output layer size is 3k (from the huggingface config and their paper), so at 32 bit floats (4 bytes) you're looking 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. So a 4090 fully loaded doing nothing sits at 12 Watts, and unloaded but idle = 12W. Everything else is on 4090 under Exllama. for models that i can fit into VRAM all the way (33B models with a 3090) i set the layers to 600. P41 was faster than I could read. r11. 2GB VRAM out of 24GB. 4. Lm studio does not use gradio, hence it will be a bit faster. 8 nvidia-dirve 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. If you want 24GB VRAM, the best two budget options are still Tesla P40 and Geforce 3090. 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. I just bit the bullet and got a second 3090ti (used), but you could try a tesla p40 for 200. Subreddit to discuss about Llama, the large language model created by Meta AI. 4 makes it x10 slower (from 17sec to 170sec to generate synthetic data of a hospital discharge note) Getting two Nvidia Tesla P40 or P100 GPUs, along with a PCIe bifurcation card and a short riser cable and 3d-printing both a mounting solution that would place them at a standoff distance from the mobo, as well as an airduct that would funnel air from the front 140MM fan through both of them (and maybe a pull-fan at the exhaust). I have a rtx 4070 and gtx 1060 (6 gb) working together without problems with exllama. 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. 53-x64v3-xanmod1 system: "Linux Mint 21. 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. 1 - SDPA is nearly as performant as FA2, since it has FA2 and Xformers, but the memory usage can be quite bad (still better than vanilla transformers) That should mean you have a Dell branded card. 53 seconds. ) oobabooga is a full pledged web application which has both: backend running LLM and a frontend to control LLM Where are you running and getting 20 t/s? Oobabooga is eating away a lot of exllama v2 perf, about 20-30% for me. witin a budget, a machine with a decent cpu (such as intel i5 or ryzen 5) and 8-16gb of ram could do the job for you. ExLlama and exllamav2 are inference engines. I think the easiest thing for exllama would be to fork it and change all the FP16 to FP32 and see what happens. The most excellent JohannesGaessler GPU additions have been officially merged into ggerganov's game changing llama. Both GPTQ and exl2 are GPU only formats meaning inference cannot be split with the CPU and the model must fit entirely in VRAM. 3. They are equivalent to llama. cpp For P100 you will be overpaying though and need more of them. With regular exllama you can't change as many generation settings, this is why the quality was worse. cpp or koboldcpp. Discussion I saw a couple deals on used Nvidia P40's 24gb and was thinking about grabbing one to install in my R730 running proxmox. This is the Reddit community-run sub for the Pi Network cryptocurrency project started by the team of Computer scientist Dr. I would probably split it between a couple windows VMs running video encoding and game streaming. 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 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. 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. Enjoy! Reply reply 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. My takeaway was, P40 and llama. 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 the model a Tesla accelerates the world's transition to sustainable energy with electric cars, solar, and integrated renewable energy solutions for homes and businesses. If you want to run LLMs on a budget then using multiple cheaper GPUs like RTX 3090 24GB or Tesla P40 24GB is a great option. 11 votes, 28 comments. to be clear, all i needed to do to install was git clone exllama into repositories and restart the app. mlc-llm doesn't support multiple cards so that is not an option for me. 1 again I can't remember, but that was important for some reason. 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. cpp actually edges out ExLlamaV2 for inference speed (w 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. 8B worked fine, but 70B always seems to fail, giving me the following output after the measurement has succeeded. First of all I have limited experience with oobabooga, but the main differences to me are: ollama is just a REST API service, and doesn't come with any UI apart from the CLI command, so you most likely will need to find your own UI for it (open-webui, OllamaChat, ChatBox etc. very detailed pros and cons, but I would like to ask, anyone try to mix up one… I am currently trying my hands on making quants for the new Llama. I split models between a 24GB P40, a 12GB 3080ti, and a Xeon Gold 6148 (96GB system ram). In these tests, I was primarily interested in how much context a particular setup could fit into and how the speed would be. Everyone, i saw a lot of comparisons and discussions on P40 and P100. practicalzfs. 179K subscribers in the LocalLLaMA community. 1 model. < llama-30b-4bit 1st load INFO:Loaded the model in 7. 0-Uncensored-Llama2-13B-GPTQ Full GPU Thank you for your quick response. A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. P40s basically can't run this. Choose the r720 due to explicit P40 mobo support in the Dell manual plus ample cooling (and noise!) from r720 fans. I put 12,6 on the gpu-split box and the average tokens/s is 17 with 13b models. i'm pretty sure thats just a hardcoded message. So Exllama performance is terrible. I have tries ollama, exllama amd KoboldCPP (Rocm edition). . A motherboard that has multiple PCIe x8 or higher slots for all the GPUs to plug into and a large enough power supply. So now llama. To get the exllama-2 loader to work, I tried adding nvcc/g++ to my environment, by editing my system environment variables and ensuring there was a path to them. Aug 1, 2023 · Two Tesla P40s are an option, and potentially the cheapest way to 48GB. one big cost factor could be a We would like to show you a description here but the site won’t allow us. Tesla P40 performance is still very low, only using 80W underload. Its really quite simple, exllama's kernels do all calculations on half floats, Pascal gpus other than GP100 (p100) are very slow in fp16 because only a tiny fraction of the devices shaders can do fp16 (1/64th of fp32). P40 was reading speed. 24 seconds. The P40 SD speed is only a little slower than P100. Look into superbooga extension for oobabboga, I've given it entire books and it can answer any questions I throw at it. I am looking at upgrading to either the Tesla P40 or the Tesla P100. But either way, great to hear. cpp supports quantized KV cache, I wanted to see how much of a difference it makes when running some of my favorite models. But that might be one cause. I also have a 3090 in another machine that I think I'll test against. Later Pascal runs at a really awful 1: 64 ratio, meaning FP16 math is completely unviable. cpp and llama-cpp-python with CUBLAS support and it will split between the GPU and CPU. 30B parameter models run at around 4-5 tokens per second on the P40. 2 Thanks a lot for such a complete answer! I'm glad my guide was helpful to you! I need to post an updated version soon because I'm using some different tools and techniques these days, but the idea remains the same. 84 seconds. 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): 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 "P8"/idle state where only 10W of power is used). I didn't try to see what is missing from just commenting the warning out, but I will. nsjzxe pifgl srgrw becad rggten fetg oiglin vrjnq aktgnybaw ijepv