Llama cpp p40 reddit. Expand user menu Open settings menu.
- Llama cpp p40 reddit cpp officially supports GPU acceleration. I've been poking around on the fans, temp, and noise. cpp, not text-gen or something else Share Sort by: Best. Using CPU alone, I get 4 tokens/second. cpp/llamacpp_HF, set n_ctx to 4096. cpp fresh for The guy who implemented GPU offloading in llama. Here's a suggested build for a system with 4 Has anyone attempted to run Llama 3 70B unquantized on an 8xP40 rig? I'm looking to put together a build that can run Llama 3 70B in full FP16 precision. 24 GB cards. Please use our Discord server instead of supporting a company that acts against its users and unpaid moderators. cpp I am asked to set CUDA_DOCKER_ARCH accordingly. cpp that improved performance. cpp llama 70b 4bit decided to see just how this would cost for a 8x GPU system would be, 6of the GPUs will be on pcie 3. Or check it out in the app stores On Pascal cards like the Tesla P40 you need to force CUBLAS to use the older MMQ kernel instead of using the tensor kernels. 15 version increased the FFT performance in 30x. zip as a valid domain name, because Reddit is trying to make these into URLs) I've been exploring how to stream the responses from local models using the Vercel AI SDK and ModelFusion. Or check it out in the app stores I'm now seeing the opposite. I don't expect support from Nvidia to last much longer though. If you're looking for tech support, /r/Linux4Noobs and /r/linuxquestions are friendly communities that can help you. I recently bought a P40 and I plan to optimize performance for it, but I'll I saw that the Nvidia P40 arent that bad in price with a good VRAM 24GB and wondering if i could use 1 or 2 to run LLAMA 2 and increase inference times? 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. cpp and exllama. Also, Ollama provide some nice QoL features that are not in llama. 47 ms / 515 tokens ( 58. Now that speculative decoding landed yesterday I have an RTX 2080 Ti 11GB and TESLA P40 24GB in my machine. cpp has been even faster than GPTQ/AutoGPTQ. So now llama. cpp with the P100, but my understanding is I can only run llama. cpp performance: 18. Shame that some Currently I have a ryzen 5 2400g, a B450M Bazooka2 motherboard and 16GB of ram. cpp by default does not use half-precision floating point arithmetic. cpp showed that performance increase scales exponentially in number of layers offloaded to GPU, so as long as video card is faster than 1080Ti VRAM is crucial thing. They do for me, no RAM shared. But that's an upside for the P40 and similar. Botton line, today they are comparable in performance. (p40 for example) Reply reply I was wondering if adding a used tesla p40 and splitting the model across the vram using ooba booga would be faster than using ggml cpu plus gpu offloading. cpp project seems to be close to implementing a distributed (serially processed layer sub-stacks on each computer) processing capability; MPI did that in the past but was broken and is still not fixed but AFAICT there's another "RPC" based option nearing fruition. Good point about where to place the temp probe. /main -t 22 -m model. cpp , it just seems models perform slightly worse with it perplexity-wise when everything else is P40's are probably going to be faster on CUDA though, at least for now. I believe llama. cpp still has a CPU backend, so you need at least a decent CPU or it'll bottleneck. MLC-LLM's Vulkan is hilariously fast, like as fast as the llama. cpp fix) Meta version yes. or llama-cpp-python: CMAKE_ARGS="-DLLAMA_CUBLAS=ON -DLLAMA_AVX2=OFF -DLLAMA_F16C=OFF -DLLAMA_FMA=OFF" pip install llama-cpp-python. 34 ms per token, 17. Make sure to also set Truncate the prompt up to this length to 4096 under Parameters. It will have to be with llama. You can see some performance listed here. Reply reply 3090 (up to a point of course). cpp, you can run the 13B parameter model on as little as ~8gigs of VRAM. But only with the pure llama. I'm looking llama. Everywhere else, only xformers works on P40 but I had to compile it. cpp that made it much faster running on an Nvidia Tesla P40? I tried recompiling and installing llama_cpp_python myself with cublas and cuda flags in order for it to indicate to use tensor cores on the Titans but I updated to the latest commit because ooba said it uses the latest llama. Some observations: the 3090 is a beast! 28 I have 3xP40s and a 3090 in a server. I didn't even wanna try the P40s. I rebooted and compiled llama. cpp shows two cuBlas options for Windows: llama-b1428-bin-win-cublas-cu11. But 24gb of Vram is cool. A self contained distributable from Concedo that exposes llama. It's a work in progress and has limitations. 70 ms / 213 runs ( 111. But the Phi comes with 16GB ram max, while the P40 has 24GB. cpp branch, and the speed of Mixtral 8x7b is beyond insane, it's like a Christmas gift for us all (M2, 64 Gb). cpp on Tesla P40 with no problems. cpp with LLAMA_HIPBLAS=1. cpp code. cpp CUDA backend. Is commit dadbed9 from llama. You seem to be monitoring the llama. On llama. cpp GGUF models. If you have multiple P40s it's definitely your best choice. llama. cpp has continued accelerating (e. GPT 3. And there's some other formats like AWQ. cpp to do real-time descriptions of camera input /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation GGML is no longer supported by llama. (found this Paper from Dell, thought it'd help) Resources Writing this because although I'm running 3x Tesla P40, it takes the space of 4 Also, many people use llama. Not that I take issue with llama. cpp loader and with nvlink patched into the code. I mostly use it for self reflection and chatting on mental health based things. There's also the bits and bytes work by Tim Dettmers, which kind of quantizes on the fly (to 8-bit or 4-bit) and is related to QLoRA. cpp stuff itself. A probe against the exhaust could work but would require testing & tweaking the GPU Get the Reddit app Scan this QR code to download the app now. These results seem off though. Running Grok-1 Q8_0 base language model on llama. ESP32 is a series of low cost, low power system on a chip microcontrollers with integrated Wi-Fi and dual-mode Bluetooth. cpp Meanwhile on the llama. zip llama-b1428-bin-win-cublas-cu12. 2: The llama. What if we can get it to infer on P40 using INT8? exl2 processes most things in FP16, which the 1080ti, being from the Pascal era, is veryyy slow at. 0-x64. Its way more finicky to set up, but I would definitely pursue it if you are on an IGP or whatever. cpp performance: 60. For those who run multiple llama. cpp with the P40. So depending on the model, it could be comparable. cpp process to one NUMA domain (e. 73x AutoGPTQ 4bit performance on the same system: 20. Can I share the actual vram usage of a huge 65b model across several P40 24gb cards? /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will To create a computer build that chains multiple NVIDIA P40 GPUs together to train AI models like LLAMA or GPT-NeoX, you will need to consider the hardware, software, and infrastructure components of your build. The P40 offers slightly more VRAM (24gb vs 16gb), but is GDDR5 vs HBM2 in the P100, meaning it has far lower bandwidth, Super excited for the release of qwen-2. For research and Get the Reddit app Scan this QR code to download the app now. cpp release and imatrix The llama. cpp logs to decide when to switch power states. If you run llama. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will Get the Reddit app Scan this QR code to download the app now. So at best, it's the same speed as llama. I always do a fresh install of ubuntu just because. It seems to have gotten easier to manage larger models through Ollama, FastChat, ExUI, EricLLm, exllamav2 supported projects. It's a different implementation of FA. The official Python community for Reddit! Stay up to date with the latest news Using Ooga, I've loaded this model with llama. cpp really the end of the line? Will anything happen in the development of new models that run on this card? Is it possible to run F16 models in F32 at the cost of half VRAM? Previous llama. Combining this with llama. As of mlx version 0. I'd rather get a good reply slower than a fast less accurate one due to running a smaller model. 56bpw/79. cpp. cpp is your best choice for the P40s. 2. 2) only on the P40 and I got around I’ve been using llama3. 3 or 2. Note: Reddit is dying due to terrible leadership I have a Tesla p40 card. 5. Expand user menu Open settings menu. However the ability to run larger models and the recent developments to GGUF make it worth it IMO. So a 4090 fully loaded doing nothing sits at 12 Watts, and unloaded but idle = 12W. cpp because of fp16 computations, whereas the 3060 isn't. A few details about the P40: you'll have to figure out cooling. cpp, koboldcpp, exllama, etc. I understand P40's won't win any speed contests but they are hella cheap, and there's plenty of used rack servers that will fit 8 of them with all the appropriate PCIE lanes and whatnot. In llama. 39x AutoGPTQ 4bit performance on this system: 45 tokens/s 30B q4_K_S Previous llama. Valheim; Genshin Impact; You can use every quantized gguf model with llama. And it looks like the MLC has support for it. Not much different than getting any card running. 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 and the old MPI code has been removed. cpp since it doesn't work on exllama at reasonable speeds. Guess I’m in luck😁 🙏 P40 INT8 about 47 TFLOPS 3090 FP16/FP32 about 35+ TFLOPS. I typically upgrade the slot 3 to x16 capable, but reduces total slots by 1. It would invoke llama. cpp it will work. Now that it works, I can download more new format models. 78 tokens/s This lets you run the models on much smaller harder than you’d have to use for the unquantized models. cpp function bindings, allowing it to be used via a simulated Kobold API endpoint. Reply reply More replies Top 1% Rank by size Get the Reddit app Scan this QR code to download the app now. Subreddit to discuss about Llama, the large language model created by Meta AI. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers I graduated from dual M40 to mostly Dual P100 or P40. By default 32 bit floats are used. Also as far as I can tell, the 8GB Phi is about as expensive as a 24GB P40 from China. I’ve decided to try a 4 GPU capable rig. I've been on the fence about toying around with a p40 machine myself since the price point is so nice, but never really knew what the numbers on it looked like since people only ever say things like "I get 5 tokens per second!" Having had a quick look at llama. cpp for P40 and old Nvidia card with mixtral 8x7b GGUF of Llama 3 8B Instruct made with officially supported llama. 0 8x but not bad since each CPU has 40 pcie lanes, combined to I tried a bunch of stuff tonight and can't get past 10 Tok/sec on llama3-7b 😕 if that's all this has I'm sticking to my assertion that only llama. cpp loader with gguf files it is orders of magnitude faster. I think the last update was getting two P40s to do ~5 t/s on 70b q4_K_M which is an amazing feat for such old hardware. I also change LLAMA_CUDA_MMV_Y to 2. I could still run llama. I updated to the latest commit because ooba said it uses the latest llama. Memory inefficiency problems. 20B models, however, with the llama. On ExLlama/ExLlama_HF, set max_seq_len to 4096 (or the highest value before you run out of memory). 79 tokens/s New PR llama. Also, I couldn't get it to work with I have added multi GPU support for llama. It uses llama. cpp with much more complex and more heavier model: Bakllava-1 and it was immediate success. Hopefully llama. This is the first time I have tried this option, and it really works well on llama 2 models. cpp dev Johannes is seemingly on a mission to squeeze as much performance as possible out of P40 cards. cpp models are give me the llama. That's at it's best. On a 7B 8-bit model I get 20 tokens/second on my old 2070. Valheim; Genshin Impact I can handle the cooling issues with the P40 and plan to use Linux. (for example, with text-generation-webui. Open comment sort options. 4 instead of q3 or q4 like with llama. tensorcores support) and now I find llama. Anyway would be nice to find a way to use gptq with pascal gpus. Now I have a task to make the Bakllava-1 work with webGPU in browser. cpp performance: 25. Reply reply FireSilicon • I use two P40s and they run fine, you just need to use GGUF models Previous llama. 5GB RAM with mlx It is sad that this is only for fresh expensive cards, which are already fast enough, while such optimizations and accelerations are most in demand for weak/old hardware (p40 for example) Reply reply I'm just starting to play around with llama. Get the Reddit app Scan this QR code to download the app now. ASUS ESC4000 G3. 14 tokens per second) llama_print_timings: eval time = 23827. You can also make use of old/existing cards (in my case, i have a 3060 12GB and a P40 collecting dust. I bench marked the Q4 and Q8 quants on my local rig (3xP40, 1x3090). Lately llama. cpp have context quantization?”. I'm running a Tesla P40, excited to try to get this stuff working locally once it's released! [open source] I went viral on X with BakLLaVA & llama. I use it daily and it performs at excellent speeds. I'm using two Tesla P40 and get like 20 tok/s on llama. Be sure to 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. 39 ms. So yea a difference is between llama. Tweet by Tim Dettmers, author of bitsandbytes: Super excited to push this even further: - Next week: bitsandbytes 4-bit closed beta that allows you to finetune 30B/65B LLaMA models on a single 24/48 GB GPU (no degradation vs full fine-tuning in 16-bit) Just installed a recent llama. cpp instances. 94 tokens per second) llama_print_timings: total time = 54691. For AutoGPTQ it has an option named no_use_cuda_fp16 to disable using 16bit floating point kernels, and instead runs ones that use 32bit only. cpp GGUF! I have been testing running 3x Nvidia Tesla 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. I have tried running mistral 7B with MLC on my m1 metal. And for $200, it's looking pretty tasty. 7. cpp, n-gpu-layers set to max, n-ctx set to 8192 (8k context), n_batch set to 512, and - crucially - alpha_value set to 2. Note that llama. 20 was. So llama. Valheim; Genshin Impact HOW in the world is the Tesla P40 faster? What happened to llama. An example is SuperHOT Get the Reddit app Scan this QR code to download the app now. You get llama. cpp you can try playing with LLAMA_CUDA_MMV_Y (1 is default, try 2) and LLAMA_CUDA_DMMV_X (32 is default try 64). 87 ms per token, 8. cpp main branch, like automatic gpu layer + support for GGML *and* GGUF model. llama_print_timings: prompt eval time = 30047. g. cpp that made it much faster running on an Nvidia Tesla P40? I tried recompiling and installing llama_cpp_python myself with cublas and cuda flags in order I'm also seeing only fp16 and/or fp32 calculations throughout llama. You can also use 2/3/4/5/6 bit with llama. Isn't memory bandwidth the main limiting factor with inference? P40 is 347GB/s, Xeon Phi 240-352GB/s. cpp performance: 10. I literally didn't do any tinkering to get the RX580 running. cpp with "-DLLAMA_CUDA=ON -DLLAMA_CLBLAST=ON -DLLAMA_CUDA_FORCE_MMQ=ON" option in order to use FP32 and gppm will soon not only be able to manage multiple Tesla P40 GPUs in operation with multiple llama. I have tried running llama. 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. Am waiting for the python bindings to be updated. Then I cut and paste the handful of commands to install ROCm for the RX580. the steps are the same as that guide except for adding a CMAKE argument "-DLLAMA_CUDA_FORCE_MMQ=ON" since the regular Yeah, it's definitely possible to pass through graphics processing to an iGPU w/ some elbow grease (a search for "nvidia p40 gaming" will bring up videos and discussion), but there still won't be display outputs on the P40 hardware itself! Get the Reddit app Scan this QR code to download the app now. EDIT: Llama8b-4bit uses about 9. cpp and Ollama. cpp, though I think the koboldcpp fork still supports it. Best. Downsides are that it uses more ram and crashes when it runs out of memory. I was up and running. And how would a 3060 and p40 work with a 70b? EDIT: llama. Training can be performed on this models with LoRA’s as well, since we don’t need to worry about updating the network’s weights. cpp on Macs, including some of the developers, so you'll probably get better support on a Mac than on this dev kit. You can run a model across more than 1 machine. Sure, I'm mostly using AutoGPTQ still because I'm able to get it working the nicest, but I believe that llama. If so can we switch back to using Float32 for P40 users? None of the code is llama-cpp-python, it's all llama. The reason is every time people try to tweak these, they get lower benchmark scores and having tried so many hundred of models, its seldom the best rated models that are the best in real life application. For what it's worth, if you are looking at llama2 70b, you should be looking also at Mixtral-8x7b. The ESP32 series employs either a Tensilica Xtensa LX6, Xtensa LX7 or a RiscV processor, and both dual-core and single-core variations are available. 5-32B today. After that, should be relatively straight forward. According to Turboderp (the author of Exllama/Exllamav2), there is very little perplexity difference 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. 1-70b Q6 on my 3x P40 with llama. I loaded my model (mistralai/Mistral-7B-v0. I read the P40 is slower, but I'm not terribly concerned by speed of the response. For me it's just like 2. I've fit upto 34B models on a single P40 @ 4-bit. 78 tokens/s A few days ago, rgerganov's RPC code was merged into llama. 1-x64. Gaming. I don't know if it's still the same since I haven't tried koboldcpp since the start, but the way it interfaces with llama. About 65 t/s llama 8b-4bit M3 Max. cpp supports working distributed inference now. cpp loader, are too large and will spill over into system RAM. But I have not tested it yet. Matrix multiplications, which take up most of the runtime are split across all available GPUs by default. 62 tokens/s = 1. Get the Reddit app Scan this QR code to download the app now HOW in the world is the Tesla P40 faster? What happened to llama. For immediate help and To those who are starting out on the llama model with llama. What I suspect happened is it uses more FP16 now because the tokens/s on my Tesla P40 got halved along with the power consumption and memory controller load. Plus I can use q5/q6 70b split on 3 GPUs. But it's still the cheapest option for LLMs with 24GB. If you use CUDA mode on it with AutoGPTQ/GPTQ-for-llama (and use the use_cuda_fp16 = False setting) I think you'll find the P40 is capable of some really good speeds that come closer to the RTX generation. cpp and it seems to support only INT8 inference on ARM CPUs. ExLlamaV2 is kinda the hot thing for local LLMs and the P40 lacks support here. 20k tokens before OOM and was thinking “when will llama. To compile llama. 5 model level with such speed, locally upvotes · comments. It seems like more recently they might be trying to make it more general purpose, as they have added parallel request serving with continuous batching recently. Valheim; Genshin Impact Subreddit to discuss about Llama, the large language model created by Meta AI. . Exllama 1 Get the Reddit app Scan this QR code to download the app now. I've read that mlx 0. Anyone try this yet, especially for 65b? I think I heard that the p40 is so old that it slows down the 3090, but it still might be faster from ram/cpu. What I was thinking about doing though was monitoring the usage percentage that tools like nvidia-smi output to determine activity -- ie: if GPU Well done! V interesting! ‘Was just experimenting with CR+ (6. And it kept crushing (git issue with description). cpp and Ollama with the Vercel AI SDK: Personal experience. GGUF/llama. There are multiple frameworks (Transformers, llama. You'll also The Tesla P40 and P100 are both within my prince range. A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. Introducing llamacpp-for-kobold, run llama. Especially for quant forms like GGML, it seems like this should be pretty straightforward, though for GPTQ I understand we may be working with full 16 bit floating point values for some calculations. Reply reply Reddit is dying due to terrible leadership from CEO /u/spez. cpp in a relatively smooth way. Now I’m debating yanking out four P40 from the Dells or four P100s. I have a nvidia P40 24GB and a GeForce GTX 1050 Ti 4GB card, I can split a 30B model among them and it mostly works. cpp it looks like some formats have more performance optimized code than others Koboldcpp is a derivative of llama. Works great with ExLlamaV2. 14, mlx already achieved same performance 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. cpp made it run slower the longer you interacted with it. cpp with a fancy UI, persistent stories, editing tools, save formats, Llama-2 has 4096 context length. It was quite straight forward, here are two repositories with examples on how to use llama. 5g gguf), llama. zip (And let me just throw in that I really wish they hadn't opened . I plugged in the RX580. cpp with GPU you need to set LLAMA_CUBLAS flag for make/cmake as your link says. cpp beats exllama on my machine and can use the P40 on Q6 models. Or check it out in the app stores TOPICS. cpp started out intended for developers and hobbyists to run LLMs on their local system for experimental purposes, not intended to bring multi user services to production. 51 tokens/s New PR llama. The P40 is restricted to llama. But llama. cpp locally with a fancy web UI, persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and more with minimal setup We're now read-only indefinitely due to Reddit Incorporated's poor management and decisions related to third party platforms and For multi-gpu models llama. ) My research indicates that this kind of rig would draw roughly 100-150 watts during use You'll get somewhere between 8-10t/s splitting it. The most excellent JohannesGaessler GPU additions have been officially merged into ggerganov's game changing llama. So the Github build page for llama. cpp or on exllamav2. Or check it out in the app stores Using fastest recompiled llama. They should load in full. Internet Culture (Viral) Amazing have a Dell PowerEdge T630, the tower version of that server line, and I can A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. cpp with all cores across both processors your inference speed will suffer as the links between both CPUs will be saturated. But the P40 sits at 9 Watts unloaded and unfortunately 56W loaded but idle. cpp HF. It currently is limited to FP16, no quant support yet. In terms of CPU Ryzen 7000 series looks very promising, because of high frequency DDR5 and implementation of AVX-512 instruction set. Or check it out in the app stores TOPICS That's how you get the fractional bits per weight rating of 2. It rocks. I was hitting 20 t/s on 2x P40 in KoboldCpp on the 6 Cost: As low as $70 for P4 vs $150-$180 for P40 Just stumbled upon unlocking the clock speed from a prior comment on Reddit sub (The_Real_Jakartax) Below command unlocks the core clock of the P4 to 1531mhz nvidia-smi -ac 3003,1531 . cpp or other similar models, you may feel tempted to purchase a used 3090, 4090, or an Apple M2 to run these models. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. For $150 you can't complain too much and that perf scales all the way to falcon sizes. Maybe 6 with full context. Top. Cons: Most slots on server are x8. First of all, when I try to compile llama. cpp instances sharing Tesla P40 Resources gppm now supports power and performance state management with multiple llama. P100 has good FP16, but only 16gb of Vram (but it's HBM2). However, I'd like to share that there are free alternatives available for you to experiment with before investing your hard-earned money. P40 has more Vram, but sucks at FP16 operations. Gaming Try it on llama. Nvidia Tesla P40 performs amazingly well for llama. I would like to use vicuna/Alpaca/llama. compress_pos_emb is for models/loras trained with RoPE scaling. cpp wrappers for other languages so I wanted to make sure my base install & model were working properly. cpp as my daily driver. Welcome to /r/Linux! This is a community for sharing news about Linux, interesting developments and press. - Would you advise me a card (Mi25, P40, k80) to add to my current computer or a second hand configuration? - what free open source AI do you advise ? thanks Get app Get the Reddit app Log In Log in to Reddit. cpp on the other hand is capable of using an FP32 pathway when required for the older cards, that's why it's quicker on those cards. invoke with numactl --physcpubind=0 --membind=0 . 97 tokens/s = 2. cpp and max context on 5x3090 this week - found that I could only fit approx. cpp works Reply reply more replies More replies More replies More replies More replies More replies You can definitely run GPTQ on P40. cpp, koboldcpp, ExLlama With 7B and 13B models, set number of layers sent to GPU to maximum. cpp with some fixes can reach that (around 15-20 tok/s on 13B models with autogptq). However if you chose to virtualize things like I did with Proxmox, there's more to be done getting everything setup properly. Restrict each llama. I'm using Bartowski GGUF (new quant after Llama. gguf ). Someone advise me to test compiled llama. cpp has something similar to it (they call it optimized kernels? not entire sure). cpp command and I'll try it, I just use -ts option to select only the 3090's and leave the P40 I use KoboldCPP with DeepSeek Coder 33B q8 and 8k context on 2x P40 I just set their Compute Mode to compute only using: In terms of pascal-relevant optimizations for llama. Without edits, it was max 10t/s on 3090s. pgsk zrx ltaso yojwf zct gzjev zbpfk zxddupp lsh udxox