Llama cpp p40. Having had a quick look at llama.



    • ● Llama cpp p40 cpp's output to recognize tasks and on which GPU lama. 14 tokens per second) llama_print_timings: eval time = 23827. cpp afterwards then gppm doesn't detect that. i use this Contribute to paul-tian/dist-llama-cpp development by creating an account on GitHub. Saved searches Use saved searches to filter your results more quickly A few days ago, rgerganov's RPC code was merged into llama. cpp achieves across the M-series chips and hopefully answer questions of people wondering if they should upgrade or not. I had to go with quantized versions event though they get a bit slow on the inference time. - 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 I don't know how you went about determining this, but the corresponding CUDA code in ggml/src/ggml-cuda/mmq. 9ghz) 64GB DDR4 and a Tesla P40 with 24gb Vram. Notifications You must be signed in to change notification settings; Fork 8 _FORCE_MMQ: no ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes ggml_init_cublas: found 1 CUDA devices: Device 0: Tesla P40, compute capability 6. Basically I'm When gppm starts first and llama. You can run the llama model which far outpaces GPT-3. As a workaround, building with a higher value of LLAMA_CUDA_MMV_Y may fix this, try adding LLAMA_CUDA_MMV_Y=4 to the llama. cpp supports working distributed inference now. Models in other data formats can be converted to GGUF using the convert_*. How can I specify for llama. Perhaps even the ability to mix any GPU that supports vulkan and tensor_split across them. cpp with the P40. I really appreciate this post. 3x with my quantized models, maybe its something to do with the two gpu backends, or the speculative only is designed with float16 Saved searches Use saved searches to filter your results more quickly Hopefully avoiding any losses in the model conversion, as has been the recently discussed topic on Llama-3 and GGUF lately. This will also be fixed. cpp with make as usual. ccp to enable gpu offloading for ggml due to a weird but but that's unrelated to this post. But it's still the cheapest option for LLMs with 24GB. 5t/s, GPU 106 t/s fastllm int4 CPU speed 7. I honestly don't think performance is getting beat without reducing VRAM. cpp with "-DLLAMA_CUDA=ON -DLLAMA_CLBLAST=ON -DLLAMA_CUDA_FORCE_MMQ=ON" in order to use FP32 and acceleration on this old cuda card. gppm will soon not only be able to manage multiple Tesla P40 GPUs in operation with multiple llama. cpp with GPU you need to set LLAMA_CUBLAS flag for make/cmake as your link says. cpp runs them on and with this information accordingly changes the performance modes 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. zip llama-b1428-bin-win-cublas-cu12. cu absolutely does use the __dp4a instruction to take advantage of int8 arithmetic. Q4_K_M. cpp modules do you know to be affected? llama-server. 6-1697589. invoke with numactl --physcpubind=0 --membind=0 . a B450M Bazooka2 motherboard and 16GB of ram. I'll keep this repo up as a means of space-efficiently testing LLaMA weights packaged as state_dicts, but for serious inference or training workloads I encourage users to migrate to transformers. Model quantization plays a crucial role in optimizing deep learning models for deployment on resource-constrained devices. You can help this by offloading more layers to the P40. Very briefly, this means that you can possibly get some speed increases and fit much larger context sizes into VRAM. cpp shows two cuBlas options for Windows: llama-b1428-bin-win-cublas-cu11. cpp requires the model to be stored in the GGUF file format. See the llama. i talk alone and close. The Radeon VII was a Vega 20 XT (GCN 5. 1x Nvidia Tesla P40, Intel Xeon E-2174G (similar to 7700K), 64GB DDR4 2666MHz, IN A VM with 24GB allocated to it. Collecting info here just for Apple Silicon for simplicity. cd build. cpp that made it much faster running on an Nvidia Tesla P40? Saved searches Use saved searches to filter your results more quickly ⚠️ 2023-03-16: LLaMA is now supported in Huggingface transformers, which has out-of-the-box int8 support. Here's a In case anyone stumbles upon this post looking for help with their P40: I recommend using GGUF models with the llama. Even at 24g, I find myself wishing the P40s were a newer architecture so they were faster. 0, and Microsoft’s Phi-3-mini-4k-instruct model in 4-bit GGUF. cpp for CPU only on Linux and Windows and use Metal on MacOS. That works if that's what you mean. 1, VMM: no Device 2: Tesla P40, compute capability 6. cpp PRs but that's a over-representation of guys wearing girl clothes I know, that's great right, an open-source project that's not made of narrow-minded hateful discriminatory bigots, and that's open to contributions from anyone, without letting But only with the pure llama. Do you have any cards to advise me with my configuration? Do you have an Llama. 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 My llama. Now that speculative decoding landed yesterday The more VRAM the better if you'd like to run larger LLMs. It's a work in progress and has limitations. I'm looking llama. 1, VMM: yes Available devices Anyone managed to get multiple Radeon GPUs to tensor_split using the vulkan backend in kobold. Went over the CPU->CPU link, as it would in your 8xP40 rig You can even run LLaMA-65B (which far surpasses GPT 3. I'd love to see what the P40 can do if you toss 8k or even 16k tokens at it. After that, should be relatively straight forward. But it does not have the integer intrinsics that llama. cpp? Question | Help I feel like this should be a thing already, or it will be a thing very soon. But that's an upside for the P40 and Llama. I generally only run models in GPTQ, AWQ or exl2 formats, but was interested in doing the exl2 vs. 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. Downsides are that it uses more ram and crashes when it runs out of memory. but the great thing is that after it's fixed in llama. 23-x64. cpp specifically Discovered a bug with the following conditions: Commit: 1ea2a00 OS: Win 11 Cuda: 12. 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. Restrict each llama. 5 Turbo, completely locally. I also bought 4 Tesla P40 to be able to learn more on inference, training, LoRa Fine-tuning, etc. It's rare. cpp to use as much vram as it needs from this cluster of gpu's? Since I am a llama. I'm actually surprised that no one else saw this considering I've seen other 2S systems being discussed in previous issues. 1) card that was released in February I've heard people running llama. 00 MB per state) llama_model_load_internal: allocating batch_size x (1280 kB + n_ctx x 256 B) = 576 MB But only with the pure llama. cpp uses for quantized inferencins. Running Grok-1 Q8_0 base Now I’m debating yanking out four P40 from the Dells or four P100s. 1, VMM: yes Device 3: Tesla P40, compute capability 6. HOW in the world is the Tesla P40 faster? What happened to llama. “Performance” without additional context will usually refer to the performance of generating new tokens since processing the prompt is 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 In terms of pascal-relevant optimizations for llama. This should result in Well, old Tesla P40 can do ~30-40 tps and cost ~150. They do come in handy for larger models but yours are low on memory. GPU are 3x Nvidia Tesla + 3090 All future commits seems to be affected. Notably, llama. cpp and exllama. Name and Version. I have done this, I'll try to explain. I use it daily and it performs at excellent speeds. cpp has continued accelerating (e. They were introduced with compute=6. The Hugging Face platform hosts a number of LLMs compatible with llama. I have 3xP40s and a 3090 in a server. 60000-91~22. 2-2, Vulkan mesa-vulkan-drivers 23. Exllama 1 Use llama. cpp developer it will be the software used for testing unless specified otherwise. I have multiple P40s + 2x3090. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption; gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage; So its like a worse cheaper P40 which requires no cooling setup. md I first cross-compile OpenCL-SDK as follows Copied from LostRuins#854 but with additional testing for llama. cpp and the old MPI code has been removed. Your setup will use a lot of power. Reply reply To compile llama. This is because Pascal cards have dog crap FP16 performance as we all know. /main The main goal of llama. 👍 4 AB0x, burningdatams, e-mon, and Nuclear6 reacted with thumbs up emoji ️ 3 tupini07, BurgerAndreas, and raphaelmerx reacted with heart emoji The NVIDIA RTX AI for Windows PCs platform offers a thriving ecosystem of thousands of open-source models for application developers to leverage and integrate into Windows applications. cpp to test the LLaMA models inference speed of different GPUs on RunPod, 13-inch M1 MacBook Air, 14-inch M1 Max MacBook Pro, M2 Ultra Mac Studio and 16-inch M3 Max MacBook Pro for LLaMA 3. Contribute to HimariO/llama. 1 You must be logged in to vote. My guess is that it will be better to fill up the server with more P40's before I start upgrading the CPU. 0. from llama-cpp-python repo:. The Hugging Face $ lscpu Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 56 On-line CPU(s) list: 0-55 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) CPU E5-2680 v4 @ 2. Lately llama. 8 t/s for a 65b 4bit via pipelining for inference. cpp is your best choice for the P40s. P40: They will work but are practically limited to FP32 compute. Both the prompt processing and token generation tests were performed using the Koboldcpp is a derivative of llama. 5g gguf), llama. Note that llama. You can also use 2/3/4/5/6 bit with llama. But that's an upside for the P40 and Currently I have a ryzen 5 2400g, a B450M Bazooka2 motherboard and 16GB of ram. I would like to run AI systems like llama. I'm using two Tesla P40 and get like 20 tok/s on llama. Quantization - larger models with Llama. 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 1x Nvidia Tesla P40, Intel Xeon E-2174G (similar to 7700K), 64GB DDR4 2666MHz, IN A VM with 24GB allocated to it. 39 ms. 3 GB/s. 3 llama. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption; gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage; Also, Tesla P40’s lack FP16 for some dang reason, so they tend to suck for training, but there may be hope of doing int8 or maybe int4 inference on them. cpp when you do the pip install, and you can set a few environment variables before that to configure BLAS support and these things. Reply reply koesn • Which llama. The correct fix would be to move this to the x dimension, which has no such limit. cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide variety of hardware - locally and in the cloud. You can definitely run GPTQ on P40. I tried that route and it's always slower. What if we can get it to infer on P40 using INT8? You signed in with another tab or window. 2023-12-18 22:22:56 INFO:Loading mixtral-8x7b-instruct-v0. exl2 won't be faster on a p40, as others have noted elx2 casts everything to fp16 on the fly and p40's have about For multi-gpu models llama. Using Ooga, I've loaded this model with llama. cpp weights detected: models/mixtral-8x7b-instruct-v0. Now take the OpenBLAS release and from there copy On paper with a single P40 you should be able to run this quantized version of Mixtral with 20gb VRAM dolphin-mixtral:8x7b-v2. Now that speculative decoding landed yesterday you can get up to 20% faster inference. Since its inception, the project has improved significantly thanks to many contributions. Just realized I never quite considered six Tesla P4. Reload to refresh your session. I am looking for old graphics cards with a lot of memory (16GB minimum) and cheap type P40, M40, Radeon mi25. cpp or llama. py Python scripts in this repo. cpp has been even faster than GPTQ/AutoGPTQ. You signed in with another tab or window. cpp is CPU only but llama runs on GPU using the HuggingFace Transformers library. You'll have to do your own cooling, the P40 is designed to Contribute to MarshallMcfly/llama-cpp development by creating an account on GitHub. It's a different implementation of FA. I recently bought a P40 and I plan to optimize performance for it, but I'll I'm wondering if it makes sense to have nvidia-pstate directly in llama. cpp with the P100, but my understanding is I can only run llama. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption; gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage; In that thread, someone asked for tests of speculative decoding for both Exllama v2 and llama. cpp and it seems to support only INT8 inference on ARM CPUs. Potentially being able to run 6bpw, more worker, etc. 2-1. Fully loaded up around 1. 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. 2x 4090s, 13900K. Since commit b3188 llama-cli produce incoherent output on multi-gpu system with CUDA and row tensor splitting. cpp and other such programs have made it all possible. I was hitting 20 t/s on 2x P40 in KoboldCpp on the 6 The performance of P40 at enforced FP16 is half of FP32 but something seems to happen where 2xFP16 is used because when I load FP16 models they work the same and still use FP16 memory footprint. cpp is on the Verge of Getting SOTA 2-bit Quants The Motivation Behind SOTA 2-bit Quants. You signed out in another tab or window. Good point about where to place the temp probe. cpp with it. No other alternative available from nvidia with that budget and with that amount of vram. That's at it's best. I'm saving it so that I can peek over it later. 5-q3_K_L You would just replace “mistral” in the second command with the above. Overview. cpp. Be sure to The server also has 4x PCIe x16. This is a collection of short llama. All of these backends are supported by llama-cpp-python and or llama-cpp-python: CMAKE_ARGS="-DLLAMA_CUBLAS=ON -DLLAMA_AVX2=OFF -DLLAMA_F16C=OFF -DLLAMA_FMA=OFF" pip install llama-cpp-python. Originally released in 2023, this open-source repository is a lightweight, I updated to the latest commit because ooba said it uses the latest llama. This is running on 2x P40's, ie: . 5) faster than GPT 3. cpp folder and cmake in build/bin. cpp Public. 43 MiB In order to evaluate of the cheap 2nd-hand Nvidia Tesla P40 24G, this is a little experiment to run LLMs for Code on Apple M1, Nvidia T4 16G and P40. The default pip install behaviour is to build llama. I don't know what's going on with llama. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption; gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage; I have a Ryzen 5 2400G, a B450M bazooka v2 motherboard and 16GB of ram. Also llama-cpp-python is probably a nice option too since it compiles llama. llama. Since I am a llama. For example, with llama. 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. 2t/s, GPU 65t/s 在FP16下 Can I run llama. We don't have tensor cores. Also I'm finding it interesting that hyper-threading is actually improving inference speeds in this Contribute to joelvaneenwyk/llama-cpp development by creating an account on GitHub. 0 8x but not bad since each CPU has 40 pcie lanes, combined to 80 lanes. ) 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 I’ve tried dual P40 with dual P4 in the half width slots. All reactions. You can get a 24gb P40 on ebay for about $200 and not have to deal with the mac BS. There were 2 3090s mixed in but it was a 5x24 test. I've been poking around on the fans, temp, and noise. 04, rocm 6. In theory P40 should be faster than 3090 . Only in GPTQ did I notice speed cut to half but once that got turned off (don't use "faster" kernel) it's back to normal. cpp might not be the fastest among A LLAMA_NUMA=on compile option with libnuma might work for this case, considering how this looks like a decent performance improvement. 47 ms / 515 tokens ( 58. it is still better on GPU. Then, get OpenBLAS OpenBLAS-0. Works great with ExLlamaV2. Higher speed is better. 1, VMM: yes Device 2: Tesla P40, compute capability 6. cpp with much more complex and more heavier model: Bakllava-1 and it was immediate success. Reply reply MLC-LLM's Vulkan is hilariously fast, like as fast as the llama. 30 MB (+ 1280. Had mixed results on many LLMs due to how they load onto VRAM. - Would you advise me a card (Mi25, P40, k80) to add to my llama-cpp-python doesn't supply pre-compiled binaries with CUDA support. 7-mixtral-8x7b. I haven't been able to build vulkan with llama-cpp My single P100 numbers jive with the other two users, and were in the right general ballpark the P40 is usually ~half the speed of P100 on things. cpp loader and with nvlink patched into the code. cpp README for a full list of supported backends. cpp it looks like some formats have more performance optimized code P40's are probably going to be faster on CUDA though, at least for now. P40 should even work with stable diffusion, I What sort of performance would you expect on a P40 with either 4 bit or 8 bit GPTQ 13B? My biggest issue with Triton is the lack of support for Pascal and older GPUs. gguf 2023-12-18 22:22:56 INFO:llama. When you launch "main" make certain the displayed flags indicate that tensor cores are not being used. 70 ms / 213 runs ( 111. - 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 So yea a difference is between llama. And therefore text-gen-ui also doesn't provide any; ooba tends to want to use pre-built binaries supplied by the developers of libraries he uses, rather than providing his own. With 70b q6_K and 7b q8_0 on 3x P40 the performance it 3. P100 has good FP16, but only 16gb of Vram (but it's HBM2). Also, Tesla P40’s lack FP16 for some dang reason, so they tend to suck for training, but there may be hope of doing int8 or maybe int4 inference on them. ExLlamaV2 is kinda the hot thing for local LLMs and the P40 lacks support here. cpp because of fp16 computations, whereas the 3060 isn't. Very briefly, this means that you can possibly get some speed increases How to properly use llama. Both the prompt processing and token generation tests were performed using the default values of 512 tokens and 128 tokens respectively with 25 repetitions apiece, and the results averaged. 40GHz CPU family: 6 Model: 79 Thread(s) per core: 2 Core(s) per socket: 14 Socket(s): 2 Stepping: 1 CPU(s) scaling MHz: ggerganov / llama. gppm must be installed on the host where the GPUs are installed and llama. Discussion P40 INT8 about 47 TFLOPS 3090 FP16/FP32 about 35+ TFLOPS. My goal is to basically have something that is reasonably coherent, and responds fast enough to one user at a time for TTS for something like home assistant. cpp development by creating an account on GitHub. cpp beats exllama on my machine and can use the P40 on Q6 models. Now I have a task to make the Bakllava-1 work with webGPU in browser. What this means for llama. First, following README. I have a intel scalable gpu server, with 6x Nvidia P40 video cards with 24GB of VRAM each. qwen2vl development by creating an account on GitHub. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption; gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage; Llama. 2. cpp build 3140 was utilized for these tests, using CUDA version 12. Having had a quick look at llama. I've tried setting the split to 4,4,1 and defining GPU0 (a P40) as the primary (this seems to be the default anyway), but the most layers I can get in GPU without hitting an OOM, however, is 82. The "HF" version is slow as molasses. zip Are some older GPUs, like maybe a P40 or something, only supported under older CUDA versions and not newer versions? Or is there some other reason to compile for two different Time has passed, I learned a lot and the gods that are creating llama. cpp with the help of for example the intel arc a770 since it has 16gb vram? It supports opencl, right? Or should I go with a RTX 3060? If you have to run on your own hardware, then get a used Nvidia P40 - it has 24GB of RAM (you will need to attach your own fan, you can do it with a 3D printer or just some cardboard to ~/llama. cpp has something similar to it (they call it optimized kernels? not entire sure). cpp and max context on 5x3090 this week - found that I could only fit approx. I'll let you know! But the official KoboldCpp with these optimizations merged should be coming very soon. And every time I've asked for inference speeds they don't respond. cpp is not using the GPU, it runs fine on the CPU (if fast enough) A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. 63 t/s which is only ~half of what I get with regular inference So the Github build page for llama. cpp and even there it The P40 is restricted to llama. Put w64devkit somewhere you like, no need to set up anything else like PATH, there is just one executable that opens a shell, from there you can build llama. 6, VMM: yes Device 1: Tesla P40, compute capability 6. 1 which the P40 is. One is from the NVIDIA official spec, which says 347 GB/s, and the other is from the TechpowerUP database, which says 694. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption; Infrastructure: Paddler - Stateful load My PC has 8 cores, so it seems like with whisper. 1 llama_model_loader: loaded meta data with 20 key-value pairs I have run llama. A probe against the exhaust could work but would require testing & tweaking the GPU Linux package distribution pains. Q6_K. Its wonderful (for me). Well done! V interesting! ‘Was just experimenting with CR+ (6. the steps are the same as that guide except for adding a CMAKE argument "-DLLAMA_CUDA_FORCE_MMQ=ON" since the regular llama-cpp-python not P40s can run GGUF models through llama. Current Behavior Cross-compile OpenCL-SDK. . You'll be stuck with llama. Plus I can use q5/q6 70b split on 3 GPUs. Beta Was this translation helpful? Give feedback. I’m leaning on towards P100s because of the insane speeds in exllamav2. However if The problem here seems to be that n_vocab is very large, and this value is used as the y dimension of the block size, which has a maximum of 65535. 1-x64. cpp with "-DLLAMA_CUDA=ON -DLLAMA_CLBLAST=ON -DLLAMA_CUDA_FORCE_MMQ=ON" option in order to use FP32 and acceleration on this old cuda card. But 24gb of Vram is cool. 4 CPU: Ryzen 5800x RAM: 64GB DDR Nonetheless, TensorRT is definitely faster than llama. Pros: No power cable necessary (addl cost and unlocking upto 5 more slots) 8gb x 6 = 48gb Cost: As low as $70 for P4 vs $150-$180 for P40 In llama. cpp in a relatively smooth way. cpp by default does not use half-precision floating point arithmetic. 1, and ROCm (dkms amdgpu/6. The higher end instincts don't compare favorably to the 3090 because of price/speed despite being OK cards. 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. By default 32 bit floats are used. 5 Turbo with two $200 24GB Nvidia Tesla P40 cards, since in 4bit the model is only 39GB with no output quality loss. But still the GPU is not Saved searches Use saved searches to filter your results more quickly Incredibly, running a local LLM (large language model) on just the CPU is possible with Llama. I have no idea why speculative for llama. I build llama. I don't expect support from Nvidia to last much longer though. crashr/gppm – launch llama. Someone advise me to test compiled llama. 56bpw/79. It can be useful to compare the performance that llama. Trending; LLaMA; After downloading a model, use the CLI tools to run it locally - see below. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption; Infrastructure: Paddler - Stateful load Contribute to eugenehp/bitnet-llama. More and increasingly efficient small (3b/7b) models are emerging. Reply Discovered a bug with the following conditions: Commit: d5d5dda OS: Win 11 CPU: Ryzen 5800x RAM: 64GB DDR4 GPU0: RTX 3060ti [not being used for koboldcpp] GPU1: Tesla P40 Model: Any Mixtral (tested a L2-8x7b-iq4 and a L3-4x8b-q6k mixtral I have 256g of ram and physical 32 cores. Instructions for converting weights can be found here. cpp have context quantization?”. 40GHz CPU family: 6 Model: 79 Thread(s) per core: 2 Core(s) per socket: 14 Socket(s): 2 Stepping: 1 CPU(s) scaling MHz: Contribute to HimariO/llama. cpp loader now. Layer tensor split works fine but is actually almost twice slower. Contribute to Qesterius/llama. /main -m dolphin-2. Currently I have a ryzen 5 2400g, a B450M Bazooka2 motherboard and 16GB of ram. Going back to using row splitting the performance only really improves for p40. 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. - Would you advise me a card (Mi25, P40, k80) to add to my hi, I have a Tesla p40 card. cpp benchmarks on various Apple Silicon hardware. The The Hugging Face platform hosts a number of LLMs compatible with llama. Reply reply More replies. Regarding the memory bandwidth of the NVIDIA P40, I have seen two different statements. cpp keeping threads at 6/7 gives the best results. g. cpp using: cmake -DLLAMA_AVX2=off -DLLAMA_F16C=off -DLLAMA_CUBLAS=on -DLLAMA_CUDA_FORCE_MMQ=on Using a llama2-70b-Q8_0 model, I see @ztxz16 我做了些初步的测试，结论是在我的机器 AMD Ryzen 5950x, RTX A6000, threads=6, 统一的模型vicuna_7b_v1. 94 tokens per second) llama_print_timings: total time = 54691. I get about 1 token every 2 seconds with a 34 billion parameter LLM inference in C/C++. 34 ms per token, 17. So, what exactly is the bandwidth of the P40? Does anyone know? Hello, I am trying to get some HW to work with llama 2 the current hardware works fine but its a bit slow and i cant load the full models. With llama. For what it's worth, if you are looking at llama2 70b, you should be looking also at Mixtral-8x7b. 20k tokens before OOM and was thinking “when will llama. Installation with OpenBLAS / . it's faster than ollama but i can't use it for conversation. Traditional quantization techniques typically rely on higher precision representations, such as 8-bit or 16-bit, to strike a Subreddit to discuss about Llama, the large language model created by Meta AI. I put in one P40 for now as the most cost effective option to be able to play with LLM's. There are currently 4 backends: OpenBLAS, cuBLAS (Cuda), CLBlast (OpenCL), and an experimental fork for HipBlas (ROCm). The only circumstances in which this code would not be used is if you were to compile with GGML_CUDA_FORCE_DMMV or my llama-cpp version is: llama_cpp_python 0. cpp CUDA backend. cpp developer it will be the I have 3xP40s and a 3090 in a server. 18. I have tried running llama. I often use the 3090s for inference and leave the older cards for SD. Its way more finicky to set up, but I would definitely pursue it if you are on an IGP or whatever. It currently is limited to FP16, no quant support yet. First, get w64devkit w64devkit-1. It uses llama. gguf -n 1024 -ngl 100 --prompt "create a christmas poem with 1000 words" -c 4096. cpp got me another +1. P40 is a Maxwell architecture, right? I am running Titan X (also Maxwell). 1 development by creating an account on GitHub. Old Nvidia P40 (Pascal 24GB) cards are easily available for $200 or less and would be easy/cheap to play. cpp is running. cpp, vicuna, alpaca in 4 bits version on my computer. cpp comparison. cpp$ lscpu Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 56 On-line CPU(s) list: 0-55 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) CPU E5-2680 v4 @ 2. When you use VRAM with some Bytes the power consumption increses to 50W. The P40 has ridiculously lower FP16 compared to the 3090, but the FP32 is roughly 35% or something (so, three of them=one 3090 in performance and cost, but with 3x the vram). Non-nvidia alternatives still can be difficult to get working, and even more hassle to P40 = Pascal(physically, the board is a 1080 TI/ Titan X pascal with different/fully populated memory pads, no display outs, and the power socket moved) Not that I take issue with llama. Reply reply But the P40 sits at 9 Watts unloaded and unfortunately 56W loaded but idle. These results seem off though. There is a reason llama. A 13B 2: The llama. tensorcores support) and now I find llama. The downside is that it appears to take more memory due to FP32. cpp in an Android APP successfully. Tested 2024-01-29 with llama. Guess I’m in luck😁 🙏 P40 has more Vram, but sucks at FP16 operations. 0-x64. cpp process to one NUMA domain (e. It is the main playground for developing new llama-cpp-python doesn't supply pre-compiled binaries with CUDA support. cpp unload the model and free the GPU VRAM, so that it saves power. You switched accounts on another tab or window. zip. Other model formats make my card #1 run at 100% and card #2 at 0%. 3. It's because it has proper use of multiple cores unlike python and my setup can go to 60-80% per GPU instead of 50% use. llama_print_timings: prompt eval time = 30047. So at best, it's the same speed as llama. gguf ggml_init_cublas: GGML_CUDA_FORCE_MMQ: yes ggml_init_cublas: CUDA_USE_TENSOR_CORES: no ggml_init_cublas: found 2 CUDA devices: Device 0: Tesla Subreddit to discuss about Llama, the large language model created by Meta AI. I have a P40 in a R720XD and for cooling I used attached some fans I pulled from a switch with some teflon Sure, I'm mostly using AutoGPTQ still because I'm able to get it working the nicest, but I believe that llama. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. 7. cpp models are give me the llama. GPU 8B Q4_K_M 8B F16 gppm uses nvidia-pstate under the hood which makes it possible to switch the performance state of P40 GPUs at all. Also, I couldn't get it to work with On Pascal cards like the Tesla P40 you need to force CUBLAS to use the older MMQ kernel instead of using the tensor kernels. 5 on a couple of $200 Tesla P40 GPUs at faster speeds than GPT-3. This is more disk and compute intensive so lets hope we get GPU inference support for BF16 models in llama. cpp iterations. cpp is one popular tool, with over 65K GitHub stars at the time of writing. The text was updated successfully, but these errors were encountered: Device 1: Tesla P40, compute capability 6. cpp!— however, it can be pretty slow. cpp (enabled only for specific GPUs, e. And it looks like the MLC has support for it. 04. Combining multiple P40 results in slightly faster t/s than a single P40. Power consumption only drops after first inference. cpp that improved performance. cpp:. it would give me 6-7t/s with llama. 87 ms per token, 8. Alpha scaling works. But TRTLLM doesn't support P40. Memory inefficiency problems. 11+cu117. If you have multiple P40s it's definitely your best choice. 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. You just dual wield 16gb on an old shitty PC for $200, able to run 70B Q3_K_S. cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption; gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage; The P40 was a really great deal for 24GB, even if it's not the fastest on the market, and I'll be buying at least two more to try to run a 65B model. Reply reply Updating to latest llama. 22. I just wanted to point out that llama. Easy money Share Strangely enough, I'm now seeing the opposite. cpp HF. gguf. 1. cpp with scavenged "optimized compiler flags" from all around the internet, IE: mkdir build. Model: xwin-lm-70b-v0. No-Statement-0001 The P40 is a cheap and capable Description. you just need to use GGUF models with llama. Matrix multiplications, which take up most of the runtime are split across all available GPUs by default. cpp that made it much faster running on an Nvidia Tesla P40? Contribute to draidev/llama. cpp and get like 7-8t/s. cpp d2f650cb (1999) and latest on a 5800X3D w/ DDR4-3600 system with CLBlast libclblast-dev 1. cpp quite well, and GPTQ models through other loaders with much less efficiency. cpp with multiple NVIDIA GPUs with different CUDA compute engine versions? I have an RTX 2080 Ti 11GB and TESLA P40 24GB in my llama. make puts "main" in llama. So llama. cpp setup now has the following GPUs: 2 P40 24GB 1 P4 8GB. cpp is adding GPU support. I could still run llama. Now I want to enable OpenCL in Android APP to speed up the inference of LLM. cpp still has a CPU backend, so you need at least a decent CPU or it'll bottleneck. hi, i have a Tesla p40 card, it's slow with ollama and Mixtral 8x7b. 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. gppm monitors llama. GGUF edging everyone out with it's P40 support, good performance at the high end, and also CPU inference for the low end. 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 I have tried running mistral 7B with MLC on my m1 metal. Applications are open for YC Summer 2023 I have a machine with a lot of old parts in it, including 8 P40s and 2 Xeon E5-2667v2 CPUs. Llama. cpp in pure GPU inference, and there are things that could be done to improve the performance of the CUDA backend, but this is not a good comparison. llama-cli version b3188 built on Debian 12. However the ability to run larger models and the recent developments to GGUF make it worth it IMO. 5% I have added multi GPU support for llama. 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, P40 will have similar tps speed to 4060ti, which is about 40 tps with 7b quantized models. Someone advise me to test compiling llama. Total cost $400 plus some junker used PC with two spare PCIe 4 x16 lanes. I've fit upto 34B models on a single P40 @ 4-bit. In terms of CPU Ryzen 7000 series looks very promising, because of high frequency DDR5 and implementation of AVX-512 instruction set Feature Description Would it be possible to set a --unload-timeout flag in "server" mode after that llama. P40 has plenty of benches, mi25 and the other amd series finally got some too, but it took forever. cpp GGUF is that the performance is equal to the average tokens/s performance llama. cpp-embedding-llama3. cpp, with a 7Bq4 model on P100, I get 22 tok/s without batching. I was under the impression both P40 and P100 along with the GTX 10x0 consumer family were really usable only with llama. cpp, the open source Llama. cpp it will work. cpp command and I'll try it, I just use -ts option to select only the 3090's and leave the P40's out of the party. The Hugging Face Hardware config is Intel i5-10400 (6 cores, 12 threads ~2. cpp code. 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. A few details about the P40: you'll have to figure out cooling. llama_model_load_internal: using CUDA for GPU acceleration ggml_cuda_set_main_device: using device 0 (Tesla P40) as main device llama_model_load_internal: mem required = 1282. 1, VMM: no llm_load_tensors: ggml ctx size = 1. cpp (gguf) make my 2 cards work equally around 80% each. With vLLM, I get 71 tok/s in the same conditions (benefiting from the P100 2x FP16 performance). I’m running Mixtral 8x7b Q8 at 5-6 token/sec on a 12 gpu rig (1060 6gb each). 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). Contribute to RobertBeckebans/AI_chatbot_llama. Average speed (tokens/s) of generating 1024 tokens by GPUs on LLaMA 3. cpp q4_0 CPU speed 7. cpp Reply reply Top 2% Rank by The guy who implemented GPU offloading in llama. cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. cpp has now partial GPU support for ggml processing. cpp supports a number of hardware acceleration backends depending including OpenBLAS, cuBLAS, CLBlast, HIPBLAS, and Metal. And it kept crushing (git issue with description). This means you will have compatibility issues and will have to watch your software carefully to not have trash performance. With CUDA, I only get about 1-3 tokens per second. Everywhere else, only xformers works on P40 but I had to compile it. It sort of get's slow at high contexts more than EXL2 or GPTQ does though. P40/P100)? nvidia-pstate reduces the idle power consumption (and More options to split the work between cpu and gpu with the latest llama. cpp Performance testing (WIP) This page aims to collect performance numbers for LLaMA inference to inform hardware purchase and software configuration decisions. 04); Radeon VII. I would like to use vicuna/Alpaca/llama. cpp-gguf development by creating an account on GitHub. _init: found 4 CUDA devices: Device 0: NVIDIA GeForce RTX 3090, compute capability 8. You can run a model across more than 1 machine. 5. cuh and ggml/src/ggml-cuda/mmvq. For example a NVIDIA P40 24GB needs 9W if nothing is loaded to VRAM. This is the first time I have tried this option, and it really works well on llama 2 models. something weird, when I build llama. cpp , it just seems models perform slightly worse with it perplexity-wise when everything else is kept constant vs gptq Obviously I'm only able to run 65b models on the cpu/ram (I can't compile the latest llama. 4-0ubuntu1~22. cpp only gives 1. xhn dbi yxk ajgmdll ibd jknk hdnt njzxx dvtjkys pvbso