Milestone. Cheers, Simon. I took a look at the OpenAI class. n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool. I just assumed it's the case for llamacpp because i didn't see anybody say otherwise. I came across this issue two days ago and spent half a day conducting thorough tests and creating a detailed bug report for. This is the recommended installation method as it ensures that llama. bin --n-gpu-layers 24 Applications are open for YC Winter 2024 Text-generation-webui manual installation on Windows WSL2 / Ubuntu . 30 Mar, 2023 at 4:06 pm. The test machine is a desktop with 32GB of RAM, powered by an AMD Ryzen 9 5900x CPU and an NVIDIA RTX 3070 Ti GPU with 8GB of VRAM. """ n_batch: Optional [int] = Field (8, alias = "n_batch") """Number of tokens to process in parallel. In the Continue configuration, add "from continuedev. Combinatorilliance. @jiapei100, looks like you have n_ctx set to 512 so thats way too small of a context, try n_ctx=4096 in the LlamaCpp initialization step for that specific model. The n_gpu_layers parameter is set to None by default in the LlamaCppEmbeddings class. GPU. That is, one gets maximum performance if one sees in. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. Maximum number of prompt tokens to batch together when calling llama_eval. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. Change -c 4096 to the desired sequence length. , stream=True) see docs. 1. 25 GB/s, while the M1 GPU can do up to 5. manager import CallbackManager from langchain. callbacks. We need to document that n_gpu_layers should be set to a number that results in the model using just under 100% of VRAM, as reported by nvidia-smi. 1. mlock prevent disk read, so. similarity_search(query) from langchain. py and should provide about the same functionality as the main program in the original C++ repository. py","path":"langchain/llms/__init__. 62. LlamaCpp [source] ¶ Bases: LLM. 1thread/core is supposedly optimal. q5_0. . Sorry for stupid question :) Suggestion: No response. cpp中的-c参数一致，定义上下文窗口大小，默认512，这里设置为配置文件的model_n_ctx数量，即4096; n_gpu_layers：与llama. The best thing you can do to help us help you, is to start llamacpp and give us. Old model files like. gguf. cpp loader also has a newer argument condition that if n-gpu-layers is -1 it will load the full model. I've added --n-gpu-layersto the CMD_FLAGS variable in webui. At a rate of 25-30t/s vs 15-20t/s running Q8 GGUF models. But the resulting binary claims it wasn't built with GPU support so it ignores --n-gpu-layers. The model can also run on the integrated GPU, and while the speed is slower, it remains usable. bin. env" file: 上記を考慮して、ローカルで環境構築する際はmodel=13b, n_gpu_layer=20かmodel=7b, n_gpu_layer=40を使用することにします。 出力値はどのモデルも微妙かなと思いましたが、ここはプロンプト次第でもう少し制御できるのかなと思うので工夫していきたいと思います。 Issue you'd like to raise. py - not. Run. To compile it with OpenBLAS and CLBlast, execute the command provided below:. Step 4: Run it. change llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, max_tokens=model_n_ctx, n_gpu_layers=model_n_gpu, n_batch=model_n_batch, callbacks=callbacks, verbose=False) We add the GPU offload settings, and we add n_ctx which is the chunk. The text was updated successfully, but these errors were encountered:n_batch: Number of tokens to process in parallel. . bin llama. /main -m models/13B/ggml-model-q4_0. When trying to load a 14GB model, mmap has to be used since with OS overhead and everything it doesn't fit into 16GB of RAM. I use the following command line; adjust for your tastes and needs:. Given a model with n layers, the total memory for the KV cache is: (n_{ ext{blocks}} cdot. Using KoboldCPP with CLBlast, gpulayers 42, with the Wizard-Vicuna-30B-Uncensored model, I'm getting 1-2 tokens/second. cpp:. The n_gpu_layers parameter determines how many layers of the model are offloaded to your GPU, and the n_batch parameter determines how many tokens are processed in parallel. These files are GGML format model files for Meta's LLaMA 7b. cpp. (4) Download a v3 ggml llama/vicuna/alpaca model - ggmlv3 - file name ends with q4_0. 9, n_batch=1024) if the user have a Nvidia GPU, part of the model will be offloaded on gpu, and it accelerate things. NET. GPUにオフロードできるレイヤー数をパラメータ「n_gpu_layers」で調整できます。 上記では「n_gpu_layers=20」としましたが、このモデルでは「0」から「40」まで指定できるそうです。これによるメモリ（メイン、VRAM）、実行時間を比較してみました。 n_gpu_layers=0In text-generation-webui the parameter to use is pre_layer, which controls how many layers are loaded on the GPU. from_pretrained(your_tokenizer) model = AutoModelForCausalLM. When i started toying with LLMs i got ooba web ui with a guide, and the guide explained that loading partial layers to the GPU will make the loader run that many layers, and swap ram/vram for the next layers. ; If you are on Windows, please run docker-compose not docker compose and. I don’t think offloading layers to gpu is very useful at this point. Q. bin", n_ctx=2048, n_gpu_layers=30 API Reference My qualified guess would be that, theoretically, you could get around a 20x speedup for GPU. The 7B model works with 100% of the layers on the card. libs. Defaults to 512. Should be a number between 1 and n_ctx. With the model I was using I could fit 35 out of 40 layers in using CUDA. Milestone. If you have enough VRAM, just put an arbitarily high number, or. ・-c N, --ctx-size N: プロンプトのコンテキストサイズの設定 ・-ngl N、--n-gpu-layers N: cuBLASの計算のために一部のレイヤーをGPUにオフロード。 ・-mg i, --main-gpu i: メインGPU。cuBLASが必要 (default:GPU 0) ・-ts SPLIT, --tensor-split SPLIT: 複数のGPUにどのように分割するかを制御. This should make utilizing these parameters more user friendly and more consistent with LlamaCpp's internal api. class LlamaCpp (LLM): """llama. Note: the above RAM figures assume no GPU offloading. Follow the build instructions to use Metal acceleration for full GPU support. cpp section under models, you can increase n-gpu-layers. Similar to Hardware Acceleration section above, you can also install with. Since the default model is llama2-chat, we use the util functions found in llama_index. API. Now, I've expanded it to support more models and formats. cpp中的-c参数一致，定义上下文窗口大小，默认512，这里设置为配置文件的model_n_ctx数量，即4096; n_gpu_layers：与llama. PyTorch is the framework that will be used by the webUI to talk to the GPU. Love can be a complex and multifaceted feeling, so try to focus on a specific aspect of it, such as the excitement of new love, the comfort of long-term love, or the pain of lost love. You'll need to play with <some number> which is how many layers to put on the GPU. My guess is that the GPU-CPU cooperation or convertion during Processing part cost too much time. Remove it if you don't have GPU acceleration. bin C: \U sers \A rmaguedin \A ppData \L ocal \P rograms \P ython \P ython310 \l ib \s ite-packages \b itsandbytes \l ibbitsandbytes_cpu. On 4090 GPU + Intel i9-13900K CPU: 7B q4_K_S: New llama. ggmlv3. Let’s use llama. To enable ROCm support, install the ctransformers package using:If running on Apple Silicon (ARM) it is not suggested to run on Docker due to emulation. binllama. python3 -m llama_cpp. So now llama. ggmlv3. It would be great to have it. Trying to run the below model and it is not running using GPU and defaulting to CPU compute. I've added --n-gpu-layersto the CMD_FLAGS variable in webui. 1 -n -1 -p "### Instruction: Write a story about llamas ### Response:"Some time back I created llamacpp-for-kobold, a lightweight program that combines KoboldAI (a full featured text writing client for autoregressive LLMs) with llama. If you have previously installed llama-cpp-python through pip and want to upgrade your version or rebuild the package with. Download the specific Llama-2 model ( Llama-2-7B-Chat-GGML) you want to use and place it inside the “models” folder. TheBloke. Using Metal makes the computation run on the GPU. cpp with the following works fine on my computer. I recommend checking if the GPU offloading option is successfully working by loading the model directly in llama. I will be providing GGUF models for all my repos in the next 2-3 days. Open Visual Studio. I tried out GPU inference on Apple Silicon using Metal with GGML and ran the following command to enable GPU inference:. n_gpu_layers: number of layers to be loaded into GPU memory. For the first time ever, this means GGML can now outperform AutoGPTQ and GPTQ-for-LLaMa inference (though it still loses to exllama) Note: if you test this, be aware that you should now use --threads 1 as it's no longer beneficial to use. Recent fixes to llama-cpp-python in the v0. CLBLAST_DIR. cpp to efficiently run them. Enable NUMA support. py. Please note that this is one potential solution and it might not work in all cases. Load a 13b quantized bin type GGMLmodel. You want as many GPU layers as possible without ‘overflowing’ the VRAM that is available for context, so to speak. streaming_stdout import StreamingStdOutCallbackHandler # Callbacks support token-wise streaming callback_manager =. The determination of the optimal configuration could. To use it. Remove it if you don't have GPU acceleration. q4_K_M. To install the server package and get started: pip install llama-cpp-python [server] python3 -m llama_cpp. bin. Set MODEL_PATH to the path of your llama. The n_gpu_layers parameter is set to None by default in the LlamaCppEmbeddings class. Enough for 13 layers. 1. Toast the bread until it is lightly browned. cpp中的-c参数一致，定义上下文窗口大小，默认512，这里设置为配置文件的model_n_ctx数量，即4096; n_gpu_layers：与llama. 🤖. cpp already supports mpt, I downloaded gguf from here, and it did load it with llama. If you want to offload all layers, you can simply set this to the maximum value. 55. Check out:. Open up a CMD and go to where you unzipped the app and type "main -m <where you put the model> -r "user:" --interactive-first --gpu-layers <some number>". Two of the most important GPU parameters are: n_gpu_layers - determines how many layers of the model are offloaded to your Metal GPU, in the most case, set it to 1 is. The guy who implemented GPU offloading in llama. 經由普通安裝(pip install llama-cpp-python)，llama-cpp-python不會在GPU執行LLM模型。即使加入執行參數(n_gpu_layers=15000)也沒有用。Source code for langchain. My outputpip uninstall llama-cpp-python -y CMAKE_ARGS="-DLLAMA_METAL=on" pip install -U llama-cpp-python --no-cache-dir pip install 'llama-cpp-python [server]' # you should now have llama-cpp-python v0. While using WSL, it seems I'm unable to run llama. StableDiffusion69 Jun 21. param n_ctx: int = 512 ¶ Token context window. llms import LlamaCpp n_gpu_layers = 1 # Metal set to 1 is enough. e. This should allow you to use the llama-2-70b-chat model with LlamaCpp() on your MacBook Pro with an M1 chip. After installation, you can use the GPU by setting the n_gpu_layers and n_batch parameters when initializing the LlamaCpp model. Default None. cpp. Allow the n-gpu-layers slider to go high enough to fully load the recently released goliath model. Remove it if you don't have GPU acceleration. n-gpu-layers: The number of layers to allocate to the GPU. ) The following is model_path: The library works the same with a CPU, but the inference can take about three times longer compared to using it on a GPU. embeddings. Otherwise, start with a low number like --n-gpu-layers 10 and then gradually increase it until you run out of memory. lm = llama2 + 'This is a prompt' + gen (max_tokens = 10) This is a prompt for the 2018 NaNoW. param n_parts: int =-1 ¶ Number of parts to split the model into. Describe the bug. Thanks. Compilation flags:. You signed in with another tab or window. Llama 65B has 80 layers and is about 40GB. To disable the Metal build at compile time use the LLAMA_NO_METAL=1 flag or the LLAMA_METAL=OFF cmake option. 78. similarity_search(query) from langchain. LinuxPS E:LLaMAllamacpp> . with ctransformers. Especially good for story telling. 非常感谢大佬，懂了，这里用cuBLAS编译，然后设置-ngl参数，让一些层在GPU上跑，提升推理的速度。 这里我仍然有几个问题，希望大佬不吝赐教！ 1 -ngl参数就是普通的数字吗？ 2 在gpu上推理的结果不是很好，我检查了SHA256，没有问题。还有可能是哪里出问题？ Dosubot suggests that there are two possible reasons for this error: either the Llama model was not compiled with GPU support or the 'n_gpu_layers' argument is not being passed correctly. --n_batch: Maximum number of prompt tokens to batch together when calling llama_eval. commented on May 14. 0. start(). Execute "update_windows. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters. The issue was in fact with llama-cpp-python. When you offload some layers to GPU, you process those layers faster. 1. ggmlv3. In theory IF I could place all layers from 65B mode in VRAM I could achieve something around 320-370ms/token :P. I find it strange that CUDA usage on my GPU is the same regardless of. Sprinkle the chopped fresh herbs over the avocado. ggerganov / llama. Open the performance tab -> GPU and look at the graph at the very bottom, called "Shared GPU memory usage". 🤪. By default, we set n_gpu_layers to large value, so llama. cpp is concerned, GGML is now dead - though of course many third-party clients/libraries are likely to continue to support it for a lot longer. cpp. cpp, llama-cpp-python. # Download the ggml-vic13b-q5_1. Example:. param n_ctx: int = 512 ¶ Token context window. LlamaIndex supports using LlamaCPP, which is basically a rewrite in C++ of the Llama inference code and allows one to use the language model on a modest piece of hardware. 81 (windows) - 1 (cuda ) - (2048 * 7168 * 48 * 2) (input) ~ 17 GB left. Each test followed a specific procedure, involving. Spread the mashed avocado on top of the toasted bread. cpp model. If you want to use only the CPU, you can replace the content of the cell below with the following lines. For example, 7b models have 35, 13b have 43, etc. llama_cpp_n_threads. In the Continue extension's sidebar, click through the tutorial and then type /config to access the configuration. param n_batch: Optional [int] = 8 ¶ Number of tokens to process in parallel. py doesn't accepts parameter n_gpu_layer whereas code has it Who can help? @hw Information The official example. 95. LLaMa 65B GPU benchmarks. g. The CLI option --main-gpu can be used to set a GPU for the single GPU. The same as llama. 6 Device 1: NVIDIA GeForce RTX 3060,. streaming_stdout import StreamingStdOutCallbackHandler n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM. I asked it where is Atlanta, and it's very, very very slow. q5_K_M. 1 -n -1 -p "{prompt}" Change -ngl 32 to the number of layers to offload to GPU. cpp will crash. py", line 74, in from_pretrained result. As in not toks/sec but secs/tok. g. Note: the above RAM figures assume no GPU offloading. cpp (a lightweight and fast solution to running 4bit quantized llama models locally). Go to the gpu page and keep it open. My output 「Llama. How to run model to ensure proper performance (boost from GPU/CUDA)? MY PARAMETERS FOR TESTING PURPOSE-p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1. 0 lama model load internal: freq_scale = 1. Example: > . bin successfully locally. GGML files are for CPU + GPU inference using llama. 3x-2x speedup from putting half of layers on the gpu. ggmlv3. Great work @DavidBurela!. However, you can still use a multiprocessing approach within the LlamaCpp model itself, which should allow you to bypass the GIL and achieve true. Reload to refresh your session. Change -c 4096 to the desired sequence length. 0 | 28 | NVIDIA GeForce RTX 3070. Haply the seas, and countries different, With variable objects, shall expel This something-settled matter in his heart, Whereon his brains still beating puts him thus From fashion of himself. 7 --repeat_penalty 1. make BUILD_TYPE=hipblas build Specific GPU targets can be specified. The text was updated successfully, but these errors were encountered: 👍 2 r7l and gururise reacted with thumbs up emoji 👀 1 gururise reacted with eyes emojiMODEL_N_CTX=1024 # Max total size of prompt+answer MODEL_MAX_TOKENS=256 # Max size of answer MODEL_STOP=[STOP] CHAIN_TYPE=betterstuff N_RETRIEVE_DOCUMENTS=100 # How many documents to retrieve from the db N_FORWARD_DOCUMENTS=100 # How many documents to forward to the LLM,. If setting gpu layers to ~20 does nothing, then this is probably what just happened. In this notebook, we use the llama-2-chat-13b-ggml model, along with the. If it's not explicitly set when creating an instance of this class, it won't be included in the model parameters, and the model won't use the GPU. Season with salt and pepper to taste. 0，无需修. Then I finally switched to using the Q6_K GGML model with llamacpp, gpu offloading, and Mirostat sampling(2, 5, 0. On a 7B 8-bit model I get 20 tokens/second on my old 2070. cpp from source. gguf", verbose = False, n_ctx = 4096 * 4, n_gpu_layers = 20, n_batch = 20, streaming = True, ) llama_pandasai = PandasAI (llm = llama)Args: model_path: Path to the model. cpp for comparative testing. For any kwargs that need to be passed in during. The issue was already mentioned in #3436. py --model models/llama-2-70b-chat. Thread(target=job1) t2 = threading. Set thread count to match your core count. Similarly, if n_gqa or n_batch are set to values that are not compatible with the model or your system's resources, it could also lead to problems. To install the server package and get started: pip install llama-cpp-python [ server] python3 -m llama_cpp. In Python, when you define a method with async def, it becomes a coroutine that needs to be awaited using. To run some of the model layers on GPU, set the gpu_layers parameter: llm = AutoModelForCausalLM. Method 2: NVIDIA GPU Step 3: Configure the Python Wrapper of llama. 4. python server. param n_batch: Optional [int] = 8 ¶ Number of tokens to process in parallel. 然后 n_threads = 20， 实际测试效果仍然很慢，大概要2-3分钟。 等一个加速优化方案docs = db. Berlin. It seems that llama_free is not releasing the memory used by the previously used weights. server --model models/7B/llama-model. bin --color -c 2048 --temp 0. gguf --color -c 4096 --temp 0. By default GPU 0 is used. if values ["n_gpu_layers"] is not None: model_params. 62 mean that now it is working well with Apple Metal GPU (if setup as above) Which means langchain & llama. 1. cpp is concerned, GGML is now dead - though of course many third-party clients/libraries are likely to continue to support it for a lot longer. 7 --repeat_penalty 1. ggmlv3. I’m running the app locally, but, inside a Docker container deployed in an AWS machine with. Remember to click "Reload the model" after making changes. ggmlv3. Grammar should be integrated in not the llamacpp-python package now too and it is also in ooba now because of that. You can also interleave generation calls with plain. Great work @DavidBurela!. change this line of code to the number of layers needed case "LlamaCpp": llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=False, n_gpu_layers=40) this gives me a time of about 10 seconds to query pdf with about 20 pages with an rtx3090 using Wizard-Vicuna-13B-Uncensored. cpp: C++ implementation of llama inference code with weight optimization / quantization gpt4all: Optimized C backend for inference Ollama: Bundles model weights. gguf --temp 0. bin --n_predict 256 --color --seed 1 --ignore-eos --prompt "hello, my name is". Set "n-gpu-layers" to 40 (if this gives another CUDA out of memory error, try 35 instead) Set Threads to 8; See translation. I’m trying to install the llama-cpp-python package in Python, but I’m encountering an issue where the wheel building process gets stuck. You should be able to put about 40 layers in there, which should give you a big speed up versus just cpu. Using Metal makes the computation run on the GPU. llama-cpp-python already has the binding in 0. You can adjust the value based on how much memory your GPU can allocate. cpp项目进行编译，生成 . MPI BuildI was able to get GPU working with this Llama model: ggml-vic13b-q5_1. But for BN layer, my understanding is that it still synchronizes only the outputs of layers, not the means and vars. langchain. Squeeze a slice of lemon over the avocado toast, if desired. Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model. This is the pattern that we should follow and try to apply to LLM inference. If not: pip install --force-reinstall --ignore-installed --no-cache-dir llama-cpp-python==0. I have the Nvidia RTX 3060 Ti 8 GB Vram If None, the number of threads is automatically determined. It is now able to fully offload all inference to the GPU. If you have more VRAM, you can increase the number -ngl 18 to -ngl 24 or so, up to all 40 layers in llama 13B. # If using LlamaCpp model edit the case for LlamaCpp and change line to the following: llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, n_gpu_layers=40, callbacks=callbacks, verbose=False) # All I added was the n_gpu_layers=40 (40 seems to be max and uses a 9GB or VRAM), decreased layers. AFAIK the 7B models has 31 layers, which easily fit into my VRAM, as while chatting for a while using . For example, in my case (Since I have 8GB VRAM), I can set up to 31 layers maximum for a 13b model like MythoMax with 4k context. Now, I have an Nvidia 3060 graphics card and I saw that llama recently got support for gpu acceleration (honestly don't know what that really means, just that it goes faster by using your gpu) and found how to activate it by setting the "--n-gpu-layers" tag inside the webui. Using CPU alone, I get 4 tokens/second. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ago NeverEndingToast Any way to get the NVIDIA GPU performance boost from llama. In the LangChain codebase, the stream method in the BaseLLM. callbacks. ShinokuSon May 10. n_gpu_layers=32 # Change this value based on your model and your GPU VRAM pool. callbacks. gguf has 33 layers that can be offloaded to GPU. 1 -ngl 64 -mg 0 --image. ggmlv3. It may be more efficient to process in larger chunks. cpp compatible models with any OpenAI compatible client (language libraries, services, etc). With 8Gb and new Nvidia drivers, you can offload less than 15. For example, starting llama. Run the chat. Already have an account? Sign in to comment. llms. /llava -m ggml-model-q5_k. I have added multi GPU support for llama. I start the server as follow: git clone code for langchain. Support for --n-gpu-layers. If it is not working, then llama. Similar to Hardware Acceleration section above, you can also install with. cpp - threads 4, n_batch 512, n-gpu-layers 0, n_ctex 2048, no-mmap unticked, mlock ticked, seed 0 no extensions. As such, we should expect a ceiling of ~1 tokens/s for sampling from the 65B model with int4s, and 10 tokens/s with the 7B model. from langchain. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Pygmalion sponsoring the compute, and several other contributors. GPU acceleration is now available for Llama 2 70B GGML files, with both CUDA (NVidia) and Metal (macOS). Dosubot suggests that there are two possible reasons for this error: either the Llama model was not compiled with GPU support or the 'n_gpu_layers' argument is not being passed correctly. The package installs the command line entry point llamacpp-cli that points to llamacpp/cli. that provide optimal performance. 1. 512: n_parts: int: Number of parts to split the model into. 0. cpp/llamacpp_HF, set n_ctx to 4096. g. 0. Here’s the command I’m using to install the package: pip3. Well, how much memoery this. Stacking transformer layers to create large models results in better accuracies, few-shot learning capabilities, and even near-human emergent abilities on a. 78)If you don't know the answer, just say that you don't know, don't try to make up an answer. llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, verbose=False, n_gpu_layers=40) i have been testing this with langchain load_tools()/agents and serpapi, openai does a great job but so far the llama models are bit mad. You should see gpu being used. I recommend using the huggingface-hub Python library: pip3 install huggingface-hub>=0. Should be a number between 1 and n_ctx. cpp performance: 109. Default None. 3 participants. Start with a clear idea of the theme or emotion you want to convey. match model_type: case "LlamaCpp": # Added "n_gpu_layers" paramater to the function llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=False, n_gpu_layers=n_gpu_layers) 🔗 Download the modified privateGPT. 👍 2. Now start generating. model = Llama(**params). go-llama. --tensor_split TENSOR_SPLIT: Split the model across multiple GPUs. It provides higher-level APIs to inference the LLaMA Models and deploy it on local device with C#/. You will also want to use the --n-gpu-layers flag. 68. ### Response:" --gpu-layers 35 -n 100 -e --temp 0. docker run --gpus all -v /path/to/models:/models local/llama. Should be a number between 1 and n_ctx. 1 ・Windows 11 前回 1. n_batch = 512 # Should be between 1 and n_ctx, consider the amou nt of VRAM in your. py","contentType":"file"},{"name. I've compiled llama. Reply. cpp but with transformers samplers, and using the transformers tokenizer instead of the internal llama. closed. -mg i, --main-gpu i: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile.