Llama cpp multi gpu. As for Koboldcpp adopting GPU enabled llama.

Llama cpp multi gpu Contribute to ggerganov/llama. A 7B model should be small enough to fit comfortably tho. In this post, I showed how the introduction of CUDA Graphs to the popular llama. cpp compiled with CLBLAST gives very poor performance on my system when I store layers into the VRAM. Method 2: NVIDIA GPU Single node, multiple GPUs. AutoGPTQ has much better oddball model support, however and can train. I took a screen capture of the Task Manager running while the model was answering questions and thought I&#39;d provide you 8XXD8 changed the title Row split is not working Multi GPU --split-mode row speed regression Apr 6, 2024. This problem limits multi GPU performance too, row split uses two threads, but 2 GPUs peg the cores at 100% and a third GPU reduces token generation speed. after building without errors. cpp, it works on gpu. I could get them both into VRAM entirely on The Hugging Face platform hosts a number of LLMs compatible with llama. If you are looking for a step-wise approach for installing the llama-cpp-python When i run . cpp from early Sept. The most excellent JohannesGaessler GPU additions have been officially merged into ggerganov's game changing llama. Has anyone managed to actually use multiple gpu for inference with llama. Each pp and tg test is run with all combinations of the specified options. The open-source llama. But as far as I tested and understand, the GPUs have to be on the same machine, and to my knowledge there is no multi-node multi-gpu implementation for llama. 73x AutoGPTQ 4bit performance on the same system: 20. 4k. ; Set the CUDA_VISIBLE_DEVICES environment variable to the GPU that you want to use; In my experience, setting CUDA_VISIBLE_DEVICES results in slightly better performance, but the difference should be minor. cpp doesn't support multi gpu yet, so probably not. 13, 2. The other option is to use kobold. After about 2 months, SYCL backend has been added more features, like windows building, multiple cards, set main GPU and more OPs. Reload to refresh your session. I'm sure many people have their old GPUs either still in their rig or lying around, and those GPUs I have added multi GPU support for llama. cpp and other inference programs like ExLlama can split the work across multiple GPUs. cpp with AMD GPU is there a ROCM implementation ? Hi i was wondering if there is any support for using llama. Recent llama. It rocks. The not performance-critical operations Has anyone managed to actually use multiple gpu for inference with llama. Sometimes closer to $200. Hi there, I ended up went with single node multi-GPU setup 3xL40. If you run into issues compiling with ROCm, try using cmake instead of make. cpp to use as much vram as it needs from this cluster Basic Vulkan Multi-GPU implementation by 0cc4m for llama. [3] [14] [15] llama. If it worked with the physical link the problem likely has to do with peer access getting automatically enabled/disabled based on the HIP implementation of cudaCanAccessPeer. Allows you to set the split mode used when running across multiple GPUs. cpp performance: 60. cpp will only use a single thread, regardless of the --threads argument. cpp gained traction with users who lacked specialized hardware as it could run on just a Llama. What this means for llama. . 2 goes small and multimodal with 1B, 3B, 11B and 90B models. Taking shortcuts and making custom hacks in favor of better performance is very welcome. Trying to run the 7B model in Colab with 15GB GPU is failing. python3 -m llama_cpp. Here’s how you can run these models on various AMD hardware configurations and a step-by-step installation guide for Ollama on both Linux and Windows Operating Systems on Radeon GPUs. cpp already does some "manual workarounds" for what underlying libs do not provide, thus Hey Guys, I have a multiple AMD GPU setup and have run into a bit of trouble with transformers + accelerate. Sort by: Best. cpp and the old MPI code has been removed. There are currently 4 backends: OpenBLAS, cuBLAS (Cuda), CLBlast (OpenCL), and an experimental fork for HipBlas (ROCm) from llama-cpp-python repo: Installation with OpenBLAS / cuBLAS / CLBlast. llama. How to properly use llama. Two methods will be explained for building llama. 4) for x86_64-apple-darwin22. cpp with dual 3090 with NVLink enabled. 97 tokens/s = 2. Running llama. I used 2048 ctx and tested dialog up to 10000 tokens - the model is still sane, no severe loops or serious problems. cpp for Vulkan and it just runs. At that point, I'll have a total of 16GB + 24GB = 40GB VRAM available for LLMs. 2x A100 GPU server, cuda 12. Is there a way to configure this to be using fp16 or thats already baked into the existing model. but if you're having multiple python processes running concurrently on the GPU, you need Examples Agents Agents 💬🤖 How to Build a Chatbot GPT Builder Demo Building a Multi-PDF Agent using Query Pipelines and HyDE Step-wise, Controllable Agents Lllama. However, When I do this, the models are split accross the 4 GPUs automatically. i have followed the instructions of clblast build by using env cmd_windows. Been running some tests and noticed a few command line options in llama cpp that I hadn’t spotted before. -sm none disables multi GPU and -mg selects the GPU to use. I tested with TheBloke's 70B XWin and Airoboros GPTQs. Please ensure you generate the question based on the given context only <</SYS>> generate 3 questions based on the given content:-{}. LLM inference in C/C++. LLaMa (short for "Large Language Model Meta AI") is a collection of pretrained state-of-the-art large language models, developed by Meta AI. This notebook goes over how to run llama-cpp-python within LangChain. Multiple AMD GPU support isn't working for me. cpp Public. For detailed info, please refer to llama. For example: Multi-modal Models. So now llama. This isn't that big of a deal, but helps when you are experimenting with multiple models. cpp do layer splitting by default now, Use llama. cpp via oobabooga doesn't load it to my gpu. Will support The model is initialized with main_gpu=0, tensor_split=None. The SYCL backend in llama. 0 (clang-1500. 56 BPW) with -ts "20,11" -c 512 yields: ggml ctx size = 0. Ollama 0. Code; Issues 256; Multi GPU with Vulkan out of memory issue. This allows you to parallelize the process across This guide will focus on the latest Llama 3. In addition, when all 2 GPUs are visible, tensor_split option doesnt work as expected, since nvidia-smi shows, that both GPUs are used. Reply reply llama. cpp code base has substantially improved AI inference performance on NVIDIA GPUs, with ongoing work promising further You can use llama. Added --instruct-preset gemma. cpp: using only the CPU or leveraging the power of a GPU (in this case, NVIDIA). Note: new versions of llama-cpp-python use GGUF model files (see here). cpp code, someone posted a note from the dev of Koboldcpp yesterday indicating that he wasn't Also try it for image generation through something like StableSwarm which can use multi-gpu. It supports inference for many LLMs models, which can be accessed on Hugging Face. If yes, please enjoy the magical features of LLM by llama. cpp log: if the first memory region of a GPU doesn't span the entire amount of VRAM then peer to peer transfers for multi-gpu won't work. ) Yes, by default llama. Llama. so; Clone git repo llama-cpp-python; Copy the llama. cpp development by creating an account on GitHub. Notifications You must be signed in to change notification settings; Fork 10k; Star 69. Finish your install of llama. 2 and later versions already have concurrency support. Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc. This command compiles the code using only the CPU. Q4_K_M. cpp GGUF is that the performance is equal to the The same issue has been resolved in llama. Closed Unanswered. amdgpu-install may have problems when combined with another package manager. gguf model. I am considering upgrading the CPU instead of the GPU since it is a more cost-effective option and will allow me to run larger models. 0. Intel GPU. Is there any way to specify which models are loaded on which devices? I would like to load each model fully onto a single GPU, having model one fully loaded on GPU 0, model 2 on GPU 1, and so on, wihtout splitting a single model accross multiple GPUs. 2023 and it isn't working for me there either. Hi i was wondering if there is any support for using llama. Detailed MacOS Metal GPU install documentation is available at docs/install/macos. What if you don't have a beefy multi-GPU workstation/server? Don't worry, this tutorial explains how to use mpirun to launch an LLaMA inference job across multiple cloud instances (one or more GPUs on each The latest oobabooga commit has issues with multi gpu llama and the older commit with the older llama version doesn’t support deepseekcoder yet. I'm able to get about 1. cpp on Intel GPUs. The same method works but for cublas when used the cublas instruction instead of clblast. It's a work in progress and has limitations. In my program, I am trying to warn the developers when they fail to configure their system in a way that allows the llama-cpp-python LLMs to leverage GPU acceleration. 39x AutoGPTQ 4bit performance on this system: 45 tokens/s 30B q4_K_S Previous llama. If you want the real speedups, you will need to offload layers onto the gpu. cpp performance: 18. server --model models/codellama-13b-instruct. I could settle for the 30B, but I can't for any less. cpp normally by compiling with LLAMA_HIPBLAS=1 and enjoy! Additional Notes: Disable CSM in BIOS if you are having trouble detecting your GPU. 78 tokens/s How to run 30B/65B LLaMa-Chat on Multi-GPU Servers. cpp for SYCL . 6. cpp innovations: with the Q4_0_4_4 CPU-optimizations, the Snapdragon X's CPU got 3x faster. M1 Mac Performance Issue Note: If you are using Apple Silicon (M1) Mac, make sure you have installed a version of Python that supports arm64 architecture. From memory vs a 1-2 month old version of llama. cpp performance: 10. Sign in Multi-GPU: N/A: N/A: N/A: LM Studio (a wrapper around llama. /main interactive mode from inside llama. Any idea why ? How many layers am I supposed to store in VRAM ? My config : OS : L Does llama. Building llama. cpp with oobabooga/text-generation? upvotes Basically, llama. llama-cpp-python supports such as llava1. cpp supports working distributed inference now. cpp supports multiple BLAS backends for faster processing. You signed in with another tab or window. I don't think there is a better value for a new GPU for LLM inference than the A770. I happen to possess several AMD Radeon RX 580 8GB GPUs that are currently idle. py models/llama-2-7b/ Now for the final stage run this to run the model (Keep in mind you can play around --n-gpu-layers and -n in order to see what is working the best for you) Despite being more memory efficient than previous language foundation models, LLaMA still requires multiple-GPUs to run inference with. gguf --n_gpu_layers 45 ggml_cuda_set_main_device: using device 0 (AMD Radeon PRO W6800) as main device You signed in with another tab or window. 3. It won't use both gpus and will be slow but you will be able try the model. 5). Considering that the person who did the OpenCL implementation has moved onto Vulkan and has said that the future is Vulkan, I don't think clblast will ever have multi-gpu support. What worked for me that enabled the GPU is installing CUDA version of llama-cpp-python that is compatible with your CUDA Hello, llama. Navigate to llama. You need to use n_gpu_layers in the initialization of Llama(), which offloads some of the work to the GPU. The optimization for memory stalls is Hyperthreading/SMT as a context switch takes longer than memory stalls anyway, but it is more designed for scenarios where threads access unpredictable memory locations rather than saturate memory bandwidth. With 88GB of usable memory between one 3090 and ggml_metal_init: allocating ggml_metal_init: found device: AMD Radeon VII METAL_DEVICE_WRAPPER_TYPE=0 . I have a intel scalable gpu server, with 6x Nvidia P40 video cards with 24GB of VRAM each. And depending on the state of that there likely is a segmentation fault during one of the memcpys between devices. At the same time, you can choose to keep some of the layers in system RAM and have the CPU do part of the computations—the main purpose is to avoid VRAM overflows. Overview Llama multi GPU I have Llama2 running under LlamaSharp (latest drop, 10/26) and CUDA-12. for Linux: Intel(R) Core(TM) i7-8700K CPU @ 3. Method 1: CPU Only. cpp support uneven split of GBs/layers between multiple GPUs? (I have slow-ish internet connection so it took ages to DL a big AWQ model. cpp now supports GPU, but it's GPU/CPU split is way, way, way faster than ooba. Compared to the famous ChatGPT, the LLaMa models are available for download and can be run on available hardware. Your best option for even bigger models is probably offloading with llama. 62 MiB offloadi Subreddit to discuss about Llama, the large language model created by Meta AI. Physical (or virtual) hardware you are using, e. Exploring Local Multi-GPU Setup for AI: Harnessing AMD Radeon RX 580 8GB for Efficient AI Model . cpp. 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. 11, 2. There is always one CPU core at 100% utilization, but it may be nothing. A few days ago, rgerganov's RPC code was merged into llama. If you have enough VRAM, just put an arbitarily high number, or decrease it until you don't get out of VRAM errors. cpp and bank on clblas. I've been fighting to get multi-GPU working all evening here. ) on Intel XPU (e. cpp for SYCL. cpp will offload to each GPU a fraction of the model proportional to the amount of free memory available on the Multi-GPU works fine in my repo. cpp does have implemented peer transfers and they can significantly speed up inference. How can I programmatically check if llama-cpp-python is installed with support for a CUDA-capable GPU?. It should at some point. For Number of layers to offload to the GPU. cpp:. Features: LLM inference of F16 and quantized models on GPU and CPU; OpenAI API compatible chat completions and embeddings routes; Reranking endoint (WIP: #9510) I have 4x 2080Ti 22G, it run very well, the model split to multi gpu ollama's backend llama. @ccbadd Have you tried it? I checked out llama. Suppose I buy a Thunderbolt GPU dock like a TH3P4G3 and put a 3090/4090 with 24GB VRAM in it, then connect it to the laptop via Thunderbolt. cpp changes re-pack Q4_0 models automatically to accelerated Q4_0_4_4 when loading them on supporting arm CPUs (PR #9921). cpp performance: 25. cpp officially supports GPU acceleration. 27 GiB (6. Thought I'd ask here before downloading a GGUF version. News github. 19 with cuBLAS backend There are two AMDW6800 graphics cards on the current machine. cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. cpp (e. Context. Anyway, I'm running llama. Share Add a Comment. Copy link Author. I have a Linux system with 2x Radeon RX 7900 XTX. cpp does not support concurrent processing, so you can run 3 instance 70b-int4 on 8x RTX 4090, set a haproxy/nginx load balancer for ollama api to improve performance. 3090 Ti and a P40, for a total of 48GB of VRAM, and 128 GB of main system RAM. md. Always respond as helpfully as possible, while being safe. Flag Description For multi-gpu, write the numbers separated by from llama_cpp import Llama def question_generator(context): prompt = """[INST] <<SYS>> You are a helpful, respectful and honest assistant. Matrix multiplications, which take up most of the runtime are split across all available GPUs by default. Built on the GGML library released the previous year, llama. Llama remembers everything from a start prompt and from the So thanks to the multi-gpu support, llama. Only odd man out is AutoGPTQ and now AWQ because they're still using accelerate to split up models for that slow ride. cpp ? When a model Doesn't fit in one gpu, you need to split it on multiple GPU, sure, but when a small model is split between multiple gpu, it's just slower than when it's running on one GPU. cpp with AMD GPU is there a ROCM implementation ? If clinfo shows multiple devices, you can use GGML_OPENCL_PLATFORM to select the correct driver. , local PC I just wanted to point out that llama. But the LLM just prints a bunch of # tokens. GPTQ. cpp requires the model to be stored in the GGUF file format. Not even from the same brand. cpp on the Snapdragon X CPU is faster than on the GPU or NPU. 0cc4m has more numbers. It doesn't gain more performance from having multiple GPUs (they work in turn, not in parallel) but it does split the weights so you can take advantage of the extra VRAM. Reply reply faldore • • Any way to get the NVIDIA GPU performance boost from llama. Using CPU alone, I get 4 tokens/second. Multi GPU with Vulkan out of memory issue. How can I specify for llama. cpp is pretty fast till you get over 4k context, can use all GPU and has a python implementation too. g. Regardless, since I did get better performance with this loader, I figured I should share these results. Clone git repo llama. Llama 3 8B Instruct loads fine and produces sensible output when I use just one card, but when I change to For instance, if the model fits into a single GPU, you can create multiple GPU server instances on a single server using different port numbers. It's neck and neck with exllama for multi card. cpp there is a setting for tensor_split for multi-gpu processing. lastrosade Feb 26, 2024 · 1 There is detailed guide in llama. Please check if your Intel laptop has an iGPU, your gaming PC has an Intel Arc GPU, or your cloud VM has Intel Data Center GPU Max and Flex Series GPUs. On a 7B 8-bit model I get 20 tokens/second on my old 2070. 70GHz Question. Now that it works, I can download more new format models. Only works if llama-cpp-python was compiled with BLAS. cpp brings all Intel GPUs to LLM developers and users. build_commit,build_number,cuda,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size Although before I invest into a new GPU, I would like to verify that it actually works, since conventional wisdom used to be that SLI only doubled performance, not memory. I have workarounds. cpp with ggml quantization to share the model between a gpu and cpu. Is this possible? At least for serial output, cpu cores are stalled as they are waiting for memory to arrive. There are two ways to do this: Use -sm none -mg <gpu> in the command line. cpp #5832 (9731134) I'm trying to load a model on two GPUs with Vulkan. Only the CUDA implementation does. cpp I think both --split-mode row and --split-mode You can use llama. cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). cpp with ROCm; run any model with tensor split (tried 2 quantizations of 7B and 13B) get segfault; Failure Logs. llm_load_tensors: offloaded 0/35 layers to GPU. The CUDA Toolkit includes the drivers and software development kit (SDK) required to Regrettably, I couldn't get the loader to operate with both GPUs. I'm not a maintainer here, but in case it helps: I think the instructions are in the READMEs too. For example, they may have installed the library using pip install llama-cpp The first step in enabling GPU support for llama-cpp-python is to download and install the NVIDIA CUDA Toolkit. python bindings, shell script, Rest server) etc - check examples directory Fast, lightweight, pure C/C++ HTTP server based on httplib, nlohmann::json and llama. As for Koboldcpp adopting GPU enabled llama. cpp folder into the llama-cpp-python/vendor; Open the llama-cpp A core dump would probably not be of much use. Environment and Context. #5720. This is fine. cpp ? When a model Doesn't fit in one gpu, you need to split it on multiple GPU, sure, but when a small model is In this article we will describe how to run the larger LLaMa models variations up to the 65B model on multi-GPU hardware and show some differences in achievable text quality regarding the different model sizes. cpp If the model is too big for your VRAM make sure to decrease the layers and try to restart the runtime to prevent multiple models being loaded at the same time. /llama-cli build: 0 (unknown) with Apple clang version 15. So now running llama. Both of them are recognized by llama. 16GB of VRAM for under $300. After the CUDA refactor PR #1703 by @JohannesGaessler was merged i wanted to try it out this morning and measure the performance difference on my ardware. 4 tokens/second on this synthia-70b-v1. 0 main: llama backend init main: load the model and apply lora adapter, if any llama_load_model_from_file: using device Metal (AMD A walk through to install llama-cpp-python package with GPU capability (CUBLAS) to load models easily on to the GPU. 2 model, published by Meta on Sep 25th 2024, Meta's Llama 3. And I think an awesome future step would be to support multiple GPUs. This method only requires using the make command inside the cloned repository. Using the latest llama. I tinkered with gpu-split and researched the topic, but it seems to me that the loader (at least the version I tested) hasn't fully integrated multi-GPU inference. It basically splits the workload between CPU + ram and GPU + vram, the performance is not great but still better than multi-node inference. cpp didn't support multi-gpu. cpp docker image I just got 17. cpp has now partial GPU support for ggml processing. You signed out in another tab or window. You can run a model across more than 1 machine. com Open. currently distributes on two cards only using ZeroMQ. Trending; LLaMA; After downloading a model, use the CLI tools to run it locally - see below. Although I understand the GPU is better at running LLMs, VRAM is expensive, and I'm feeling greedy to run the 65B model. -o and -v, all options can be specified multiple times to run multiple tests. 79 tokens/s New PR llama. gjmulder changed the title Set gpu device Set GPU device on multi-GPU systems May 30, 2023 gjmulder closed this as completed May 30, 2023 pseudotensor mentioned this issue Oct 7, 2023 So you just have to compile llama. So llama. cpp is capable of running large models on multiple GPUs. To convert existing GGML models to GGUF you Added multiple gpu (--main-gpu 0, --split-mode none, --tensor-split 0. This is what I'm talking about. 5-2 t/s with 6700xt (12 GB) running WizardLM Uncensored 30B. Navigation Menu Toggle navigation. So I had no experience with multi node multi gpu, but far as I know, if you’re playing LLM with huggingface, you can look at the device_map or TGI (text generation inference) or torchrun’s MP When the entire model is offloaded to the GPU, llama. Instructions to build llama are in the main readme here. You switched accounts on another tab or window. 1, evaluated llama-cpp-python versions: 2. I would try exllama first, it can run 65B parameter model in 40 to 45 gigabyte of vram on two GPUs. And The last time I looked, the OpenCL implementation of llama. bat that comes with the one click installer. cpp with GPU (CUDA) support, enabling users to maximize computational efficiency. For example, we can have a tool like ggml-cuda-llama which is a very custom ggml translator to CUDA backend which works only with LLaMA graphs and nothing else, but does some very LLaMA-specific optimizations. I don't think it's ever worked. cpp or llama. By default if you compiled with GPU support some calculations will be offloaded to the GPU during inference. cpp code base was originally released in 2023 as a lightweight but efficient framework for performing inference on Meta Llama models. It's my understanding that llama. My GPUs have 20 and 11 gigs of VRAM Loading a Q6_K quant of size 26. 51 tokens/s New PR llama. 5,0. Like if you fit even half the model in VRAM, you'll probably get at least twice the speed of CPU processing. cpp yet. cpp quickly became attractive to many users and developers (particularly for use on personal workstations) due to its focus on C/C++ without Compile llama. This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project. Also, I couldn't get it to work with Ranges:4 Features: KERNEL_DISPATCH Fast F16 Operation: TRUE Wavefront Size: 32(0x20) Workgroup Max Size: 1024(0x400) Workgroup Max Size per Dimension: x 1024(0x400) y 1024(0x400) z 1024(0x400) Max Waves Per CU: 32(0x20) Max Work-item Per CU: 1024(0x400) Grid Max Size: 4294967295(0xffffffff) Grid Max Size per Dimension: x Previous llama. 62 tokens/s = 1. 5 which allow the language . Open comment sort options but Vulkan and Cuda on llama. Set this to 1000000000 to offload all layers to the GPU. cpp, but don't know if llama. llama-cpp-python is a Python binding for llama. cpp propagates to llama-cpp-python in time. cpp with multiple NVIDIA GPUs with different CUDA compute engine versions? I have an RTX 2080 Ti 11GB and TESLA P40 24GB in my I know that supporting GPUs in the first place was quite a feat. 9. Speed and recent llama. CUDA ggerganov / llama. 5-2 t/s This article aims to provide a comprehensive guide to building Llama. I'm fairly It has support for multiple GPU fine-tuning and Quantized LoRA (int8, int4, and int2 coming soon). Skip to content. You've quote the make instructions - but you may find cmake instructions work better. cpp root of the project (I was not able to run 7b as is as I have not enough GPU memory, I was able only after I had quantized it) python3 convert. But even without it I think llama. When I run LlamaCppEmbeddings from LangChain and the same model (7b quantized ), it doesnt work on gpu and takes around 4minutes to answer a question using the RetrievelQAChain. This is a breaking change. Not sure how long they’ve been there, but of most interest was the -sm option. cpp; Open the repo folder and run the command make clean & GGML_CUDA=1 make libllama. This means that you can choose how many layers run on CPU and how many run on GPU. You can read more about the multi-GPU across GPU brands Vulkan support in this PR. There's loads of different ways of using llama. Set of LLM REST APIs and a simple web front end to interact with llama. cpp context shifting is working great by default. 2b. Wrapyfi enables distributing LLaMA (inference only) on multiple GPUs/machines, each with less than 16GB VRAM. Question | Help I'm a newcomer to the realm of AI for personal utilization. lastrosade asked this question in Q&A. I use my standard prompts with different models in I think we need to solve for this, models are automatically loaded and split on multiple GPUs if you have BaseMosaic enabled in your XORG config, overriding the default flags that you can explicitly set as your main GPU. On multi-gpu: Almost same, but mul with hip/cublasSgemm, and return WITHOUT convertion to f32. Q5_K_M. "General-purpose" is "bad". cpp) offers a setting for selecting the number of layers that can be offloaded to the GPU, with 100% making the GPU the sole processor. It currently is limited to FP16, no quant support yet. Using Llama. There may be a motherboard setting named something like Above 4G When loading a model with llama. lzoqv ldhm ylpon jtghy hxtdkv fjpue puxv eiuw rlcm rig