Llama cpp multi gpu. Only works if llama-cpp-python was compiled with BLAS.
Llama cpp multi gpu It's my understanding that llama. cpp didn't support multi-gpu. cpp has now partial GPU support for ggml processing. I tested with TheBloke's 70B XWin and Airoboros GPTQs. Using CPU alone, I get 4 tokens/second. Not even from the same brand. Always respond as helpfully as possible, while being safe. 11, 2. Is this possible? At least for serial output, cpu cores are stalled as they are waiting for memory to arrive. From memory vs a 1-2 month old version of llama. cpp to use as much vram as it needs from this cluster Basic Vulkan Multi-GPU implementation by 0cc4m for llama. cpp supports multiple BLAS backends for faster processing. cpp innovations: with the Q4_0_4_4 CPU-optimizations, the Snapdragon X's CPU got 3x faster. cpp supports working distributed inference now. 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. 78 tokens/s How to run 30B/65B LLaMa-Chat on Multi-GPU Servers. . It won't use both gpus and will be slow but you will be able try the model. 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. 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. ; 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 Public. Reply reply faldore • • Any way to get the NVIDIA GPU performance boost from llama. 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 used 2048 ctx and tested dialog up to 10000 tokens - the model is still sane, no severe loops or serious problems. ) Yes, by default llama. I don't think it's ever worked. The most excellent JohannesGaessler GPU additions have been officially merged into ggerganov's game changing llama. As for Koboldcpp adopting GPU enabled llama. AutoGPTQ has much better oddball model support, however and can train. For Number of layers to offload to the GPU. The not performance-critical operations Has anyone managed to actually use multiple gpu for inference with llama. cpp GGUF is that the performance is equal to the The same issue has been resolved in llama. What this means for llama. cpp compiled with CLBLAST gives very poor performance on my system when I store layers into the VRAM. cpp via oobabooga doesn't load it to my gpu. 97 tokens/s = 2. Reply reply llama. cpp performance: 10. Intel GPU. 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. The same method works but for cublas when used the cublas instruction instead of clblast. 5,0. cpp officially supports GPU acceleration. It currently is limited to FP16, no quant support yet. There's loads of different ways of using llama. Method 2: NVIDIA GPU Single node, multiple GPUs. Hi i was wondering if there is any support for using llama. cpp for SYCL . 2023 and it isn't working for me there either. News github. cpp #5832 (9731134) I'm trying to load a model on two GPUs with Vulkan. Trending; LLaMA; After downloading a model, use the CLI tools to run it locally - see below. 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. cpp code base has substantially improved AI inference performance on NVIDIA GPUs, with ongoing work promising further You can use llama. 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. This command compiles the code using only the CPU. 62 tokens/s = 1. 39x AutoGPTQ 4bit performance on this system: 45 tokens/s 30B q4_K_S Previous llama. Share Add a Comment. Finish your install of llama. 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. Physical (or virtual) hardware you are using, e. 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. after building without errors. You can run a model across more than 1 machine. 6. CUDA ggerganov / llama. 5). I would try exllama first, it can run 65B parameter model in 40 to 45 gigabyte of vram on two GPUs. "General-purpose" is "bad". cpp: using only the CPU or leveraging the power of a GPU (in this case, NVIDIA). 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. Anyway, I'm running llama. Sort by: Best. cpp now supports GPU, but it's GPU/CPU split is way, way, way faster than ooba. cpp with oobabooga/text-generation? upvotes Basically, llama. python bindings, shell script, Rest server) etc - check examples directory Fast, lightweight, pure C/C++ HTTP server based on httplib, nlohmann::json and llama. However, When I do this, the models are split accross the 4 GPUs automatically. 1, evaluated llama-cpp-python versions: 2. cpp, but don't know if llama. LLM inference in C/C++. 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. 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. llama-cpp-python is a Python binding for llama. 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. cpp performance: 60. If you want the real speedups, you will need to offload layers onto the gpu. Added --instruct-preset gemma. Built on the GGML library released the previous year, llama. Only works if llama-cpp-python was compiled with BLAS. You switched accounts on another tab or window. #5720. cpp changes re-pack Q4_0 models automatically to accelerated Q4_0_4_4 when loading them on supporting arm CPUs (PR #9921). bat that comes with the one click installer. 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. 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 . Skip to content. Thought I'd ask here before downloading a GGUF version. cpp with ggml quantization to share the model between a gpu and cpu. 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. /main interactive mode from inside llama. 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. Question | Help I'm a newcomer to the realm of AI for personal utilization. For example: Multi-modal Models. lastrosade asked this question in Q&A. 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. cpp there is a setting for tensor_split for multi-gpu processing. ) on Intel XPU (e. cpp with ROCm; run any model with tensor split (tried 2 quantizations of 7B and 13B) get segfault; Failure Logs. 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. Two methods will be explained for building llama. cpp with GPU (CUDA) support, enabling users to maximize computational efficiency. 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. 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. There is always one CPU core at 100% utilization, but it may be nothing. server --model models/codellama-13b-instruct. Copy link Author. 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. Your best option for even bigger models is probably offloading with llama. cpp and the old MPI code has been removed. 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. Taking shortcuts and making custom hacks in favor of better performance is very welcome. Wrapyfi enables distributing LLaMA (inference only) on multiple GPUs/machines, each with less than 16GB VRAM. Open comment sort options but Vulkan and Cuda on llama. A 7B model should be small enough to fit comfortably tho. If you are looking for a step-wise approach for installing the llama-cpp-python When i run . 13, 2. cpp on the Snapdragon X CPU is faster than on the GPU or NPU. Context. I have a intel scalable gpu server, with 6x Nvidia P40 video cards with 24GB of VRAM each. Running llama. 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. amdgpu-install may have problems when combined with another package manager. At that point, I'll have a total of 16GB + 24GB = 40GB VRAM available for LLMs. On a 7B 8-bit model I get 20 tokens/second on my old 2070. Set this to 1000000000 to offload all layers to the GPU. Allows you to set the split mode used when running across multiple GPUs. Although I understand the GPU is better at running LLMs, VRAM is expensive, and I'm feeling greedy to run the 65B model. 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. A few days ago, rgerganov's RPC code was merged into llama. cpp or llama. Q5_K_M. 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. 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. GPTQ. cpp and other inference programs like ExLlama can split the work across multiple GPUs. Contribute to ggerganov/llama. cpp for Vulkan and it just runs. 2 and later versions already have concurrency support. cpp yet. 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. Llama. You can read more about the multi-GPU across GPU brands Vulkan support in this PR. 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. This notebook goes over how to run llama-cpp-python within LangChain. -o and -v, all options can be specified multiple times to run multiple tests. 62 MiB offloadi Subreddit to discuss about Llama, the large language model created by Meta AI. cpp performance: 25. If you run into issues compiling with ROCm, try using cmake instead of make. 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. Been running some tests and noticed a few command line options in llama cpp that I hadn’t spotted before. cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). Recent llama. Notifications You must be signed in to change notification settings; Fork 10k; Star 69. 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. But even without it I think llama. cpp and bank on clblas. [3] [14] [15] llama. Note: new versions of llama-cpp-python use GGUF model files (see here). 51 tokens/s New PR llama. And The last time I looked, the OpenCL implementation of llama. You signed out in another tab or window. If yes, please enjoy the magical features of LLM by llama. cpp I think both --split-mode row and --split-mode You can use llama. , local PC I just wanted to point out that llama. cpp will only use a single thread, regardless of the --threads argument. This method only requires using the make command inside the cloned repository. com Open. 2b. Q4_K_M. Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc. This isn't that big of a deal, but helps when you are experimenting with multiple models. 16GB of VRAM for under $300. 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. Is there a way to configure this to be using fp16 or thats already baked into the existing model. cpp; Open the repo folder and run the command make clean & GGML_CUDA=1 make libllama. 19 with cuBLAS backend There are two AMDW6800 graphics cards on the current machine. cpp requires the model to be stored in the GGUF file format. Like if you fit even half the model in VRAM, you'll probably get at least twice the speed of CPU processing. Using the latest llama. cpp code base was originally released in 2023 as a lightweight but efficient framework for performing inference on Meta Llama models. lastrosade Feb 26, 2024 · 1 There is detailed guide in 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. 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. Both of them are recognized by llama. How can I programmatically check if llama-cpp-python is installed with support for a CUDA-capable GPU?. 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. 0 (clang-1500. Not sure how long they’ve been there, but of most interest was the -sm option. Please ensure you generate the question based on the given context only <</SYS>> generate 3 questions based on the given content:-{}. I've been fighting to get multi-GPU working all evening here. There may be a motherboard setting named something like Above 4G When loading a model with llama. What worked for me that enabled the GPU is installing CUDA version of llama-cpp-python that is compatible with your CUDA Hello, llama. So now running llama. 3. Each pp and tg test is run with all combinations of the specified options. It rocks. 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. This allows you to parallelize the process across This guide will focus on the latest Llama 3. cpp brings all Intel GPUs to LLM developers and users. 0. so; Clone git repo llama-cpp-python; Copy the llama. cpp for SYCL. 5 which allow the language . Set of LLM REST APIs and a simple web front end to interact with llama. cpp:. cpp is pretty fast till you get over 4k context, can use all GPU and has a python implementation too. i have followed the instructions of clblast build by using env cmd_windows. 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. There are two ways to do this: Use -sm none -mg <gpu> in the command line. It's neck and neck with exllama for multi card. 0cc4m has more numbers. md. But the LLM just prints a bunch of # tokens. cpp (e. This means that you can choose how many layers run on CPU and how many run on GPU. cpp docker image I just got 17. 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. cpp normally by compiling with LLAMA_HIPBLAS=1 and enjoy! Additional Notes: Disable CSM in BIOS if you are having trouble detecting your GPU. To convert existing GGML models to GGUF you Added multiple gpu (--main-gpu 0, --split-mode none, --tensor-split 0. I could settle for the 30B, but I can't for any less. Regardless, since I did get better performance with this loader, I figured I should share these results. 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. You need to use n_gpu_layers in the initialization of Llama(), which offloads some of the work to the GPU. LLaMa (short for "Large Language Model Meta AI") is a collection of pretrained state-of-the-art large language models, developed by Meta AI. currently distributes on two cards only using ZeroMQ. cpp doesn't support multi gpu yet, so probably not. The SYCL backend in llama. 79 tokens/s New PR llama. I'm not a maintainer here, but in case it helps: I think the instructions are in the READMEs too. 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. Using Llama. cpp on Intel GPUs. cpp. 9. After about 2 months, SYCL backend has been added more features, like windows building, multiple cards, set main GPU and more OPs. cpp development by creating an account on GitHub. It should at some point. cpp with AMD GPU is there a ROCM implementation ? Hi i was wondering if there is any support for using llama. cpp, it works on gpu. So llama. The other option is to use kobold. gguf model. Reload to refresh your session. Code; Issues 256; Multi GPU with Vulkan out of memory issue. 5-2 t/s This article aims to provide a comprehensive guide to building Llama. The open-source llama. Any idea why ? How many layers am I supposed to store in VRAM ? My config : OS : L Does llama. python3 -m llama_cpp. 5-2 t/s with 6700xt (12 GB) running WizardLM Uncensored 30B. This is fine. cpp context shifting is working great by default. @ccbadd Have you tried it? I checked out llama. 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. I could get them both into VRAM entirely on The Hugging Face platform hosts a number of LLMs compatible with llama. Llama remembers everything from a start prompt and from the So thanks to the multi-gpu support, llama. 4 tokens/second on this synthia-70b-v1. For detailed info, please refer to llama. This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project. I happen to possess several AMD Radeon RX 580 8GB GPUs that are currently idle. In addition, when all 2 GPUs are visible, tensor_split option doesnt work as expected, since nvidia-smi shows, that both GPUs are used. And depending on the state of that there likely is a segmentation fault during one of the memcpys between devices. Clone git repo llama. By default if you compiled with GPU support some calculations will be offloaded to the GPU during inference. Speed and recent llama. Environment and Context. Overview Llama multi GPU I have Llama2 running under LlamaSharp (latest drop, 10/26) and CUDA-12. cpp folder into the llama-cpp-python/vendor; Open the llama-cpp A core dump would probably not be of much use. I'm fairly It has support for multiple GPU fine-tuning and Quantized LoRA (int8, int4, and int2 coming soon). Will support The model is initialized with main_gpu=0, tensor_split=None. -sm none disables multi GPU and -mg selects the GPU to use. 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. for Linux: Intel(R) Core(TM) i7-8700K CPU @ 3. You've quote the make instructions - but you may find cmake instructions work better. It basically splits the workload between CPU + ram and GPU + vram, the performance is not great but still better than multi-node inference. Now that it works, I can download more new format models. /llama-cli build: 0 (unknown) with Apple clang version 15. Only the CUDA implementation does. llama-cpp-python supports such as llava1. Detailed MacOS Metal GPU install documentation is available at docs/install/macos. 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. 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. cpp performance: 18. 4) for x86_64-apple-darwin22. 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. 2 model, published by Meta on Sep 25th 2024, Meta's Llama 3. cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. I took a screen capture of the Task Manager running while the model was answering questions and thought I'd provide you 8XXD8 changed the title Row split is not working Multi GPU --split-mode row speed regression Apr 6, 2024. So now llama. Exploring Local Multi-GPU Setup for AI: Harnessing AMD Radeon RX 580 8GB for Efficient AI Model . 73x AutoGPTQ 4bit performance on the same system: 20. Building llama. How can I specify for llama. cpp do layer splitting by default now, Use llama. I have workarounds. Method 1: CPU Only. 56 BPW) with -ts "20,11" -c 512 yields: ggml ctx size = 0. Multi GPU with Vulkan out of memory issue. g. cpp is capable of running large models on multiple GPUs. 2x A100 GPU server, cuda 12. Compared to the famous ChatGPT, the LLaMa models are available for download and can be run on available hardware. I have a Linux system with 2x Radeon RX 7900 XTX. Ollama 0. cpp propagates to llama-cpp-python in time. Sometimes closer to $200. llama. Multiple AMD GPU support isn't working for me. cpp from early Sept. If you have enough VRAM, just put an arbitarily high number, or decrease it until you don't get out of VRAM errors. llm_load_tensors: offloaded 0/35 layers to GPU. Closed Unanswered. It supports inference for many LLMs models, which can be accessed on Hugging Face. I don't think there is a better value for a new GPU for LLM inference than the A770. cpp does have implemented peer transfers and they can significantly speed up inference. cpp with dual 3090 with NVLink enabled. It's a work in progress and has limitations. Trying to run the 7B model in Colab with 15GB GPU is failing. 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. My GPUs have 20 and 11 gigs of VRAM Loading a Q6_K quant of size 26. Navigation Menu Toggle navigation. 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. In this post, I showed how the introduction of CUDA Graphs to the popular llama. 4k. 70GHz Question. On multi-gpu: Almost same, but mul with hip/cublasSgemm, and return WITHOUT convertion to f32. And I think an awesome future step would be to support multiple GPUs. 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. This is what I'm talking about. Navigate to 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. Sign in Multi-GPU: N/A: N/A: N/A: LM Studio (a wrapper around llama. 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. Instructions to build llama are in the main readme here. You signed in with another tab or window. This is a breaking change. I'm able to get about 1. Only odd man out is AutoGPTQ and now AWQ because they're still using accelerate to split up models for that slow ride. 2 goes small and multimodal with 1B, 3B, 11B and 90B models. Has anyone managed to actually use multiple gpu for inference with llama. cpp gained traction with users who lacked specialized hardware as it could run on just a 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. Matrix multiplications, which take up most of the runtime are split across all available GPUs by default. Hi there, I ended up went with single node multi-GPU setup 3xL40. How to properly use llama. 3090 Ti and a P40, for a total of 48GB of VRAM, and 128 GB of main system RAM. 27 GiB (6. xijpnutgohjrsgpptnjfokwcjqcknphptmdicmurmywrdcngeaqlfkppx
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