Ollama mac gpu reddit. This thing is a dumpster fire. I have an opportunity to get a mac pro for decent price with AMD Radeon Vega Pro Duo 32gb. 1 t/s (Apple MLX here reaches 103. I expect the MacBooks to be similar. 084358s prompt eval rate: 120. OLLAMA_ORIGINS A comma separated list of allowed origins. This article will explain the problem, how to detect it, and how to get your Ollama workflow running with all of your VRAM (w Jan 6, 2024 · Download the ollama_gpu_selector. Install the Nvidia container toolkit. I have an ubuntu server with a 3060ti that I would like to use for ollama, but I cannot get it to pick it up. Also I’d be a n00b Mac user so Firstly, this is interesting, if only as a reference point in the development of the GPU capability and the gaming developer kit. AMD is playing catch up but we should be expecting big jumps in performance. Assuming you have a supported Mac supported GPU. cpp for iPhones/iPads. In this implementation, there's also I/O between the CPU and GPU. 9gb (num_gpu 22) vs 3. Also, Ollama provide some nice QoL features that are not in llama. 3 times. What is palæontology? Literally, the word translates from Greek παλαιός + ον + λόγος [ old + being + science ] and is the science that unravels the æons-long story of life on the planet Earth, from the earliest monera to the endless forms we have now, including humans, and of the various long-dead offshoots that still inspire today. Well, exllama is 2X faster than llama. It seems like a MAC STUDIO with an M2 processor and lots of RAM may be the easiest way. It has 16 GB of RAM. sh. (needs to be at the top of the Modelfile) You then add the PARAMETER num_gpu 0 line to make ollama not load any model layers to the GPU. If part of the model is on the GPU and another part is on the CPU, the GPU will have to wait on the CPU which functionally governs it. 185799541s prompt eval count: 612 token(s) prompt eval duration: 5. com. If you start using 7B models but decide you want 13B models. Overview. I don't even swap. 5 on mistral 7b q8 and 2. 1 "Summarize this file: $(cat README. But you can get Ollama to run with GPU support on a Mac. Try to find eGPU that you can easily upgrade GPU so as you start using different Ollama models and you'll have the option to get bigger and or faster GPU as your needs chance. On linux, after a suspend/resume cycle, sometimes Ollama will fail to discover your NVIDIA GPU, and fallback to running on the CPU. Easier to upgrade, you'll get more flexibility is RAM and GPU options. Aug 17, 2023 · It appears that Ollama currently utilizes only the CPU for processing. 3B, 4. wired_limit_mb=0. yaml -f docker-compose. Oct 5, 2023 · docker run -d -v ollama:/root/. Additionally, I've included aliases in the gist for easier switching between GPU selections. 5 TB/s bandwidth on GPU dedicated entirely to the model on highly optimized backend (rtx 4090 have just under 1TB/s but you can get like 90-100t/s with mistral 4bit GPTQ) $ ollama run llama3. I am able to run dolphin-2. Prompt: why is sky blue M1 Air, 16GB RAM: total duration: 31. I might have even Execute ollama show <model to modify goes here> --modelfile to get what should be as base in the default TEMPLATE and PARAMETER lines. Anyways, GPU without any questions. I have an M2 with 8GB and am disappointed with the speed of Ollama with most models , I have a ryzen PC that runs faster. Specifically, I'm interested in harnessing the power of the 32-core GPU and the 16-core Neural Engine in my setup. Whether a 7b model is "good" in the first place is relative to your expectations. Since these things weren't saturating the SoC's memory bandwidth I thought that the caching/memory hierarchy improvements might allow for higher utilization of the available bandwidth and therefore higher Some things support OpenCL, SYCL, Vulkan for inference access but not always CPU + GPU + multi-GPU support all together which would be the nicest case when trying to run large models with limited HW systems or obviously if you do by 2+ GPUs for one inference box. However, there are a few points I'm unsure about and I was hoping to get some insights: I allow the GPU on my Mac to use all but 2GB of the RAM. Get the Reddit app Scan this QR code to download the app now. I am looking for some guidance on how to best configure ollama to run Mixtral 8X7B on my Macbook Pro M1 Pro 32GB. IME, the CPU is about half the speed of the GPU. First time running a local conversational AI. It doesn't have any GPU's. I have the GPU passthrough to the VM and it is picked and working by jellyfin installed in a different docker. Yesterday I did a quick test of Ollama performance Mac vs Windows for people curious of Apple Silicon vs Nvidia 3090 performance using Mistral Instruct 0. 12 tokens/s eval count: 138 token(s) eval duration: 3. As per my previous post I have absolutely no affiliation whatsoever to these people, having said that this is not a paid product. Make it executable: chmod +x ollama_gpu_selector. To get 100t/s on q8 you would need to have 1. The M3 Pro maxes out at 36 gb of RAM, and that extra 4 gb may end up significant if you want to use it for running LLMs. When I first launched the app 4 months ago, it was based on ggml. - OLlama Mac only? I'm on PC and want to use the 4090s. 763920914s load duration: 4. You can get an external GPU dock. I thought the apple silicon NPu would be significant bump up in speed, anyone have recommendations for system configurations for optimal local speed improvements? The constraints of VRAM capacity on Local LLM are becoming more apparent, and with the 48GB Nvidia graphics card being prohibitively expensive, it appears that Apple Silicon might be a viable alternative. I have a Mac Studio M2 Ultra 192GB and several MacBooks and PCs with Nvidia GPU. According to modelfile, "num_gpu is the number of layers to send to the GPU(s). If you're happy with a barebones command-line tool, I think ollama or llama. Just installed a ryzen 7 7800x3d and a 7900 xtx graphics card with a 1000W platinum PSU. Secondly, it's a really positive development with regards to Mac's gaming capabilities, and where it might be heading. Many people Hi everyone! I recently set up a language model server with Ollama on a box running Debian, a process that consisted of a pretty thorough crawl through many documentation sites and wiki forums. Everything shuts off after I log into user. total duration: 8. Apple M2 Ultra with 24‑core CPU, 76‑core GPU, 32‑core Neural Engine) Use any money left over to max out RAM. What GPU, which version of Ubuntu, and what kernel? I'm using Kubuntu, Mint, LMDE and PopOS. 37 tokens/s eval count: 268 token(s) Anyway, my M2 Max Mac Studio runs "warm" when doing llama. FYI not many folks have M2 Ultra with 192GB RAM. 097ms prompt eval rate: 89. It's not the most user friendly, but essentially what you can do is have your computer sync one of the language models such as Gemini or Llama2. Introducing https://ollamac. Yet a good NVIDIA GPU is much faster? Then going with Intel + NVIDIA seems like an upgradeable path, while with a mac your lock. To reset the GPU memory allocation to stock settings, enter the following command: sudo sysctl iogpu. I think this is the post I used to fix my Nvidia to AMD swap on Kubuntu 22. Here results: 🥇 M2 Ultra 76GPU: 95. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications. gpu. It's the fast RAM that gives a Mac it's advantage. Now you can run a model like Llama 2 inside the container. The other thing is to use the CPU instead of the GPU. And remember, the whole post is more about complete apps and end-to-end solutions, ie, "where is the Auto1111 for LLM+RAG?" (hint it's NOT PrivateGPT or LocalGPT or Ooba that's for sure). I don't necessarily need a UI for chatting, but I feel like the chain of tools (litellm -> ollama -> llama. Large models run on Mac Studio. And GPU+CPU will always be slower than GPU-only. I optimize mine to use 3. Like others said; 8 GB is likely only enough for 7B models which need around 4 GB of RAM to run. Ollamac is a native macOS app for Ollama. Hej Im considering to buy a 4090 with 24G of RAM or 2 smaller / cheaper 16G cards What i do not understand from ollama is that gpu wise the model can be split processed on smaller cards in the same machine or is needed that all gpus can load the full model? is a question of cost optimization large cards with lots of memory or small ones with half the memory but many? opinions? "To know the CC of your GPU (2. cpp are good enough. My specs are: M1 Macbook Pro 2020 - 8GB Ollama with Llama3 model I appreciate this is not a powerful setup however the model is running (via CLI) better than expected. 5-4. The GPU usage for Ollama remained at 0%, and the wired memory usage shown in the Activity Monitor was significantly less than the model size. Fix the issue of Ollama not using the GPU by installing suitable drivers and reinstalling Ollama. The 14 core 30 GPU M3 Max (300GB/s) is about 50 tokens/s, which is the same as my 24-core M1 Max and slower than the 12/38 M2 Max (400GB/s). x up to 3. You can workaround this driver bug by reloading the NVIDIA UVM driver with sudo rmmod nvidia_uvm && sudo modprobe nvidia_uvm Sometimes stuff can be somewhat difficult to make work with gpu (cuda version, torch version, and so on and so on), or it can sometimes be extremely easy (like the 1click oogabooga thing). 926087959s prompt eval count: 14 token(s) prompt eval duration: 157. You add the FROM line with any model you need. Try to get a laptop with 32gb or more of system RAM. Get the Reddit app Scan this QR code to download the app now no matter how powerful is my GPU, Ollama will never enable it. In this post, I'll share my method for running SillyTavern locally on a Mac M1/M2 using llama-cpp-python. Which is the big advantage of VRAM available to the GPU versus system RAM available to the CPU. The layers the GPU works on is auto assigned and how much is passed on to CPU. Run Ollama inside a Docker container; docker run -d --gpus=all -v ollama:/root/. Also using ollama run --verbose instead of running from api/curl method We would like to show you a description here but the site won’t allow us. Download Ollama on macOS Also, there's no ollama or llama. I rewrote the app from the ground up to use mlc-llm because it's waay faster. Don't bother upgrading storage. If LLMs are your goal, a M1 Max is the cheapest way to go. Here's what's new in ollama-webui: docker compose -f docker-compose. Ai for details) Koboldcpp running with SillyTavern as the front end (more to install, but lots of features) Llamacpp running with SillyTavern front end It mostly depends on your ram bandwith, with dual channel ddr4 you should have around 3. And Ollama also stated during setup that Nvidia was not installed so it was going with cpu only mode. /ollama_gpu_selector. Run Llama 3. docker exec New to LLMs and trying to selfhost ollama. I was wondering if Ollama would be able to use the AMD GPU and offload the remaining to RAM? Ollama generally supports machines with 8GB of memory (preferably VRAM). The only reason to offload is because your GPU does not have enough memory to load the LLM (a llama-65b 4-bit quant will require ~40GB for example), but the more layers you are able to run on GPU, the faster it will run. 2-2. sh script from the gist. Only 30XX series has NVlink, that apparently image generation can't use multiple GPUs, text-generation supposedly allows 2 GPUs to be used simultaneously, whether you can mix and match Nvidia/AMD, and so on. 6 and was able to get about 17% faster eval rate/tokens. Lastly, it's just plain cool that you can run Diablo 4 on a Mac laptop! Never give in to negativity! In my test all prompts are not long, just a simple questions and expecting simple answers. cpp even when both are GPU-only. You can also consider a Mac. 5-mixtral-8x7b. Also can you scale things with multiple GPUs? The issue with llama. md)" Ollama is a lightweight, extensible framework for building and running language models on the local machine. So, if it takes 30 seconds to generate 150 tokens, it would also take 30 seconds to process the prompt that is 150 tokens long. e. 639212s eval rate: 37. 7B and 7B models with ollama with reasonable response time, about 5-15 seconds to first output token and then about 2-4 tokens/second after that. Read reference to running ollama from docker could be option to get eGPU working. That way your not stuck with whatever onboard GPU is inside the laptop. The Pull Request (PR) #1642 on the ggerganov/llama. 2 t/s) 🥈 Windows Nvidia 3090: 89. All the features of Ollama can now be accelerated by AMD graphics cards on Ollama for Linux and Windows. Any of the choices above would do, but obviously if your budget allows, the more RAM/GPU cores the better. Mar 14, 2024 · Ollama now supports AMD graphics cards in preview on Windows and Linux. 2 and 2-2. My device is a Dell Latitude 5490 laptop. Follow the prompts to select the GPU(s) for Ollama. Trying to collect data about ollama execution in windows vs mac os. More hardware support is on the way! Feb 26, 2024 · If you've tried to use Ollama with Docker on an Apple GPU lately, you might find out that their GPU is not supported. ollama -p 11434:11434 --name ollama ollama/ollama Nvidia GPU. My question is if I can somehow improve the speed without a better device with a . When I use the 8b model its super fast and only appears to be using GPU, when I change to 70b it crashes with 37GB of memory used (and I have 32GB) hehe. It seems that this card has multiple GPUs, with CC ranging from 2. cpp, up until now, is that the prompt evaluation speed on Apple Silicon is just as slow as its token generation speed. As a result, the prompt processing speed became 14 times slower, and the evaluation speed slowed down by 4. Did you manage to find a way to make swap files / virtual memory / shared memory from SSD work for ollama ? I am having the same problem when I run llama3:70b on Mac m2 32GB ram. Also check how much VRAM your graphics card has, some programs like llama. ollama -p 11434:11434 --name ollama ollama/ollama Run a model. Get up and running with large language models. And even if you don't have a Metal GPU, this might be the quickest way to run SillyTavern locally - full stop. - LangChain Just don't even. cpp inference. Ollama on Mac pro 2019 and AMD GPU. 416995083s load duration: 5. 8 on llama 2 13b q8. Customize and create your own. 1) you can see in Nvidia website" I've already tried that. cpp repository, titled "Add full GPU inference of LLaMA on Apple Silicon using Metal," proposes significant changes to enable GPU support on Apple Silicon for the LLaMA language model using Apple's Metal API. I'm wondering if there's an option to configure it to leverage our GPU. cpp can put all or some of that data into the GPU if CUDA is working. I know it's obviously more effective to use 4090s, but I am asking this specific question for Mac builds. 6 t/s 🥉 WSL2 NVidia 3090: 86. Q4_K_M in LM Studio with the model loaded into memory if I increase the wired memory limit on my Macbook to 30GB. Run the script with administrative privileges: sudo . 1GB then ollama decide how to separate the work. 04 just add a few reboots. 1, Phi 3, Mistral, Gemma 2, and other models. cpp?) obfuscates a lot to simplify it for the end user and I'm missing out on knowledge. If you have ever used docker, Ollama will immediately feel intuitive. The only thing is, be careful when considering the GPU for the VRAM it has compared to what you need. Hello r/LocalLLaMA. I want to run Stable Diffusion (already installed and working), Ollama with some 7B models, maybe a little heavier if possible, and Open WebUI. It's built for Ollama and has all the features you would expect: Connect to a local or remote server System prompt Max out on processor first ( i. Just pop out the 8Gb Vram GPU and put in a 16Gb GPU. ollama/models") OLLAMA_KEEP_ALIVE The duration that models stay loaded in memory (default is "5m") OLLAMA_DEBUG Set to 1 to enable additional debug logging I would try to completely remove/uninstall ollama and when installing with eGPU hooked up see if any reference to finding your GPU is found. I can run it if you provide me prompts you like to test. I'm currently using ollama + litellm to easily use local models with an OpenAI-like API, but I'm feeling like it's too simple. - MemGPT? Still need to look into this Ollama is a CLI allowing anyone to easily install LLM models locally. I use a Macbook Pro M3 with 36GB RAM, and I can run most models fine and it doesn't even affect my battery life that much. You'll also likely be stuck using CPU inference since Metal can allocate at most 50% of currently available RAM. Trying to figure out what is the best way to run AI locally. 2 q4_0. It is not available in the Nvidia site. Mac and Linux machines are both supported – although on Linux you'll need an Nvidia GPU right now for GPU acceleration. Since devices with Apple Silicon use Unified Memory you have much more memory available to load the model in the GPU. yaml up -d --build /r/StableDiffusion is back open after the The infographic could use details on multi-GPU arrangements. SillyTavern is a powerful chat front-end for LLMs - but it requires a server to actually run the LLM. upvote · comments I have a 12th Gen i7 with 64gb ram and no gpu (Intel NUC12Pro), I have been running 1. cpp main branch, like automatic gpu layer + support for GGML *and* GGUF model. My opinion is get a desktop. However, Ollama is missing a client to interact with your local models. x. Even using the CPU, the Mac is pretty fast. Or check it out in the app stores Can Ollama accept >1 for num_gpu on Mac to specify how many layers What GPU are you using? With my GTX970 if I used a larger model like samantha-mistral 4. Ollama running on CLI (command line interface) Koboldcpp because once loaded has its own robust proven built in client/front end Ollama running with a chatbot-Ollama front end (see Ollama. 92 tokens/s NAME ID SIZE PROCESSOR UNTIL llama2:13b-text-q5_K_M 4be0a0bc5acb 11 GB 100 How good is Ollama on Windows? I have a 4070Ti 16GB card, Ryzen 5 5600X, 32GB RAM. Although there is an 'Intel Corporation UHD Graphics 620' integrated GPU. Jun 30, 2024 · Quickly install Ollama on your laptop (Windows or Mac) using Docker; Launch Ollama WebUI and play with the Gen AI playground; Without GPU on Mac M1 Pro: With Nvidia GPU on Windows: On linux I just add ollama run --verbose and I can see the eval rate: in tokens per second . OLLAMA_MODELS The path to the models directory (default is "~/. 1 t/s Mac architecture isn’t such that using an external SSD as VRAM will assist you that much in this sort of endeavor, because (I believe) that VRAM will only be accessible to the CPU, not the GPU. noidcfiwjgodrlkfuqobcdnxntwbmuxwifyqjtycylwxruahrpmjrcgmr
