Llama on m2 reddit I’m guessing gpu support will show up within the next few weeks. cpp project, it is possible Getting my feet wet with llama. Previous posts with more discussion and info: Meta newsroom: Want to add to Meta released LLaMA, a state of the art large language model, about a month ago. M2 Max is 400GB/s, M2 Ultra is 800GB/s. 1. 22 tokens per second Eval: 28. Longer prompts with some of the long context models can take a few minutes to kick off. No, turned out my mobo didn't have the right M2 slot and I quickly moved on to other things. cpp. Hi, I recently discovered Alpaca. I tried this on my M2 Mac and it does seem to be the case. Llama 3 dominates the upper and mid cost-performance front (full analysis) 2. ) I recently put together a detailed guide on how to easily run the latest LLM model, Meta Llama 3, on Macs with Apple Silicon (M1, M2, M3). Someone has linked to this thread from another place on reddit: [r/datascienceproject] Run Llama 2 Locally in 7 Lines! (Apple Silicon Mac) (r/MachineLearning) Performance: 46 tok/s on M2 Max, 156 tok/s on RTX 4090. 2-2. There’s work going on now to improve that. https://llama. Would I be better off purchasing a Mac with large unified memory for running ML locally such as LLaMA? Given that Apple M2 Max with 12‑core CPU, 38‑core GPU, 16‑core Neural Engine with 96GB unified memory and 1TB SSD storage is currently $4,299, would that Anyway, my M2 Max Mac Studio runs "warm" when doing llama. 86 seconds: 35. Botton line, today they are comparable in performance. I have found one obscure system vendor that offers system in a 4U rackmount for $8,000 barebone. Running these tests are using 100% of the GPU as well. 125. Hi, I just received a Mac laptop from work and wanted to test llama2 two models on it. 15 version increased the FFT performance in 30x. How to fine-tune llama2 on Mac M2 with 16gb? Question | Help /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. For code, I am using the llama cpp python. cpp because there's a new branch (literally not even on the main branch yet) of a very experimental but very exciting new feature. Key Highlights: Today a new version of llama. I expect the MacBooks to be similar. The 13B model does run well on my computer but there are much better models available like the 30B and 65B. 5 hrs = $1. Right now I believe the m1 ultra using llama. Hard to say. I tried 70b LLAMA 2 on 64g M2 max and it’s struggling to give me 6 tokens a sec even with Q8. simonwillison. Any of the choices above would do, but obviously if your budget allows, the more RAM/GPU cores the better. Probably decently. 5 on mistral 7b q8 and 2. I can post screen caps if anyone want's to see. More info: https://rtech. Currently Downloading Falcon-180B-Chat-GGUF Q4_K_M -- 108GB model is going to be pushing my 128GB machine. Since the M1/M2 ultra probably have more memory bandwidth than the GPU can keep up with. If you have any quick questions to ask, please use this megathread instead of a post. cpp and quantized models up to 13B. Pulls about 400 extra watts when "thinking" and can generate a line of chat in response to a few lines of context in about 10-40 seconds (not sure how many seconds per token that works out to. As of mlx version 0. - 6900HS/3050ti 4GB is Personal experience. Share Sort by: Best. 8 on llama 2 13b q8. I know, I know, before you rip into me, I realize I could have bought something with CUDA support for less money but I use the Mac for other things and love the OS, energy use, form factor, noise level (I do music), etc. cpp til. cpp is the next biggest option. cpp and trying to use GPU's during training. About 65 t/s llama 8b-4bit M3 Max. cpp has no ui so I'd wait until there's something you need from it before getting into the weeds of working with it manually. 38 tokens per second 565 tokens in 15. Subreddit to discuss about Llama, the large language model created by Meta AI. Prompt eval is also done on the cpu. 14, mlx already achieved same performance of llama. Skip to main content. cpp has native support on Apple silicon so for LLMs it might end up working out well. Depends on how power management will allow it to boost. There are even demonstrations showing the successful application of the changes with 7B, 13B, and 65B LLaMA models 1 2 . cpp the best software to For my purposes, which is just chat, that doesn’t matter a lot. Time taken for llama to respond to this prompt ~ 9sTime taken for llama to respond to 1k prompt ~ 9000s = 2. Open comment sort options. Watching llama. 75 per hour: The number of tokens in my prompt is (request + response) = 700 Cost of GPT for one such call = $0. Is HF not up to the task? Why? I don’t mind using different tools, just curious why huggingface is not in the picture. com with My M2 Studio has been decent for inference, especially running Airoboros-65B on GPU, but there is a delay reading each prompt, usually as long or longer than the generation of the answer. support/docs/meta -M2 Macbook Air 24GB will be better at running 13B and 7B models. 170K subscribers in the LocalLLaMA community. 2 and 2-2. But I have not tested it yet. cpp inference. 27ms per token, 35. Whether you're a developer, AI enthusiast, or just curious about leveraging powerful AI on your own hardware, this guide aims to simplify the process for you. Would this be a good option for tokens per second, or would there be something better? Also is llama. Get the Reddit app Scan this QR code to download the app now. Members Online It mostly depends on your ram bandwith, with dual channel ddr4 you should have around 3. To get 100t/s on q8 you would need to have 1. I am astonished with the speed of the llama two models on my 16 GB Mac air, M2. I mean- my M2 Ultra is two M2 Max processors stacked on top of each other, and I get the following for Mythomax-l2-13b: Llama. 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 This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API changes, which break third-party apps and moderation tools. If you can get a M1/M2 Max(Ultra) with more memory for the same price as the M3 Max 36GB, I would get that instead. I've found this to be the quickest and simplest method to run SillyTavern locally. 5-4. It can be useful to compare the performance that llama. Llama. I am using llama. It works well. 13B model = 24 tok/s! Credits to Georgi Gerganov. cpp Metal for this model on a M2 Ultra. A 192GB M2 Ultra Max Studio is ~$6k. (Optional) Install llama-cpp-python with Metal acceleration Llama. Please use our Discord server instead of supporting a company that acts against its users and unpaid moderators. cpp is released where it can do 40 tok/s inference of the 7B model on a M2 Max, with 0% CPU usage, by fully using all 38 GPU cores. Hello, I am looking at a M2 Max (38 GPU Cores) Mac Studio with 64 gigs of ram to run interference on llama 2 13b. 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) Running LLaMA 7B on a 64GB M2 MacBook Pro with llama. cpp and Ollama. More info: https Subreddit to discuss about Llama, the large language model created by Meta AI. It may run faster on M2 CPU than M2 GPU funnily enough. 79ms per token, 56. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and Want to start playing with Meta’s Llama 2? On an M2 max I get about 30~50 tokens per second for the 7b model. . 001125Cost of GPT for 1k such call = $1. Share your thoughts on Llama 3. I'm curious about whether additional swap memory is required when loading 70b LLAMA 2 Apple M1 and M2 became famous for LLM inference because of the large RAM bandwidth of the Pro and Ultra models (400 and 800 GB/s respectively). practicalzfs. The compute I am using for llama-2 costs $0. Checking google, everyone seems to recommend llama. cpp and have been enjoying it a lot. Ultimately what is the faster CPU for running general-purpose LLMs before GPU acceleration? M2 or Intel 12th gen? I'll limit it to the best-released processor on both sides. Software has moved on quite a lot, and I'm wondering whether Note: Reddit is dying due to terrible leadership from CEO /u/spez. More info: https://rtech The 192GB M2 Ultra seems to have a 75% ratio - so, about 144GB usable by GPU. EDIT: Llama8b-4bit uses about 9. For immediate help and problem solving, please join us at https://discourse. That’s about how much just 4x 3090s currently cost. I've read that mlx 0. I usually don't like purchasing from Apple, but the Mac Pro M2 Ultra with 192GB of memory and 800GB/s bandwidth seems like it might be a really good deal Hi All, I bought a Mac Studio m2 ultra (partially) for the purpose of doing inference on 65b LLM models in llama. Using "Wizard-Vicuna" and "Oobabooga Text Generation WebUI" I'm able to generate some answers, but they're being generated very slowly. I'm looking to buy another machine to work with LLaMA models. 6 tokens per second Llama cpp python in Oobabooga: M2 Ultra 128GB 24 core/60 gpu cores. M2 Mac’s with smaller overall ram sizes definitely use the 66% ratio (my M2 32Gbyte Mac mini has a 66% ram ratio) - but I don’t know where the switch over point is. Make sure that you have the correct python libraries so that you could 117 votes, 77 comments. With this PR, LLaMA can now run on Apple's M1 Pro and M2 Max chips using Metal, which would potentially improve performance and efficiency. Lenovo, HP, Dell offer Threadripper Pro 7985WX in their workstation, and the CPU alone with 8 channels starts at $7,000. Best of all, for the Mac M1/M2, this method can take advantage of Metal acceleration. I run it on a M1 MacBook Air that has 16GB of RAM. Since even with the same memory bandwidth, the M2 is faster than the M1. You cannot even buy Epyc systems through normal retail channels. net Open. I am running. Basic M1 and M2 models offer a Looks to be about 15-20t/s from the naked eye, which seems much slower than llama. Open menu Open navigation Go to Reddit Also the community recently discovered that the M3 Max chips aren't held together by infinity fabric like in the M1 and M2 Max, meaning that theoretically there won't be the same thing holding Apple Not sure I'm in the right subreddit, but I'm guessing I'm using a LLaMa language model, plus Google sent me here :) So, I want to use an LLM on my Apple M2 Pro (16 GB RAM) and followed this tutorial. meta. I have a mac mini M2 with 24G of memory and 1TB disk. Add to that the GPU processing improvements and it should be a winner. cpp achieves across the M-series chips and hopefully answer questions of people wondering if they should upgrade or Step-by-step guide to implement and run Large Language Models (LLMs) like Llama 3 using Apple's MLX Framework on Apple Silicon (M1, M2, M3, M4). We're now read-only indefinitely due Kobold. OP is waiting for a future M3 Ultra which should be comparable to the M1/M2 Ultra in memory bandwidth. I noticed that finally Llama cpp added -ngl option to finetune command. These models needed beefy hardware to run, but thanks to the llama. But in this case llama. With the testflisht version, llama 2 is happily llamaing. com. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. cpp metal uses mid 300gb/s of bandwidth. The impact of these changes is significant. This thread is talking about llama. Surprisingly, my 32gb m2 machine can’t even load the 7b model. cpp directly: Prompt eval: 17. Or check it out in the app stores   Subreddit to discuss about Llama, the large language model created by Meta AI. 5GB RAM with mlx Actually the Mac Studios are quite cost effective, the problem has been general compute capabilities due to lack of CUDA. cpp is constantly getting performance improvements. 87 I'm running LLaMA 30B on six AMD Insight MI25s, using fp16 but converted to regular pytorch with vanilla-llama. For a 70B Q3 model, I get 4 t/s using a M1 Max with llama. For SillyTavern, the llama-cpp-python local LLM server is a drop-in replacement for OpenAI. I'm using this command to start, on a tiny test dataset (5 Subreddit to discuss about Llama, the large language model created by Meta AI. cpp do 40 tok/s inference of the 7B model on my M2 Max, with 0% CPU usage, and Use llama. lqrtyr xiof zpzqy havgdo zdfwm nxyym tig icxa nqvgf qzmb