Llama cpp m2 ultra. 🥉 WSL2 NVidia 3090: 86. cpp use that npu to fast up Motivation Intel® Core™ Ultra processors deliver three dedicated engines (CPU, GPU, and NPU) to help unlock the power of AI Jul 19, 2023 · If you have M2 Max 96gb, tried adding -ngl 38 to use MPS Metal acceleration (or a lower number if you don't have that many cores). M2 max = 400 GB/s. cpp工具 为例，介绍模型量化并在 本地CPU上部署 的详细步骤。. Here are the current numbers on M2 Ultra for LLaMA, LLaMA-v2 and Falcon 7B: . Here’s a one-liner you can use to install it on your M1/M2 Mac: Here’s what that one-liner does: cd llama. Jun 1, 2023 · M2 Max で Metal で llama 7B が 40 tokens/sec!!! Rinna 3. cpp is an excellent choice for running LLaMA models on Mac M1/M2. You're probably aware, but OpenAI has different speeds for different kinds of customers. Usage. There’s work going on now to improve that. Up until now, Llama. There could still be a 100% speed increase in the pipeline, depending on how optimizations go. LLaMA unlocks large language model potential, revolutionizing research endeavors. cpp also has support for Linux/Windows. ggmlv3. Many people conveniently ignore the prompt evalution speed of Mac. It should also be noted that ~1/3 of the ram is reserverd for the CPU, and programs running those models can take up to ~3GB of RAM. Dec 2, 2023 · The 4090 is 1. 3 GB on disk. cpp already supports it (although the conversion script needed some changes). cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide variety of hardware - locally and in the cloud. Hope MLX will improve and gain faster prompt processing, token speed and quantized cache/better compression options. /main --model your_model_path. Between quotes like "he implemented shaders currently focus on qMatrix x Vector multiplication which is normally needed for LLM text-generation. Basic M1 and M2 models offer a fraction of their RAM bandwidth (68 and 100 GB/s respectively) so they offer no significant speed advantage over a standard PC. wired_limit_mb=0. cpp and run local servers in terminal, but I want to be able to send API requests from other machines on the network (or even out of network if it's possible). 15. 2 I wouldn't want to pick a fight with an Apple user, because their M2 Sep 17, 2023 · I'm currently leaning towards purchasing the Mac Studio M2 with 192GB RAM, but I've also been considering the Mac Pro M2 with the same memory configuration. WebUI Demo. This implies that you need a very beefy machine. so to me, it Today I updated Oobabooga to the latest version, and with it came a newer version of Llama. It along with it's smaller brethren (Orin Nano, Orin NX) can be deployed into embedded and robotics applications at the edge. Oct 14, 2023 · llama. Performance: 46 tok/s on M2 Max, 156 tok/s on RTX 4090. It's also unified memory (shared between the ARM cores and the CUDA cores), like the Apple M2's have, but for that the software needs to be specifically optimized to use zero-copy (which llama. M2 = 100 GB/s. Get the download. The models were tested using the Q4_0 quantization method, known for significantly reducing the model size albeit at the cost of quality loss. cpp is already optimized on Apple hardware, and Tunney didn't opt for Apple's proprietary compiler. cpp / grok-1 support @ibab_ml on X. This was apparently because llama. But for basic M1/M2 and M1/M2 Pro, GPU and CPU inference speed is the same. And the M2 Ultra has a bandwidth of 800 GB/s which is about 8 times faster than an average modern desktop CPU (dual-channel DDR4-6400 offers a bandwidth of 102 GB/s). Speculative execution for LLMs is an excellent inference-time optimization. However, I am unable to train anything larger than ~3B parameter model on a M2 Ultra with 64GB of RAM. Mar 13, 2023 · 130亿参数模型仅需4GB内存. Once downloaded, move the model file to llama. cpp] 最新build（6月5日）已支持Apple Silicon GPU！ 建议苹果用户更新 llama. cpp doesn't appear to support any neural net accelerators at this point (other than nvidia tensor-rt through CUDA). 5 t/s. cpp on the Mac used either 0 or 1 for ngl; 0 off, 1 on. cpp test, M2 MacBook Pro 96GB. With llama. m2 ultra has 800 gb/s. sh ${model} "f Feb 2, 2024 · The M1/M2 Pro supports up to 200 GB/s unified memory bandwidth, while the M1/M2 Max supports up to 400 GB/s and M1/M2/M3 Ultra 800 GB/s. You need a beefy machine to run grok-1. 3 points by Jimmc414 31 minutes ago | hide | past | favorite | 1 comment. Zestyclose_Yak_3174. Jul 22, 2023 · Llama. Many people who own the M2 Max 96GB and M1/M2 Ultra models have reported speeds of 65B when using the GPU. It stops being true if you substitute "personal LLM node" for most of anything else, especially "computer", because there are many good computer boxes that beat Mac Studio if we ignore the LLM use case. txt. 4:25 AM · Mar 11, 2023 Sep 8, 2023 · Llama2 13B Orca 8K 3319 GGUF model variants. 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. slowllama is not using any quantization. From my research, it seems there's minimal difference in computational power between these two devices. Get up and running with Llama 3, Mistral, Gemma, and other large language models. The m2 ultra studio goes up to 192 gb for less than 8k As far as tokens per second on llama-2 13b, it will be really fast, like 30 tokens / second fast (don't quote me on that but all I know is it's REALLY fast on such a slow model). MLX. Windows则可能需要cmake等编译工具的安装（Windows用户出现模型无法理解中文或生成速度特别慢时请参考 FAQ#6 ）。. Our latest version of Llama is now accessible to individuals, creators, researchers, and businesses of all sizes so that they can experiment, innovate, and scale their ideas responsibly. As cherrypop only requires 5. 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. py. That seems pretty fast to me. Tokens/sec performance with 4-bit quantization: llama. m2 max has 400 gb/s. while m2 max has only 40 tokens/s in 7b model and 24 tokens/s in 13b. M2 pro = 200 GB/s. Jun 16, 2023 · Yeah, for M2 Max, the GPU (38 core) is almost 2 times faster. 2 t/s) 🥈 Windows Nvidia 3090: 89. run the . The most prioritized ones are accounts that have gpt-4-32k. 1 t/s. 5 t/s (Apple MLX here reaches 76. gguf model is ideal. streamlit run chat_with_llama2-WebUI. Llama 2 13B is the larger model of Llama 2 and is about 7. I don’t think so. M1 GPU Performance. But search for the right branch and build it. 隨著人工智能的快速發展，大型語言模型（LLM）如 Llama 2, 3 已成為技術前沿的熱點。. - ollama/ollama Apr 5, 2024 · Ollama Mistral Evaluation Rate Results. It isn't near GPU level (1TB/s) or M1/M2 level (400 up to 800GB/s for the biggest M2 studio) sudo sysctl iogpu. 16 conda activate llama (4) Install the LATEST llama-cpp-pythonwhich happily supports MacOS Metal GPU as of version 0. I'm using the 65B Dettmer Guanco model. This release includes model weights and starting code for pre-trained and instruction tuned Jun 6, 2023 · The M1 Ultra is supported by Asahi Linux and I have to say the memory bandwidth makes things like running llama. 62 (you needed xcode installed in order pip to build/compile the C++ code) The Pull Request (PR) #1642 on the ggerganov/llama. And 2 cheap secondhand 3090s' 65b speed is 15 token/s on Exllama. The Mac Studio can support 128 GB of VRAM and the MacBook Pro supports 96 GB. 11. • 7 mo. The M1 Ultra and M2 Ultra mac studios have bandwidth of 800GB/s, and the above models run reasonably well on them. FYI not many folks have M2 Ultra with 192GB RAM. In the above results, the last two- (2) rows are from my casual gaming rig and the aforementioned work laptop. I suspect for at least 7B as well. cpp:light-cuda: This image only includes the main executable file. CUDA V100 PCIe & NVLINK: only 23% and 34% faster than M3 Max with MLX, this is some serious stuff! MLX stands out as a game changer when compared to CPU and MPS, and it even comes close to the performance of a TESLA V100. e. Dec 15, 2023 · The M2 Pro has double the memory bandwidth of an M2, a M1/2/3 Max doubles this (400GB/s due to a 512Bit wide memory bus), and the M1/2 Ultra doubles again (800BG/s, 1024Bit memory bus). • 11 days ago. Still takes a ~30 seconds to generate prompts. Typically on a laptop or desktop, the CPU and GPU have Jun 4, 2023 · [llama. 6. Accessible to various researchers, it's compatible with M1 Macs, allowing LLaMA 7B and 13B to run on M1/M2 MacBook Pros using llama. Sep 2, 2023 · And 10 tok/s on M2 Ultra. These are ggufs, run in llama. 74 ms per token) Mar 18, 2024 · Run grok on an Mac Studio with an M2 Ultra and 192GB of unified ram: See: llama2. If you are halfway through a 8000 token converation, (4000 tokens of prompt processing) it means that on a M2 Ultra: llama. There is a pronounced stark performance difference from traditional CPUs (Intel or AMD) simply because we I have a $5000 128GB M2 Ultra Mac Studio that I got for LLMs due to speculation like GP here on HN. ChatGLM-6B: Q4_0 Q4_1 Here is a typical run using LLaMA v2 13B on M2 Ultra: local/llama. Apr 3, 2024 · The Apple M2 Ultra-powered Mac Studio saw some performance regression in both prompt and evaluation performance for the Q8_0 data type. No real improvement between MPS and MLX on M3 Pro though. However, it only supports usage in a text terminal. 5x / 1. cpp it is already possible to do a lora finetune, but I am not aware if the branch was already merged. com/Dh2emCBmLY — Lawrence Chen (@lawrencecchen) March 11, 2023 More detailed instructions here Aug 23, 2023 · 以 llama. 7, though. Hard to say. 1 t/s (Apple MLX here reaches 103. Next, simply drag and drop your folder onto the command line, and then press the ‘Enter’. Jun 18, 2023 · Test Setup. Prompt eval is also done on the cpu. An easy to see this difference is comparing a trial account's Turbo speed to a pay-as-you-go one. This latter bit is a big deal. open mac terminal, execute chmod +x . 本地快速部署体验推荐使用经过指令精调的Alpaca模型，有条件的推荐使用8-bit Mar 15, 2023 · LLaMA, the Large Language Model Meta AI, advances AI research with a noncommercial research-focused license. cpp已添加基于Metal的inference，推荐Apple Silicon（M系列芯片）用户更新，目前该改动已经合并至main branch。 刚订购一款M2 Ultra 192GB的Mac Studio来体验一下推理，10多天才拿到货，工厂还在做。 在Github问了半天，终于有大佬回复了他们跑llama. この手法については、OpenAIのKarpathy氏が以下のポストで解説している。. cpp is a port of Llama in C/C++, which makes it possible to run Llama 2 locally using 4-bit integer quantization on Macs. And 7x faster in the GPU-heavy prompt-processing (similar to training or full batch-processing). cpp on Mac Studio M2 Ultra TLDR; GPU memory size is key to running large LLMs — Apple Silicon because of its unified memory allows for local simulation of… Jetson AGX Orin has 64GB of unified memory, 2048 CUDA cores, and is $1999 ($1699 if you have an EDU email). cppに「Speculative Sampling（投機的サンプリング）」という実験的な機能が マージされて 話題になっていた。. Speaking from personal experience, the current prompt eval speed on 65B on a m1 ultra 128gb / 64 core. ggml --n-gpu-layers 100 How to Install Llama. using exllama you can get 160 tokens/s in 7b model and 97 tokens/s in 13b model. cpp 2024-03-26 141: 66: TinyLlama 1. Rumor has it that it lives in the cores of 8 GPU's and that the Model must fit in the VRAM. revenant-miami. However, Llama. so 4090 is 10% faster for llama inference than 3090. cd. See the installation guide on Mac. Technically, you can use text-generation-webui as a GUI for llama. Red text is the lowest, whereas, Green is for the highest recorded score across all runs. The initial load up is still slow given I tested it with a longer prompt, but afterwards in interactive mode, the back and forth is almost as fast as how I felt when I first met the original ChatGPT (and in the few days when everyone was hitting it). cpp/models Llama cpp started slowing after about 10 threads, and limited usability of falcon 180b gptq at the moment ( only transformers and hugging face text inference compatible ). M2 ultra = 800 GB/s. Instead, it offloads parts of model to SSD or main memory on both forward/backward passes. Here's an example command:. M2 Ultra Mac Studio is, literally, "the smallest, prettiest, out of the box easiest, most powerful personal LLM node today". Collaborator. It has some upsides in that I can run quantizations larger than 48GB with extended context, or run multiple models at once, but overall I wouldn't strongly recommend it for LLMs over an An alternative would be a m2 ultra or the upcoming m3 ultra. cpp See also: Large language models are having their Stable Diffusion moment right now . cpp 是开发者 Georgi Gerganov 用纯 C/C++ 代码实现的 LLaMA 模型推理开源项目。 所谓推理，即是「给输入 - 跑模型 - 得输出」的模型运行过程。 最近 Georgi Gerganov 用搭载苹果 M2 Ultra 处理器 的设备运行了一系列测试，其中包括 并行运行 128 个 Llama 2 7B 流 。 Here results: 🥇 M2 Ultra 76GPU: 95. Two 4090s can run 65b models at a speed of 20+ tokens/s on either llama. The M2 Ultra's big draw is Framework. 📚 愿景：无论您是对Llama已有研究和应用经验的专业开发者，还是对Llama中文优化感兴趣并希望深入探索的新手，我们都热切期待您的加入。在Llama中文社区，您将有机会与行业内顶尖人才共同交流，携手推动中文NLP技术的进步，开创更加美好的技术未来！ . LLM inference in C/C++. cpp 是开发者 Georgi Gerganov 用纯 C/C++ 代码实现的 LLaMA 模型推理开源项目。 所谓推理，即是「给输入 - 跑模型 - 得输出」的模型运行过程。 最近 Georgi Gerganov 用搭载苹果 M2 Ultra 处理器的设备运行了一系列测试，其中包括并行运行 128 个 Llama 2 7B 流。 Aug 11, 2023 · Due to its native Apple Silicon support, llama. I'm actually surprised that no one else saw this considering I've seen other 2S systems being discussed in previous issues. 86 ms llama_print_timings: sample time = 378. llama. /scripts/run-all-perf. bin. Whenever that happens, MLX can be integrated to LLM inference software that most of us already use. 6B だと 60 tokens/sec くらい出そう(爆速) Ryzen もノート用だと AI engine(まあ中身は RDNA3(w/ Matrix Core) ですけど)搭載の RyzenAI も出てきているので, M2 Max + Metal と同等のが AMD/Intel CPU でもできるようになってきそうです! Jul 26, 2023 · Download the llama2. Dec 15, 2023 · On M2 Ultra we get a 24% improvement compared to MPS. twitter. to run at a reasonable speed with python llama_cpp. text-generation-webui To execute Llama. cpp is constantly getting performance improvements. cpp and (currently) for good reason. cpp. rtx 4090 has 1008 gb/s. Pretty solid! llama_print_timings: load time = 12638. So ballpark 25% speedup. ) Technically Intel and AMD also have unified memory but I wonder if it actually works. Which makes sense since the M2 Ultra has twice the memory bandwidth. Thanks to Falcon 180B using the same architecture as Falcon 40B, llama. cpp, which began GPU support for the M1 line today. 67x faster than an M2 Ultra (llama-2 7B FP16/Q4_0) for token-generation. MLC/TVM. Jul 18, 2023 · The updated model code for Llama 2 is at the same facebookresearch/llama repo, diff here: meta-llama/llama@6d4c0c2 Seems codewise, the only difference is the addition of GQA on large models, i. Sep 5, 2023 · 10. 3 t/s. They are way cheaper than Apple Studio with M2 ultra. This means that you can do a 70b q8, or a 180b q3_K_M. Running Llama 2 13B on M3 Max. 1B: f16: M2 Ultra: llamafile-0. How far off is Llama 2 70B in terms of hallucination/qualify of output Aug 28, 2023 · The performance of Falcon 7B should be comparable to LLaMA 7B since the computation graph is computationally very similar. ago. GG uses a Mac Ultra. Pure C++ implementation based on ggml, working in the same way as llama. If that number stands up to comprehensive testing, it's a pretty nice upgrade! † Test: Mistral example, converted to fp16 GGUF for Llama. A 128GB MacOS machine should have a working space of 97GB of VRAM; the same as the M1 Ultra Mac Studio. q4_0. The main goal of llama. Reply. Right now I believe the m1 ultra using llama. 👍 2. Mar 21, 2024 · iGPU in Intel® 11th, 12th and 13th Gen Core CPUs. Plain C/C++ implementation without any dependencies. It's already faster than cpu alone - but there are some smart people making it even faster. and I think your hardware will certainly be sufficient for at least a 3B model. 消息一出，圈内瞬间就热闹了起来，大家纷纷 Jan 15, 2024 · 用筆電就能跑 LLaMA ! llama. llama-2-13b-guanaco-qlora. cpp的数据。 为了说服自己买Ultra是值得长期拥有的，我赌本地推理和微调会火。 Sep 1, 2023 · Full F16 precision 34B Code Llama at >20T/s on M2 Ultra | Hacker News. I wonder how many threads you can use make these models work at lightning speed. cpp 教學. For example if you get a machine with 64 GB of Meta Llama 3. This high bandwidth is really a result of Apple having designed a unified memory architecture for the M1 and M2 chips. cpp only very recently added hardware acceleration with m1/m2. 2 t/s) Command used: ollama run mistral --verbose "write a short story of albert einstein of 40 words". The 3090s make no sense as an option. download the 13B-chat,70B-chat only. Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks. Contribute to ggerganov/llama. Prompt eval rate comes in at 192 tokens/s. Hi All, I bought a Mac Studio m2 ultra (partially) for the purpose of doing inference on 65b LLM models in llama. I get 7. We are unlocking the power of large language models. 5 t/s, so about 2X faster than the M3 Max, but the bigger deal is that prefill speed is 126 t/s, over 5X faster than the Mac (a measly 19 t/s). sh to start the download process. 6 t/s. Running it locally via Ollama running the command: % ollama run llama2:13b Llama 2 13B M3 Max Performance GG as in GGML and GGUF is the force behind llama. I know how to use llama. dhiltgen self-assigned this on Mar 11. cpp probably isn't). A quick survey of the thread seems to indicate the 7b parameter LLaMA model does about 20 tokens per second (~4 words per second) on a base model M1 Pro, by taking advantage of Apple Silicon’s Neural Engine. 7 tok/s with LLaMA2 70B q6_K ggml (llama. 1. . the repeat_kv part that repeats the same k/v attention heads on larger models to require less memory for the k/v cache. For example MacBook Pro M2 Max using Llama. It is also relatively easy to estimate the 65B speed based on the performance of smaller models. cpp, and all versions up I know how to use llama. Now we clone the llama from github by simply adding the following code into the Jan 22, 2024 · Intel® Core™ Ultra processors now has released , how can llama. This version now respects the ngl flag completely, and a 120b model now can manually offload 141 layers on the Mac. *Should* be able to at least get 7B model fine tuned with the 96GB of RAM but that is just conjecture. Compared to the price of Two very good nvidia cards and the PC to host it I think the price is reasonable for running Linux on high end Apple Hardware. 21. cpp metal uses mid 300gb/s of bandwidth. Make sure you have streamlit and langchain installed and then execute the Python script: pip install -r requirements. The rust GPU driver is also a joy to use. cpp now supporting Intel GPUs, millions of consumer devices are capable of running inference on Llama. 9. For those with 16 or 32GB of RAM, macOS can run with about ~3GB of RAM if you are really limited memory wise, but it would be wiser to leave an extra 3-4GB if you want to run VS Code or a web browser on the side. この説明を Llama. AMD also has AVX support on all recent CPUs. I'd like to do inference with 70B models, train loras (if possible with the amount of vram/with the m2) and maybe use it for some stable diffusion. Yeah, I plan on putting up a guide soon on exactly how to do this. Apr 3, 2024 · It wasn't a clean sweep for llamafile 0. (This may change tomorrow. I am testing this on an M1 Ultra with 128 GPU of RAM and a 64 core GPU. On the previous version of Llama. In contrast with training large models from scratch (unattainable) or Mar 8, 2024 · Ollama currently uses llama. Q5_K_M. Speed. Anyway, 200GB/s is still quite slow. LLama. cpp is partially running on metal right now (code is in a pull request) so this is coming to the M1/M2 very soon. 99% uses llama. cpp, a package that helps users to run LLMs on their personal hardware, claimed to be running the model on an Apple M2 Ultra. cpp development by creating an account on GitHub. In summary, this PR extends the ggml API and implements Metal shaders/kernels to allow Mar 11, 2023 · Running LLaMA 7B and 13B on a 64GB M2 MacBook Pro with llama. cpp). I’m guessing gpu support will show up within the next few weeks. cpp way faster than Intel CPUs. This is based on the latest build of llama. Grok-1 is a true mystical creature. 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. Note that the latest model iPhones ship with a Neural Engine of similar performance to latest model M-series MacBooks (both iPhone 14 Looking further, 3090 memory bandwidth is only about 15% higher than the m2 ultra. For a 16GB RAM setup, the openassistant-llama2–13b-orca-8k-3319. 然而，Llama 2 最小的模型有7B In the context of Apple CPU/GPU inference, the bottleneck is RAM bandwidth: M1 = 60 GB/s. I thought people might be interested in seeing performance numbers for some different quantisations, running on an AMD EPYC 7502P 32-Core Processor with 256GB of ram (and no GPU). cpp On Mac (Apple Silicon M1/M2) Actually with llama. Fine-tune Llama2 and CodeLLama models, including 70B/35B on Apple M1/M2 devices (for example, Macbook Air or Mac Mini) or consumer nVidia GPUs. However, the problem will be memory bandwidth. cpp is the foundation of a lot of software packages. and more than 2x faster than apple m2 max. Starting to look at kubernetes and playing with various hyper visor setups at the moment, and hoping can properly use all of the systems capabilities. Llama. Oobabooga uses llama-cpp-python wrapper. cpp, first ensure all dependencies are installed. I suspect there isn't a huge difference in speed between miqu q5 and llama2 q8. Mar 9, 2016 · conda create -n llama python=3. Performance is blazing fast, though it is a hurry up and wait pattern. May 14, 2023 · A LLAMA_NUMA=on compile option with libnuma might work for this case, considering how this looks like a decent performance improvement. 4️⃣ M3 Max 40GPU: 67. But, as of writing, it could be a lot slower. 37 GB of RAM, and you have 64 GB to play with, surely you could run multiple instances of the On my 3090+4090 system, a 70B Q4_K_M GGUF inferences at about 15. Besides the specific item, we've published initial tutorials on several topics over the past month: Building instructions for discrete GPUs (AMD, NV, Intel) as well as for MacBooks Saved searches Use saved searches to filter your results more quickly Sep 12, 2023 · Georgi Gerganov, developer of llama. 前不久，Meta前脚发布完开源大语言模型LLaMA，后脚就被网友放出了无门槛下载链接，「惨遭」开放。. Compared to the OpenCL (CLBlast Sep 8, 2023 · first type. So Feb 21, 2024 · Running the 70B LLaMA 2 LLM locally on Metal via llama. copy the download link from email, paste to terminal. 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). MPS backend is measured on an Apple M2 Ultra device using 1 thread. Then, adjust the --n-gpu-layers flag based on your GPU's VRAM capacity for optimal performance. This Mac Studio is located in my company office and I should use the company VPN to connect to it (I can SSH or do Screen Sharing). More hardwares & model sizes coming soon! This is done through the MLC LLM universal deployment projects. Ignoring that, llama. sh to give the authority. sh file, store it on mac. Mar 11, 2023 · 65B running on m1 max/64gb! 🦙🦙🦙🦙🦙🦙🦙 pic. Tokens are generated faster than I can read, but Llama 2 Uncensored M3 Max Performance. Which provide enough unified memory but seem to lack in compability, have slower t/s and especially (!) time to first token. That's faster than my 2070. This is an M2 Ultra Mac Studio with 192GB of RAM I used Oobabooga for the inference program Except for Miqu which only had a q5, I used q8 for everything. cpp or Exllama. The eval rate of the response comes in at 64 tokens/s. The Apple M2 Ultra-powered Mac Studio saw some performance regression in both prompt and evaluation performance for the Q8_0 data type. /download. cpp can run 7B model with 65 t/s, 13B model with 30 t/s, and 65B model with 5 t/s . 【新智元导读】现在，Meta最新的大语言模型LLaMA，可以在搭载苹果芯片的Mac上跑了！. 06 ms / 512 runs ( 0. jr qt fd qw hw ma rw ma ac gd