Llama hardware requirements reddit

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• Just seems puzzling all around. cpp! It runs reasonably well on cpu. It regularly updates the llama. ADMIN MOD. I just got one of these (used) just for this reason. LocalLLaMA) submitted 10 months ago * by fatboy93. Furthermore, Phi-2 matches or outperforms the recently Sort by: Search Comments. We are unlocking the power of large language models. We applied the same method as described in Section 4, training LLaMA 2-13B on a portion of the RedPajama dataset modified such that each data sample has a size of exactly 4096 tokens. Here's what's important to know: The model was trained on 40% more data than LLaMA 1, with double the context length: this should offer a much stronger starting foundation Mar 19, 2023 · I encountered some fun errors when trying to run the llama-13b-4bit models on older Turing architecture cards like the RTX 2080 Ti and Titan RTX. cpp one runs slower, but should still be acceptable in a 16x PCIe slot. We discuss hardware requirements like GPU, RAM, CPU. OpenLLaMA: An Open Reproduction of LLaMA In this repo, we release a permissively licensed open source reproduction of Meta AI's LLaMA large language model. For fast inference, the 3090 and 4090 are sorta king when it comes to consumer hardware, 24GB is an important threshold since it opens up 33b 4bit quant models to run in vram. 4bit needs little bit over one fourth of the original model and one half of the 8bit quantized model. It was quite slow around 1000-1400ms per token but it runs without problems. It allows for GPU acceleration as well if you're into that down the road. 5bpw) On my 3090, I get 50 t/s and can fit 10k with the kV cache in vram. It's probably not as good, but good luck finding someone with full fine And this. Expecting ASICS for LLMs to be hitting the market at some point, similarly to how GPUs got popular for graphic tasks. Continue to r/LocalLLaMA. The performance of an Dolphin model depends heavily on the hardware it's running on. With 3x3090/4090 or A6000+3090/4090 you can do 32K with a bit of room to spare. 5 tokens/second with little context, and ~3. 23) and have run into a puzzling issue. Below are the Dolphin hardware requirements for 4-bit quantization: At least 95% of it is still relevant today because, sadly, not much has changed with regards to hardware. 1 of CUDA toolkit (that can be found here. If you're using the GPTQ version, you'll want a strong GPU with at least 10 gigs of VRAM. Everything seemed to load just fine, and it would Subreddit to discuss about Llama, the large language model created by Meta AI. , coding and math. Running huge models such as Llama 2 70B is possible on a single consumer GPU. I personally prefer 65B with 60/80 layers on the GPU, but this post is about >2048 context sizes so you can look around for a happy medium. 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. Our latest version of Llama – Llama 2 – is now accessible to individuals, creators, researchers, and businesses so they can experiment, innovate, and scale their ideas responsibly. 128k Context Llama 2 Finetunes Using YaRN Interpolation (successor to NTK-aware interpolation) and Flash Attention 2 r/LocalLLaMA • Google Research releases new 10. exllama scales very well with multi-gpu. An insightful illustration from the PagedAttention paper from the authors of vLLM suggests that key-value (KV) pair caching alone can occupy over 30% of a 40GB A100 GPU for a 13B parameter model. I understand Alpaca/Vicuna etc are fine-tuned versions of Meta Llama Models (7B, 13B). For GPU inference, using exllama 70B + 16K context fits comfortably in 48GB A6000 or 2x3090/4090. On a 7B 8-bit model I get 20 tokens/second on my old 2070. Not very practical, but still useful to compare quality on your own data and processes. whl. All in one front to back, and comes with one model already loaded. - CPU: AMD 5800x. , tens of thousands of instructions, it's best to fine-tune the actual model. I'm definitely waiting for this too. In November, I am figuring on upgrading to an 5950x, a 4090, and slotting a second 3060. Would run a 3B model well. The perplexity also is barely better than the corresponding quantization of LLaMA 65B (4. The 4bit 70B model is ~35GB. If you care about quality, I would still recommend quantisation; 8-bit quantisation. In other words, hardware requirements will still increase. Now, MoE models like Mixtral use a gating mechanism to call upon specific 'experts,' which seemingly offers We would like to show you a description here but the site won’t allow us. Given the amount of VRAM needed you might want to provision more than one GPU and use a dedicated inference server like vLLM in order to split your model on several GPUs. I will however need more VRAM to support more people. You can just fit it all with context. For the model itself, take your pick of quantizations from here. With overhead, context and buffers this does not fit in 24GB + 12GB. I get 7. You can add models. Nearly no loss in quality at Q8 but much less VRAM requirement. oobabooga4. While the parameters occupy about 65%. Members Online Chatting with an LLM on Mac terminal using SiLLM built on top of MLX (gemma-2b-it on a MacBook Air 16 GB) We would like to show you a description here but the site won’t allow us. 119K subscribers in the LocalLLaMA community. cpp (here is the version that supports CUDA 12. Worst example is GPU + CPU. RAM: Minimum 16GB for Llama 3 8B, 64GB or more for Llama 3 70B. Mar 7, 2023 · It does not matter where you put the file, you just have to install it. cpp, an open source library designed to allow you to run LLMs locally with relatively low hardware requirements. Software Requirements Aug 31, 2023 · For beefier models like the llama-13b-supercot-GGML, you'll need more powerful hardware. Llama2 itself for basic interaction has been excellent. For 24GB and above, you can pick between high context sizes or smarter models. 11) while being significantly slower (12-15 t/s vs 16-17 t/s). Meta says that "it’s likely that you can fine-tune the Llama 2-13B model using LoRA or QLoRA fine-tuning with a single consumer GPU with 24GB of memory, and using QLoRA requires even less GPU memory and fine-tuning time than LoRA" in their fine-tuning guide. Oobabooga server with openai api, and a client that would just connect via an api token. 7 billion parameters, Phi-2 surpasses the performance of Mistral and Llama-2 models at 7B and 13B parameters on various aggregated benchmarks. Can someone explain what is mixtral 8x7B? Everything is in the title I understood that it was a moe (mixture of expert). com/en/latest/release/windows_support. 6 models on my local machine, which has the following specs: - GPU: RTX3080ti 12GB. But since your command prompt is already navigated to the GTPQ-for-LLaMa folder you might as well place the . things get weirder when looking at We would like to show you a description here but the site won’t allow us. Learn how ollama supports various hardware for AI projects and join the discussion with other enthusiasts on Reddit. At 8 bit quantization you can roughly expect 70 GB RAM/VRAM requirement or 3x 4090 The most cost effective way to run 70B LLMs locally at high speed is a mac studio because the GPU can tap into the huge memory pool and has very good bandwidth despite this. There are also a couple of PRs waiting that should crank these up a bit. But gpt4-x-alpaca 13b sounds promising, from a quick google/reddit search. Combinatorilliance. E. 70B seems to suffer more when doing quantizations than 65B, probably related to the amount of tokens trained. I hope it is useful, and if you have questions please don't hesitate to ask! Sep 9, 2023 · 40. Also using Gradient to fine-tune removes the need for a GPU. cpp or other public llama systems have made changes to use metal/gpu. Join the discussion on r/LocalLLaMA about the benefits and drawbacks of using Mac M2 for local linear models and applications. whl file in there. Quantization is the way to go imho. I remember there was at least one llama-based-model released very shortly after alpaca, and it was supposed to be trained on code, like how there's MedGPT for doctors. I'll be deploying exactly an 70b model on our local network to help users with anything. 0. Using CPU alone, I get 4 tokens/second. They only trained it with 4k token size. cpp it ships with, so idk what caused those problems. The most notable changes would be the existence of the 4060 ti 16gb and the price cut from the 4080 super, but neither of those really change much. my 3070 + R5 3600 runs 13B at ~6. /r/hardware is a place for quality computer hardware news, reviews, and intelligent discussion. Hardware requirements for realtime-ish responses? I'm trying to run mixtral on a ryzen 5600 with 16gb ram and a radeon 5700xt. Finetuning base model > instruction-tuned model albeit depends on the use-case. Hi everyone, I've been working with the LLaVA 1. Without the 3060, it was taking at least 2+ hours. html. I'm testing the 13b-1. If this holds true, I imagine a 8b model trained on a whopping 100T tokens would be required to be run in FP16 to not lose significantly quality. Yes, you can run 13B models by using your GPU and CPU together using Oobabooga or even CPU-only using GPT4All. Training is already hard enough without tossing on weird hardware and trying to get the code working with that. But what we have to understand for the matter here, is that since both those are needed for running apps, they are wired to be fast. Then enter in command prompt: pip install quant_cuda-0. Sabin_Stargem. For example: koboldcpp. • 1 yr. Guide: build llama. Do not confuse backends and frontends: LocalAI, text-generation-webui, LLM Studio, GPT4ALL are frontends, while llama. It rocks. I'm intending to get a laptop for uni and I want it to be able to run an LLM with adequate response time. 2. Disk Space: Llama 3 8B is around 4GB, while Llama 3 70B exceeds 20GB. For 8gb, you're in the sweet spot with a Q5 or 6 7B, consider OpenHermes 2. Llama 2: open source, free for research and commercial use. It has an RK3588, with an ARM based processor capable of running various pytorch applications, and a (surprisingly hefty) NPU capable of using models converted via Rockchip's NPU software available on As a fellow member mentioned: Data quality over model selection. Phi-3 is so good for shitty GPU! I use an integrated ryzen GPU with 512 MB vram, using llamacpp, and the MS phi3 4k instruct gguf, I am seeing between 11-13 TPS on half a gig of ram. Generally not really a huge fan of servers though. Feb 2, 2024 · Find out what is the best desktop build for running LLaMA and Llama-2 large language model locally at home. To get to 70B models you'll want 2 3090s, or 2 4090s to run it faster. •. Below are the Open-LLaMA hardware requirements for 4-bit We would like to show you a description here but the site won’t allow us. LLaMA 3 8B requires around 16GB of disk space and 20GB of VRAM (GPU memory) in FP16. Vram requirements are too high prob for GPT-4 perf on consumer cards (not talking abt GPT-4 proper, but a future model(s) that perf similarly to it). Quick and early benchmark with llama2-chat-13b batch 1 AWQ int4 with int8 KV cache on RTX 4090: 1 concurrent session: 105 tokens/s. I though the point of moe was to have small specialised model and a "manager Yes, search for "llama-cpp" and "ggml" on this subreddit. Aug 31, 2023 · Hardware requirements. I did run 65B on my PC a few days ago (Intel 12600, 64GB DDR4, Fedora 37, 2TB NVMe SSD). cpp . exe --model "llama-2-13b. We're unlocking the power of these large language models. cpp. ggmlv3. With only 2. The difference in output quality between 16-bit (full-precision) and 8-bit is nearly negligible but the difference in hardware requirements and generation speed is massive! 18. But it appears as one big model not 8 small models. 5k user, . Members Online If your DeepSeek Coder V2 is outputting Chinese - your template is probably wrong (as are the official Ollama templates) We would like to show you a description here but the site won’t allow us. If you want to go faster or bigger you'll want to step up the VRAM, like the 4060ti 16GB, or the 3090 24GB. This release includes model weights and starting code for pre-trained and instruction-tuned We would like to show you a description here but the site won’t allow us. cpp/kobold. The performance of an Open-LLaMA model depends heavily on the hardware it's running on. With so many fewer parameters, do we have any sense of the hardware requirements to inference locally on any of the LLaMa models? It’s exciting to think that the SOTA might actually be moving closer to common hardware capabilities rather than further away! Apr 21, 2024 · Ollama is a free and open-source application that allows you to run various large language models, including Llama 3, on your own computer, even with limited resources. 5k bot] for it to understand context. I can see that its original weight are a bit less than 8 times mistral's original weights size. I found that 8 bit is a very good tradeoff between hardware requirements and LLM quality. It takes 15+ seconds per token. This is a research model, not a model meant for practical application. Members Online High Yield: "Apple M3, M3 Pro & M3 Max — Chip Analysis" Question: Option to run LLaMa and LLaMa2 on external hardware (GPU / Hard Drive)? Hello guys! I want to run LLaMa2 and test it, but the system requirements are a bit demanding for my local machine. Sep 27, 2023 · Quantization to mixed-precision is intuitive. cpp officially supports GPU acceleration. e. Next important threshold down is 12gb for 13b, and 8gb for 7b. 8 concurrent sessions: 580 tokens/s. I know that PEFT LoRa methods have reduced the VRAM requirements significantly to fine-tune these models. CPU works but it's slow, the fancy apples can do very large models about 10ish tokens/sec proper VRAM is faster but hard to get very large sizes. LLaMA-v2 megathread. It does put about an 85% load on my little CPU but it generates fine. 3 t/s running Q3_K* on 32gb of cpu memory. cpp on windows with AMD GPUs, and using ROCm Tutorial | Guide ( self. 6 models on the Ollama platform (v0. You can specify thread count as well. Reply. 1) and you'll also need version 12. Now that it works, I can download more new format models. I have seen it requires around of 300GB of hard drive space which i currently don't have available and also 16GB of GPU VRAM, which is a bit more Deploying LLaMA 3 8B is fairly easy but LLaMA 3 70B is another beast. Dec 12, 2023 · For beefier models like the Llama-2-13B-German-Assistant-v4-GPTQ, you'll need more powerful hardware. The base LLaMa models can do prompt completion but are fine-tuned to respond in certain ways. Everyone is using NVidia hardware for training so it'll be a lot easier to do what everyone else is doing. Ollama takes advantage of the performance gains of llama. 4bit Mistral MoE running in llama. Download the model. To run Llama 3 models locally, your system must meet the following prerequisites: Hardware Requirements. For fast inference, the nVidia GeForce RTX3090 & 4090 are sort of must have when it comes to consumer local hardware. Note also that ExLlamaV2 is only two weeks old. Check if your GPU is supported here: https://rocmdocs. But I'd avoid Mac for training. I use 13B GPTQ 4-bit llamas on the 3060, it takes somewhere around 10GB and has never hit 12GB on me yet. The system prompt I came up with [that included the full stat sheet] that made GPT-4 work pretty well was about 2k tokens, then 4k was a chat log sent as a user prompt, and 2k was saved for the bot's response. I think if you want to convert a 30b model into q2, the bottleneck here would be the download size of the pytorch files. 0001 should be fine with batch size 1 and gradient accumulation steps 1 on llama 2 13B, but for bigger models you tend to decrease lr, and for higher batch size you tend to increase lr. But be aware it won't be as fast as GPU-only. AMD 6900 XT, RTX 2060 12GB, RTX 3060 12GB, or RTX 3080 would do the trick. The U/I is a basic OpenAi-looking thing and seems to run fine. It’s commonly cited that GPT-3 175B requires ~800gb vram to load the model and inference. g Mistral derivatives). . Splitting between unequal compute hardware is tricky and usually very inefficient. Running the models. As you probably know, the difference is RAM and VRAM only store stuff required for running applications. Batch size and gradient accumulation steps affect learning rate that you should use, 0. 5 tokens/second at 2k context. From a hardware perspective, a computer today has 3 types of storage : internal, RAM, and VRAM. But whatever, I would have probably stuck with pure llama. q4_K_S. It acts as a broker for the models, so it’s future proof. Once you have downloaded the files, you must first convert them into one ggml float16 file. They just need to be converted to transformers format, and after that they work normally, including with --load-in-4bit and --load-in-8bit . For recommendations on the best computer hardware configurations to handle Open-LLaMA models smoothly, check out this guide: Best Computer for Running LLaMA and LLama-2 Models. The fastest GPU backend is vLLM, the fastest CPU backend is llama. Get $30/mo in computing using Modal. Steps for building llama. It actually runs tolerably fast on the 65b llama, don't forget to increase threadcount to your cpu count not including efficiency cores (I have 16). I feel like LLaMa 13B trained ALPACA-style and then quantized down to 4 bits using something like GPTQ would probably be the sweet spot of performance to hardware requirements right now (ie likely able to run on a 2080 Ti, 3060 12 GB, 3080 Ti, 4070, and anything higher. Bare minimum is a ryzen 7 cpu and 64gigs of ram. ago. cpp on windows with ROCm. Meta Llama 3. cpp, koboldcpp, vLLM and text-generation-inference are backends. If you are on Windows: r/oobaboogazz. Anyhow, you'll need the latest release of llama. possibly even a 3080). gguf quantized llama and llama-like models (e. Llama 3 8b with 15T tokens apparently have noticeable quality drops on high quants such as Q6 and even Q8. 5 Mistral 7B. LLaMA 2 is available for download right now here. A float16 ggml Depending on where you draw the line for AI hardware, let me introduce the Khadas Edge 2 (pro), or the Orange Pi 5 (with up to 32GB of VRAM!). takes about 42gig of RAM to run via Llama. A single 3090 let's you play with 30B models. 23GB of VRAM) for int8 you need one byte per parameter (13GB VRAM for 13B) and using Q4 you need half (7GB for 13B). Note that it's over 3 GB). It's slow but not unusable (about 3-4 tokens/sec on a Ryzen 5900) To calculate the amount of VRAM, if you use fp16 (best quality) you need 2 bytes for every parameter (I. LLaMA (Large Language Model Meta AI), a state-of-the-art foundational large language model designed to help…. However, if you're working with a smaller dataset, a LoRA or qLoRA fine-tuning would be more suitable. We need a thread and discussions on that issue. 7B multilingual machine translation model competitive with Meta's NLLB 54B translation model It doesn't look like the llama. Basically runs . I honestly don't think 4k tokens with LLAMA 2 vanilla would be enough [2k sys, 1. Ah, I was hoping coding, or at least explanations of coding, would be decent. amd. GPU: Powerful GPU with at least 8GB VRAM, preferably an NVIDIA GPU with CUDA support. bin" --threads 12 --stream. the quantize step is done for each sub file individually, meaning if you can quantize the 7gig model you can quantize the rest. 6 and 34b-1. 0-cp310-cp310-win_amd64. To do this, refer to the model repo and mimic their initial training process with your data. unsloth is ~2. Yes, I mean what is special hardware for you? I have Intel i5 and that’s quite enough for the conversion process. Subreddit to discuss about Llama, the large language model created by Meta AI. It seems about as capable as a 7b llama 1 model from 6 months ago. Large language model. • 10 mo. The framework is likely to become faster and easier to use. We provide PyTorch and Jax weights of pre-trained OpenLLaMA models We previously heard that Meta's release of an LLM free for commercial use was imminent and now we finally have more details. As for LLaMA 3 70B, it requires around 140GB of disk space and 160GB of VRAM in FP16. Honestly the quality compared to the best 7B models (which run at 5 token per second on CPU) isn't that different, so for the moment I don't invest in better hardware, waiting for either a breakthrough in quality or cheaper hardware. In this release, we're releasing a public preview of the 7B OpenLLaMA model that has been trained with 200 billion tokens. So now llama. Faster ram/higher bandwidth is faster inference. The most excellent JohannesGaessler GPU additions have been officially merged into ggerganov's game changing llama. Koboldcpp is a standalone exe of llamacpp and extremely easy to deploy. Right now, a Ryzen 3600, 128gb of 3600 RAM, and a RTX 3060 12gb. Notably, it achieves better performance compared to 25x larger Llama-2-70B model on muti-step reasoning tasks, i. 650 subscribers in the LLaMA2 community. You only really need dual 3090's for 65B models. For the CPU infgerence (GGML / GGUF) format, having enough RAM is key. 16gb for 13b with extended context is also noteworthy. I'm testing the models and will update this post with the information so far. We aggressively lower the precision of the model where it has less impact. For recommendations on the best computer hardware configurations to handle Dolphin models smoothly, check out this guide: Best Computer for Running LLaMA and LLama-2 Models. 9 concurrent sessions (24GB VRAM pushed to the max): 619 tokens/s. ~50000 examples for 7B models. The current hardware is quite fast with 13b, takes about half an hour with the initial prompting of a 70b. The ExLlama is very fast while the llama. Dec 12, 2023 · Hardware requirements. We would like to show you a description here but the site won’t allow us. 2x faster in finetuning and they just added Mistral. pyroserenus. * (mostly Q3_K large, 19 GiB, 3. What would I need that my current pc doesn't have? If you have a vast amount of data, i. 1. r/LocalLLaMA: Subreddit to discuss about Llama, the large language model created by Meta AI. 4090s are still the best if you aren't spending tens of thousands of dollars, used 3090s are still extremely good for their price, maximizing Hm, I have no trouble using 4K context with llama2 models via llama-cpp-python. 10 vs 4. My questions are: A model quantized from 16bit to 8 bit will need a little bit over half the requirements of the original 16 bit model. cpp too if there was a server interface back then. 5. Beyond that, I can scale with more 3090s/4090s, but the tokens/s starts to suck. kg bf bw dw su yp cz bv zo rr