A Mac M1 Ultra 64 Core GPU with 128GB of 800GB/s RAM will run a Q8_0 70B at around 5 tokens per second. 5 times more VRAM!!) Key Points: H100 is ~4. 4 models work fine and are smart, I used Exllamav2_HF loader (not for speculative tests above) because I haven't worked out the right sampling parameters. 8% faster. 30B 4bit is demonstrably superior to 13B 8bit, but honestly, you'll be pretty satisfied with the performance of either. I am getting underwelming responses compared to locally running Meta-Llama-3-70B-Instruct-Q5_K_M. Oct 5, 2023 · In the case of llama. thereisonlythedance. Other. 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. To get 100t/s on q8 you would need to have 1. The inference speeds aren’t bad and it uses a fraction of the vram allowing me to load more models of different types and have them running concurrently. 55 LLama 2 70B to Q2 LLama 2 70B and see just what kind of difference that makes. 1_ How many GPUs with how much VRAM, what kind of CPU, how much RAM? Is multiple SSDs in a striped RAID helpful for loading the models into (V)RAM faster? I read that 70B models require more that 70GB VRAM. So my question is, let us imagine I get a 24 GB VRAM card I'm currently using Meta-Llama-3-70B-Instruct-Q5_K_M. 6 bit and 3 bit was quite significant. 3 2. Trained on 15T tokens. Q_8 to Q_6k seems the most damaging, when with other models it felt like Q_6k was as good as fp16. Macs with 32GB of memory can run 70B models with the GPU. To improve the inference efficiency of Llama 3 models, we’ve adopted grouped query attention (GQA) across both the 8B and 70B sizes. For the larger models, Miqu merges and Command R+ remain superior for instruct-style long context generation, but I prefer Llama-3 70B for assistant style back and forths. But all the Llama 2 models I've used so far can't reach Guanaco 33B's coherence and intelligence levels (no 70B GGML available yet for me to try). New Tiktoken-based tokenizer with a vocabulary of 128k tokens. Pretrained on 15 trillion tokens. On my windows machine it is the same, i just tested it. I get that with A770 16Gb and 64gb ram using vulkan and q4 70b models. Also, there is a very big difference in responses between Q5_K_M. To determine if you have too many layers on Win 11, use Task Manager (Ctrl+Alt+Esc). I'm running 70B Q5 KM ok on an AMD 5950 and two 980 pro ssds, 3080, 128gb DDR5. The quality differential shouldn't be that big and it'll be way faster. Start with cloud GPUs. These were the only I could compare because they can be fully offloaded to vram of respective cards. I run a 5bpw exl2 quant of most llama 3 70b models at 7-8 tokens per second with a 4090,4060ti 16gb x2. co/unsloth Downloading will now be 4x faster! Working on adding Llama-3 into Unsloth which make finetuning 2x faster and use 80% less VRAM, and inference will natively be 2x faster. 4bit Mistral MoE running in llama. In fact, it did so well in my tests and normal use that I believe this to be the best local model I've ever used – and you know I've seen a lot of models It would make sense to start with LLaMA 33B and Falcon 40B, which ought to be doable. Looks like it is the model of choice for ~56GB VRAM configs If you're doing a full tune it's gonna be like 15x that which is way out of your range. I'm using deepspeed zero stage 3 and Llama 70b in FP16 but still We would like to show you a description here but the site won’t allow us. Price: $28,000 (approximately one kidney) Performance: 370 tokens/s/GPU (FP16), but it doesn't fit into one. Not a direct answer to your question, but my P40 rig (which fully loads all layers for a Q5_M 70B model on only P40s) gets about 7-8 tokens per second with low context, and about 3-4 a second Subreddit to discuss about Llama, the large language model created by Meta AI. 28gb needed. 5 on mistral 7b q8 and 2. They‘ve built a smart, engaging chatbot. Lambda cloud is what I recommend. That runs very very well. 2-2. Now start generating. A new and improved Goliath -like merge of Miqu and lzlv (my favorite 70B). 8k context length. Software Requirements Amgadoz. Your wallet might stop crying (not really) 192GB HBM3 on MI300X. 5 bpw as my main model for a bit now. Disk Space: Llama 3 8B is around 4GB, while Llama 3 70B exceeds 20GB. 24GB VRAM seems to be the sweet spot for reasonable price:performance, and 48GB for excellent performance . Text in to text out only on the models (currently). As a rule of thumb, you can expect an actual performance of ~60% of theoretical bandwidth, that is, 55 GB/s. Subreddit to discuss about Llama, the large language model created by Meta AI. 5. Those 2 4060ti have been the best $400 I've spent in a long time. How do I deploy LLama 3 70B and achieve the same/ similar response time as OpenAI’s APIs? Subreddit to discuss about Llama, the large language model created by Meta AI. Its truly the dream "unlimited" vram setup if it works. RAM: Minimum 16GB for Llama 3 8B, 64GB or more for Llama 3 70B. •. News. it's still useful, but it's prohibitively compute intensive to make them all with imatrix for 70B and have it out in a reasonable amount of time, I may go back and redo the others with imatrix 2 channel of DDR 5600 MT/s has a theoretical bandwidth of 5600 x 2 channels x 8 byte (64 bit) bus width, that is, 89. gguf and Q4_K_M. 16GB not enough vram in my 4060Ti to load 33/34 models fully, and I've not tried yet with partial. To run Llama 3 models locally, your system must meet the following prerequisites: Hardware Requirements. granted, it's the only 70B i've ever used, and i'm accustomed to 7b/13b models. cpp, but they find it too slow to be a chatbot, and they are right. Midnight Miqu 1. It looks like the LoRa weights need to be combined with the original Either that or they're taking a massive loss. 5bpw) On my 3090, I get 50 t/s and can fit 10k with the kV cache in vram. 8 on llama 2 13b q8. Those run great. But since you're going for a nvidia card, it might be slightly faster. I'm using fresh llama. Phind captures instructions amazingly but isn't as proficient of a developer. On dual 3090's I can get 4-6t/s with a Q4 and I'm not happy with it. It should stay at zero. 2. 5-4. . Meta releases Code Llama2-70B, claims 67+ Humaneval. There's a free Chatgpt bot, Open Assistant bot (Open-source model), AI image generator bot, Perplexity AI bot, 🤖 GPT-4 bot ( Now Llama 3 70b Q5_K_M GGUF on RAM + VRAM. Llama 2 7B is priced at 0. Let's call the difference 4x. 7B) m1 ultra: Dolphin-Mixtral 8x7B (big hopes for llama3 70b or yet unreleased wizard 70b) Upd: WizardLM 8x22B outperforms Mixtral 8x7B dramatically even at Q2_K. So Replicate might be cheaper for applications having long prompts and short outputs. I recently got a 32GB M1 Mac Studio. Plus quite a bit of time for prompt ingestion. Deploying LLaMA 3 8B is fairly easy but LLaMA 3 70B is another beast. 5 bpw (maybe a bit higher) should be useable for a 16GB VRAM card. 3, trained on the Mistral-7B base, achieves 64. Use axolotl; I also had much better luck with qlora and zero stage 2 than trying to do a full fine tune and zero stage 3. Plans to release multimodal versions of llama 3 later Plans to release larger context windows later. Yeah, Mistral 7B is still a better base for fine tuning than Llama 3-8B. 10$ per 1M input tokens, compared to 0. 8b parameter version and 70b parameter version. 0, it now achieves top rank with double perfect scores in my LLM comparisons/tests. It is a Q3_K_S model so the 2nd smallest for 70B in GGUF format, but still it's a 70B model. With 3x3090/4090 or A6000+3090/4090 you can do 32K with a bit of room to spare. You can't run anything on 32GB that won't also run on 24GB, so your two 4060tis don't really get you anything that a single 3090 wouldn't give you. Love this idea. Gemma 27b at rank 12. Smaller models i can just shove to the 4090 and hit 30+tk/s with exl2. cpp! It runs reasonably well on cpu. " Inference speed on 4. 6 GB/s. With other models like Mistral, or even Mixtral, it We would like to show you a description here but the site won’t allow us. 4-0. Costs $1. 60 to $1 an hour you can figure out what you need first. The question is, how can i make 10x faster, the optimal runtime around 0. With 4060 Ti 16gb vram, 43 layers offloaded its around 4-6sec. I get 7. this was on llama 2. Open the performance tab -> GPU and look at the graph at the very bottom, called " Shared GPU memory usage". Keep in mind that there is some multi gpu overhead, so with 2x24gb cards you can't use the entire 48gb. Resources Initially noted by Daniel from Unsloth that some special tokens are untrained in the base Llama 3 model, which led to a lot of fine-tuning issues for people especially if you add your own tokens or train on the instruct Further testing 2. Just seems puzzling all around. 4bpw vram loading different context sizes show: 21. gguf and it's decent in terms of quality. 2_ How much VRAM do you need for full 70B, how much for quantized? 3_ How noticeable is performance difference between full and quantized? So maybe 34B 3. Thanks for the advice. Reply reply Rare-Side-6657 We would like to show you a description here but the site won’t allow us. So we have the memory requirements of a 56b model, but the compute of a 12b, and the performance of a 70b. , but here are things I did: Has anyone tested out the new 2-bit AQLM quants for llama 3 70b and compared it to an equivalent or slightly higher GGUF quant, like around IQ2/IQ3? The size is slightly smaller than a standard IQ2_XS gguf 4090. Any comments welcome! We would like to show you a description here but the site won’t allow us. Has anyone tried using this GPU with ExLlama for 33/34b models? What's your experience? Additionally, I'm curious about offloading speeds for…. you will get 8k if you have integrated graphics or run ubuntu in a server environment. 84gb for 1024. To this end, we developed a new high-quality human evaluation set. I guess you can try to offload 18 layers on GPU and keep even more spare RAM for yourself. At 0. Many people actually can run this model via llama. Also, Goliath-120b Q3_K_M or L GGUF on RAM + VRAM for story writing. Laptop: WizardLM2 7B (llama3 is a bit dumber, checking Starling 10. For GPU inference, using exllama 70B + 16K context fits comfortably in 48GB A6000 or 2x3090/4090. My organization can unlock up to $750 000USD in cloud credits for this project. So you can either save money by buying one 3090, or you can spend a bit more to get two 3090s and run 70b models with your 48GB VRAM. My primary use case, in very simplified form, is to take in large amounts of web-based text (>10 7 pages at a time) as input, have the LLM "read" these documents, and then (1) index these based on word vectors and (2) condense each document down to 1-3 sentence We would like to show you a description here but the site won’t allow us. 7800X3D. I'm mostly been testing with 7/13B models, but I might test larger ones when I'm free this weekend. 4 and 3 bit model to a 5-bit model of the same parameter count. In fact I'm done mostly but Llama 3 is surprisingly updated with . 1-0. 4bpw. You should use vLLM & let it allocate that remaining space for KV Cache this giving faster performance with concurrent/continuous batching. The 5 bit will have significantly lower perplexity, increasing the quality of the responses. To get a more accurate comparison of output quality, download a GGUF of both models at the same bit size, then compare. 0bpw is around ±7-8 token/second. 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) 70b models can only be run at 1-2t/s on upwards of 8gb vram gpu, and 32gb ram. 8B and 70B. In your specific case, you're comparing the quality of a 2. gguf. The LLaMA paper apparently has a figure of 380 tokens/sec/GPU when training, but they likely achieve much better performance compared to this scenario, because they've over-provisioned the VRAM to get better training speed. Although these are for quantizations optimized for speed so depending on what model you're trying to use it might be slower. I'll probably stick with Euryale 1. e. You will need ram = vram, ddr3 is enough, pcle 3x8 is enough, a good psu. During Llama 3 development, Meta developed a new human evaluation set: In the development of Llama 3, we looked at model performance on standard benchmarks and also sought to optimize for performance for real-world scenarios. It mostly depends on your ram bandwith, with dual channel ddr4 you should have around 3. decoder only architecture. We would like to show you a description here but the site won’t allow us. Those were done on exllamav2 exclusively (including the gptq 64g model) and the bpws and their VRAM reqs are (mostly to just load, without taking in mind, the cache and the context): Just uploaded 4bit pre quantized bitsandbytes (can do GGUF if people want) versions of Llama-3's 8b instruct and base versions on Unsloth's HF page! https://huggingface. The attention module is shared between the models, the feed forward network is split. Members Online Abliterated-v3: Details about the methodology, FAQ, source code; New Phi-3-mini-128k and Phi-3-vision-128k, re-abliterated Llama-3-70B-Instruct, and new "Geminified" model. It is still good to try running the 70b for summarization tasks. Use multiple prompts. It generally sounds like they’re going for an iterative release. The other option is an Apple Silicon Mac with fast RAM. 8) We would like to show you a description here but the site won’t allow us. A6000, maybe dual A6000. 5t/s. The issue I’m facing is that it’s painfully slow to run because of its size. 55 gguf tho. According to the github: "By design, Aphrodite takes up 90% of your GPU's VRAM. It would be interesting to compare Q2. If you have a huge case to fit two second hand 3090 this is the way. I’ve proposed LLama 3 70B as an alternative that’s equally performant. A full fine tune on a 70B requires serious resources, rule of thumb is 12x full weights of the base model. LLaMA 3 8B requires around 16GB of disk space and 20GB of VRAM (GPU memory) in FP16. Apr 18, 2024 · Compared to Llama 2, we made several key improvements. It's poor. Beyond that, I can scale with more 3090s/4090s, but the tokens/s starts to suck. llama3-70B as of now). I have a GTX 1650 ( which has 4 gb VRAM). Hope Meta brings out the 34B soon and we'll get a GGML as well. I use it to code a important (to me) project. Today at 9:00am PST (UTC-7) for the official release. GPU: Powerful GPU with at least 8GB VRAM, preferably an NVIDIA GPU with CUDA support. I was excited to see how big of a model it could run. Mixtral 8x7B was also quite nice Synthia-7B-v1. Thanks! We have a public discord server. 7gb for 512. (LLaMA-2-70B-Chat is 66. At no point at time the graph should show anything. Llama-3 still the best for english queries? Actually if the smaller memory of gemma is important than perhaps gemma is a better choice for english queries. Use lmdeploy and run concurrent requests or use Tree Of Thought reasoning. Has anyone noticed a significant difference when using llama-3-70B Q4, Q5 and higher quants? Also, not only llama, but other 70B+ models as well. *Stable Diffusion needs 8gb Vram (according to Google), so that at least would actually necessitate a GPU upgrade, unlike llama. As a note for anyone else going this route, you might want For inference (tests, benchmarks, etc) you want the most amount of VRAM so you can run either more instances or the largest models available (i. Tried out Aphrodite by the way, got it running but I kept running out of memory trying to get EXL2 models loaded. 225 t/s on 4000gb (2T parameter f16)model could work, couldn't it? It would work nicely with 70B+ models and the higher bitrate sizes beyond Q4! We would like to show you a description here but the site won’t allow us. Meta has released the checkpoints of a new series of code models. 21. For Llama 3 8B, using Q_6k brings it down to the quality of a 13b model (like vicuna), still better than other 7B/8B models but not as good as Q_8 or fp16, specifically in instruction following. Anything above that is slow af Here's the thing: 32GB is a weird in-between VRAM capacity. A 70B_q4_k_m quant (70B parameters quantized to ~4. This paper looked at 2 bit-s effect and found the difference between 2 bit, 2. I know that benchmark results are pretty much the same for Q4 and Q5, and that as the model size increases, the difference in quants becomes less noticeable, but I'd like to hear your experiences and What is the intent of the server: Run 24/7 as production server, I don't exactly know the concurrent load I should expect, but the LLM running on it should be able to serve a 4bit 70B llama2 model to 5 concurrent users in a rate of 5 t/s each ( rough estimate ), so 25t/s at least, aiming closer to 100 t/s would be ideal. They have the same llama 2 license. Not sure how to get this to run on something like oobabooga yet. 22. Server will also run 10 We would like to show you a description here but the site won’t allow us. You are better off using Together. And if you're using SD at the same time that probably means 12gb Vram wouldn't be enough, but that's my guess. In 7b/8b q8 model, I've seen cublas perform better on a 3060 than vulkan on a770. Hey u/adesigne, if your post is a ChatGPT conversation screenshot, please reply with the conversation link or prompt. 2 tokens per second. However, it's literally crawling along at ~1. Llama 3 uses a tokenizer with a vocabulary of 128K tokens that encodes language much more efficiently, which leads to substantially improved model performance. 45t/s near the end, set at 8196 context. The favorite fits into your VRAM with decent quants. Meta Llama-3-8b Instruct spotted on Azuremarketplace. The real question is if there's much of a point point of a 40b model when you can fit a 5 bit quant of the 70b into the roughly same memory. Initially I was EXTREMELY excited when llama-2 was released, assuming that finetunes would further improve its abilities, but as this post correctly points out, llama-2 finetunes of guanaco and airoboros are less capable in the creative fiction department, not more, in various aspects (see previously mentioned post for the deets). 32GB DDR5 6000 CL30. You might be able to run a heavily quantised 70b, but I'll be surprised if you break 0. 2 and 2-2. SqueezeLLM got strong results for 3 bit, but interestingly decided not to push 2 bit. 1,25 token\s. cpp, you can't load q2 fully in gpu memory because the smallest size is 3. 70B seems to suffer more when doing quantizations than 65B, probably related to the amount of tokens trained. 05$ for Replicate). 0bpw using EXL2 with 16-32k context. Man, ChatGPT's business model is dead :X. This is a follow-up to my previous posts here: New Model RP Comparison/Test (7 models tested) and Big Model Comparison/Test (13 models tested) Originally planned as a single test of 20+ models, I'm splitting it up in two segments to keep the post managable in size: First the smaller models (13B + 34B), then the bigger ones (70B + 180B). We have no data for 2. 72gb for 7168. 8b makes sense because you can't really quant a 70b that low, but 70b should be possible to quant down to equivalent of 20b somewhat plausibly, with a fine-tune to get it back together. 99 per hour. its not the same as your specific use case though. NET 8. Llama 3 is out of competition. (Total 72GB VRAM) Note that if you use a single GPU, it uses less VRAM (so a A6000 with 48GB VRAM can fit more than 2x24 GB GPUs, or a H100/A100 80GB can fit larger models than 3x24+1x8, or similar) Hi, I am trying to build a machine to run a self-hosted copy of LLaMA 2 70B for a web search / indexing project I'm working on. With a 3090 and sufficient system RAM, you can run 70b models but they'll be slow. I’m really interested in the private groups ability, getting together with 7-8 others to share gpu. The best would be to run like 3-4B models. You are going to have to run a very low quant to be able to run on it on a single 4090, likely will be very poor quality answers. Though, if I have the time to wait for LLama-3 at rank 5. It's just that the 33/34b are my heavier hitter models. cpp builds, following the README, and using the a fine-tune based off a very recent pull of the Llama 3 70B Instruct model (the official Meta repo). With --no-mmap the data goes straight into the vram. 5 is good for third person narrative, I think v1 is a bit better if you want first person dialog. The graphs from the paper would suggest that, IMHO. 6. Memory: 80GB (MI300X has almost 2. 75x. 5 bits per weight) is 42. Personally, I'm waiting until novel forms of hardware are created before Lastly and that one probably works you could run two different instances of LLms for example a bigger one on the 3090 and a smaller on the p40 i asume. gguf (testing by my random prompts). So, to a easier comparison, from better to worse perplex: Non-SuperHOT model 2048 context > Non-SuperHOT model 8192 context and Alpha 4 > SuperHOT model 8192 context and compress_pos_emb 4. The quants and tests were made on the great airoboros-l2-70b-gpt4-1. With many trials and errors I can run llama 8b at 8t/s for prompt evals and 4 t/s for generation evals. Zuck FTW. It may be can't run it at max context. 4. I use the 70B and its hallucination is to add the question into the answer sometimes but it always gives good datapoints in data analysis. Barely enough to notice :) MI300X costs 46% less. And I have 33 layers offloaded to the GPU which results in ~23GB of VRAM being used with 1GB of VRAM left over. Deepseek is the better coder, but it doesn't understand instructions as well. But 7k is fine, I do alpha_value 1. g. cpp. 5sec. AI or something if you really want to run 70B. * (mostly Q3_K large, 19 GiB, 3. It won't have the memory requirements of a 56b model, it's 87gb vs 120gb of 8 separate mistral 7b. midnight miqu is the far and away the best model i've ever used for RP. It has given me 3-4 tokens/s depending on context size. exllama scales very well with multi-gpu. I have run stuff like Mixtral 8x7B quantized on my computer, despite being twice as big as my VRAM, by offloading. It turns out that's 70B. Should i use a 2nd 4060Ti or invest in a 4090? Or any other better GPU? Im using CBLAST, the model using around 9GB VRAM and 27 CPU core (total 28). 24 GB VRAM would get me to run that, but I think spending something like over $2k just to run 7B is a bit extreme. You can run inference at 4,8 or 16 bit, (and it would be best if you can test them all for your specific use-cases, it's not as simple as always running the smallest bit quant). Gemma still has a wide range for confidence intervals so may change next week. 85 on the 4-evals used in HuggingFace. 1 model. I personally see no difference in output for use cases like storytelling or general knowledge, but there is a difference when it comes to precision in output, so programming and function calling are things We would like to show you a description here but the site won’t allow us. The endpoint looks down for me. The perplexity also is barely better than the corresponding quantization of LLaMA 65B (4. Groq's output tokens are significantly cheaper, but not the input tokens (e. 5bits/bps: ~45 GB VRAM 6bits/bpw: ~54GB VRAM 7bits/bpw: ~68GB VRAM Tests were made on my personal PC which has 2x4090 and 1x3090. Will occupy about 53GB of RAM and 8GB of VRAM with 9 offloaded layers using llama. 11) while being significantly slower (12-15 t/s vs 16-17 t/s). 5 GB. 2 bpw is usually trash. 23. Better than the unannounced v1. 70B models are just on a completely new level compared to the small models. the bigger the quant the less the imatrix matters because there's less aggressive squishing that needs to happen. 0 knowledge so I'm refactoring. Per 1k, 0. Also, sadly, there is no 34B model released yet for LLaMA-2 to test if a smaller, less quantized model produces better output than this extreme quantized 70B one. Or something like the K80 that's 2-in-1. So while you can run something that calls itself 70B on CPU, it may not be useful outside testing/proof of concept use cases. It's a reasonable speed for the size and cost. A second GPU would fix this, I presume. 10 vs 4. 3 and 2. When you partially load the q2 model to ram (the correct way, not the windows way), you get 3t/s initially at -ngl 45 , drops to 2. 4bit is optimal for performance . About 1. For a good experience, you need two Nvidia 24GB VRAM cards to run a 70B model at 5. 3 bpw 70B llama 3 models scores very similarly in benchmarks to 16 bpw in gptq. I don't really have anything to compare to. 15gb for 2048. I'm using OobaBooga and Tensor core box/etc are all checked. Edit: Also managed to get really coherent results on 65B, 4K ctx using NTK RoPE scaling. Rank 1 and 2 if you consider only models you can run locally. 09gb for 4096. For some reason I thanked it for its outstanding work and it started asking me LLaMA-2 with 70B params has been released by Meta AI. Also the web server shows additional parameters to fine tune, so look at applying various different parameters. Alpaca LoRa - finetuning possible on 24GB VRAM now (but LoRA) Neat! I'm hoping someone can do a trained 13B model to share. This is exciting, but I'm going to need to wait for someone to put together a guide. I can tell you form experience I have a Very similar system memory wise and I have tried and failed at running 34b and 70b models at acceptable speeds, stuck with MOE models they provide the best kind of balance for our kind of setup. Loading a 7gb model into vram without --no-mmap, my ram usage goes up by 7gb, then it loads into the vram, but the ram usage stays. Llama-3-8B with untrained tokens embedding weights adjusted for better training/NaN gradients during fine-tuning. 3 t/s running Q3_K* on 32gb of cpu memory. From their announcement: Today we’re releasing Code Llama 70B: a new, more performant version of our LLM for code generation — available under the same license as previous And since I'm used to LLaMA 33B, the Llama 2 13B is a step back, even if it's supposed to be almost comparable. Basically, it seems that NTK RoPE scaling is better that we expected. I do include Llama 3 8b in my coding workflows, though, so I actually do like it for coding. If you are able to saturate the gpu bandwidth (of 3090) with a godly compression algorithm, then 0. 5 tokens/second. I rarely use 70b q4_k_m for summary (ram+vram), and use mistral on other devices, but only for writing stories. kn cn zl tg da du hz nc fv yo