70b model on 4090 reddit. "He saved us when he didn't have to.

7gb. If you imagine exllamav2 running fp16, then the difference of this inference library to HF is still 1. I understood egpu don't give too much troubles driver wish So you could give it a shot You should still be bottlenecked by vram bandwidth not by thunderbolt so not loosing too much on that. Finishing first half of layers in card 1, then card 2. 6. 35 and a 120B I can run at 3. You can do a lot with the 24 GB in a 4090 RTX. From what I read, that should only affect the speed of loading the model, not the actual inferrence. I have a 4090 + 3090 running exllama 14,24 gpu split which fills the 3090 to about 95% and leaves the 4090 at around 60-70% at 8k. Running the 70B model at q2, AI get around 4 t/s. I run desktop ubuntu, with a 3090 powering the graphics, consuming 0. It has been fine-tuned for instruction following as well as having long-form conversations. Was able to load the above model on my RTX-3090 and it works, but I'm not seeing anywhere near this kind of performance: Output generated in 205. "He saved us when he didn't have to. The M1 Ultra Mac Studio with 128GB costs far less ($3700 or so) and the inference speed is identical So the main draw for me of the studio is that I can run a 70b q8 at 10 tokens per second, which for that quality I'm more than happy with the slower run, and can even The Ultra model doesn't provide 96GB, that's only available with the Max. 2GB of ram, running in LM Studio with n_gpu_layers set to 25/80, I was able to get ~1. For 70b, 2 p40s give you like 7-8ts and 2 3090s give you 18t/s. Members Online 🐺🐦‍⬛ Huge LLM Comparison/Test: 39 models tested (7B-70B + ChatGPT/GPT-4) A friend told me that for 70b when using q4, performance drops by 10%. Mistral AI new release. Beyond that, I can scale with more 3090s/4090s, but the tokens/s starts to suck. A100's are just so much better than A6000 or 4090's for LLM. For example, current llama. 4bpw quantized mode, but in Chinese it's terrible, very very terrible and completely unusable. 8 tokens/s, regardless of the prompt. 2 70b. All this sequoia thing is doing for you is forcing you to store the full, four-times-larger weights on your PC. The 4060 TI (16GB) only costs about a third of the price (~650Eur). Thanks to patch provided by emvw7yf below, the model now runs at almost 10 tokens per second for 1500 context length. On a big (70B) model that doesn't fit into allocated VRAM, the ROCm inferences slower than CPU w/ -ngl 0 (CLBlast crashes), and CPU perf is about as expected - about 1. Reply. Mix and match will get you that same 7-8t/s. I have tried a few GGUF models but due to my lack of acknowledgement I found them too slow to use. This applies to any model. 65 bpw is also popular for being roughly equivalent to a 4-bit GPTQ quant with 32g act order and should enable you to easily HOF, Strix and Suprim X have the highest VRM of all cards. Also, just a fyi the Llama-2-13b-hf model is a base model, so you won't really get a chat or instruct experience out of it. Pretty much, if you don't think you'll be able to get nvidia p2p working, and your tasks can't be parallelized between GPUs, go with a We would like to show you a description here but the site won’t allow us. I got 192GB Mac Studio with one idea "there's no way any time in near future there'll be local models that wouldn't fit in this thing". 70b/65b models work with llama. I guess in 128Gb of memory we could fit even 4 bit quantized 170B model. No errors, just runs continuously. Tested: 24gb max context sizes with 70B exllamav2. Load up with 8k context and get settled in with the mores of the model and how to cue it to return in the style you like. So a 13b model on the 4090 is almost twice as fast as it running on the M2. Are layers executed concurrently across gpus as multiple feed forward passes are conducted or is it more similar to model parallel where only one layer executes on one gpu at a time. Grok & Mixtral 8x22B: Let us introduce ourselves. Brand new with warranty and as cheap as secondhand. 24 gb VRAM for 200 bucks is insane. Speed was: `Output generated in 424. It won't involve cpu at all. Anything running off of your cpu/ram is going to be painfully slow. If you go to 4 bit, you still need 35 GB VRAM, if you want to run the model completely in GPU. exllamav2 gives 194 t/s on 7B 4bit 4090. Try out the -chat version, or any of the plethora of fine-tunes (guanaco, wizard, vicuna, etc). Yes, provided the FP16 model is in gguf format already. torchrun --nproc_per_node 8 example_chat_completion. 4090 24gb is 3x higher price, but will go for it if its make faster, 5 times faster gonna be enough for real time data processing. Stalked online platforms for single 3090s that I guess stores actually found they have single units of leftover phased out stock for. Edit: I use LZLV 4. I've also run 33b models locally. 4090 is much more expensive than 3090 but it wouldn’t give you that more benefit when it comes to LLMs (at least regarding inference. But you are also going to have to deal with obsolescence, lack of good FP16 ops and being suck with llama. 5 Pro creeps closer to GPT-4o, at competitive prices. A modern CPU with a 4090 and a 70b at Q4 doing a 50% offload would be significantly faster than 2t/s. If I had the 4080, then used oobabooga/kobold/&c. Recently, some people appear to be in the dark on the maximum context when using certain exllamav2 model, as well as some issues surrounding windows drivers skewing performance. Don't forget ur 4090 laptop has a ~500gb/s memory bandwidth and the 3090 is about ~1tb/s iirc. bin" is 13, but I subtract one to account for variations, so 12. However, when attempting to run the 70b versions, the model loads and then runs on the GPUs at 100% forever. All Synthia models are uncensored. Also released was a 1. 3. Yeah I run the uncensored version and it’s incredible. The 3090 can't access the memory on the P40, and just using the P40 as swap space would be even less efficient than using system memory. As a rule of thumb, you can expect an actual performance of ~60% of theoretical bandwidth, that is, 55 GB/s. While training, it can be up to 2x times faster. From what I have read the increased context size makes it difficult for the 70B model to run on a split GPU, as the context has to be on both cards. 1 x RTX 4090 vs 3 x RTX 4060TI. I happened to do this yesterday, testing the Dromedary 65B 4bit GPTQ I'd just uploaded to HF. For a exllama2 quant of a 70b model, you can fit ~5. New 120B model. Auto-regressive causal LM created by combining 2x finetuned Llama-2 70B into one. If you don't care about OC, the FE is the best at MSRP. The 70b model is slower than the 34b model, yet it should be the currently best 70b instruct tune (as it should be better than the 2 bit chat-llama-70b) to use on Exactly! RTX 3090 has the best or at least one of the best vram/dollar value (rtx 3060 and p 40 are also good choices, but the first is smaller and the latter is slower). ADA is newest version of gpu. From Bunyan Hui’s Twitter announcement: “We are proud to present our sincere open-source works: Qwen-72B and Qwen-1. 8). With 3x3090/4090 or A6000+3090/4090 you can do 32K with a bit of room to spare. This time with the most recent quip# library, currently redoing the 34b model with it, too. One 48GB card should be fine, though. Detailed Test Report. Just brainstorming here while windowshopping for new GPU. exllama scales very well with multi-gpu. The larger the model, the less it suffers from weight quantization. At 70b, Q4 is effectively indistinguishable from fp16 quality-wise. In tasks that can utilize 2 cards, dual 3090 wins. For more details, please refer to Opinion regarding the optimal model. ai/blog/unleash-the-power-of-l We would like to show you a description here but the site won’t allow us. The Xwin and Synthia models are popular at that parameter count. A q4 34B model can fit in the full VRAM of a 3090, and you should get 20 t/s. Its a 28 core system, and enables 27 cpu cores to the llama. The "fp16" part is more of a curse than a blessing. 000Eur). You could try GGML 65B and 70B models provided you have enough RAM, but I'm not sure if they would be fast enough for you. Either in settings or "--load-in-8bit" in the command line when you start the server. You could run 70b models or anything smaller locally, offloading some layers to GPU. In any situation where you compare them 1v1, a 4090 wins over a 3090. These are the best models in terms of quality, speed, context. at least 128. I personally recommend XWIN or LZLV 70B. General rule of thumb is that the lowest quant of the biggest model you can run is better than the highest quant of lower sized models, BUT llama 1 v llama 2 can be a different story, where quite a few people feel that the 13bs are quite competitive, if not better than, the old 30bs. 07 tokens/s, 15 tokens, context 1829, seed 780703060) For reference, here is my command line: python server. 8-1. Maybe even switch to the new 7B and 13B code instruct models for finetunes going forward, if the notion that better coding performance = improved general intelligence holds true. Run the 5_KM for your setup you can reach 10t-14t / s with high context. P40 will bottleneck your 3090. 6x VRAM is a limit of model quality you can run, not speed. Edit: Do not offload all the layers into the GPU in LM Studio, around 10-15 layers are enough for these models depending on the context size. 65 bit, EXL2 format on 2x3090's. 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. Oh, a tip I picked up while playing: It makes a big difference for future responses if you go back and edit the response after its generated, get rid or, rephrase words and replace parts you It shows that quantized model of certain size have a certain perplexity, and even 2 bit quantized 170B model would be better that not quantized 70B model. I run 13b GGML and GGUF models with 4k context on a 4070 Ti with 32Gb of system RAM. Prices here in europe are pretty crazy for the 4090 (barely below 2. py or convert-hf-to-gguf. The Ultra offers 64GB, 128GB, or 192GB options. Anyways im guessing im looking to get responses from a 70b llama 2 model in 5-20t/s range might be a more specific detail, 5-10t/s is fine though I basically need help on everything haha, mobo to hold 2 cards, psu to maintain the whole machine efficiently, enough ram and cpu to keep up (baseline is fine), cooling options, case etc, I might game I mostly use 30/33/34b GPTQ models. While I understand people have very cheap GPU's with 4 or 6 GB's of VRam the ultimate for a local LLM is a 4090 with a good CPU. However, I only have a 4090, which is fine for English conversations in 2. My 4090 gets 50, a 4090 is 60% bigger than a 4080. We would like to show you a description here but the site won’t allow us. 31 seconds (3. I get like 1. This model is license friendly, and follows the same license with Meta Llama-2. There is a much lower maximum t/s due to model size being x4 larger than 4bit model. 0. Speaking from experience, also on a 4090, I would stick with 13B. The perplexity difference between q6 and q8 for 70b is very small, like only measurable because it's all inside a computer and not some expensive test equipment. Funny I use P40 for SD and the 3090s for the model. The 13B coding model beats the vanilla 70B model in coding performance by quite a large margin! Wizard-30B-GPTQ is good so far on 4090. Just have to be careful on choosing the inference engine. Used 3090 still go for 1000+ Eur as well here. I have tested 33b 4bit training, you may need a small rank size to increase Quantized orca 70b 2 bit model. 0 achieves a substantial and comprehensive improvement on coding, mathematical reasoning and open-domain conversation capacities. Also, check out Clipboard Conqueror. Run yourself the 65b 4bit model split between the 4090s. I have 2 3090. 2X A100. Actually we see that 3 bit model can achieve like 70% quality of not quantized model. 48gb vram could afford q4 70b and mixtral indeed. On a 70B model, even at q8, I get 1t/s on a 4090+5900X (with 4 GB being taken by bad nvidia drivers getting fucked by my monitor config). 14. My expectation is that your best time is going to be on a Llama 3 70B quant 5-6 gigabytes smaller than your ram. Something that often gets overlooked with 4x 3090 / 4090 cost is the cost of the rest of the system. You need more CPU ram. 5 bits per weight) is 42. It works but it is crazy slow on multiple gpus. So far, from what I've found, for an almost-always-better than another choice for 20b exl2 model, this one really shines and I keep coming back to it often - it just comprehends the dialog and what is being asked the most consistently from what I've seen and used: Thanks! Its a 4060ti 16gb; llama said its a 43 layer 13b model (orca). I know the second card is just a vram container and it only uses one for the processing, but they're both identical cards so that shouldn't matter. Please use it with caution and with best intentions. But since the P40 is way slower than the 3090, the 3090 will be The RTX 4090 also has several other advantages over the RTX 3090, such as higher core count, higher memory bandwidth, higher NVLink bandwidth, and higher power limit. The title, pretty much. 4080 is not a good choice. A single 48GB card would be able to run Llama2 70B with 4-bit quantization. Keep eye on windows performance monitor and GPU vram and PC ram usage. 7900 XTX I am not sure, as that uses ROCM. Its a debian linux in a host center. Subreddit to discuss about Llama, the large language model created by Meta AI. Meanwhile, on the 3090 I get 1. 54 tokens/s, 1504 tokens, context 33, seed 1719700952)`. I like 4. Offloading 25-30 layers to GPU, I can't remember the generation speed but it was about 1/3 that of a 13b model. 1, mesa 23. cpp) allows you to offload layers to gpu, I don't gave answers to this question yet regarding how fast it would be. model --max_seq_len 512 --max_batch_size 4. While a 103B I can run at 3. I can get up to 1500 tokens returned before it OOMs on 2 x 4090. XWIN70B starts off at 11-13 t/s and then settles to around 6-7 t/s as the conversation get lengthy. New in 2. Besides being slower, the ROCm version also caused amdgpu exceptions that killed Wayland 2/3 times (I'm running Linux 6. It depends what other processes are allocating VRAM, of course, but at any rate the full 2048-token The speed will be 20+ t/s, which is faster than you read. Most people don't run local llms in half-precision, due to vram costs. It has significantly higher speeds than old a6000 AMP, but yes, it is 7k whereas amp is 4. As much as you can fit without triggering the backwards offloading by the driver. In particular I'm hoping for efficient parallelism: splitting a model up across cards and then using one card, swapping to the other, back and forth forever sounds less than ideal. cpp. 5k atm. I think since its 38gb it will run in 48gb split like you got. This model is trained on top of the amazing StellarBright base model. It's like a copy paste LLM command line that works in any app or editor. A 3090 gpu is a 3090 gpu, you can use it in a PC or a egpu case. If you care about OC, the Tuf, Strix are good choices. I can scale 70b up to 16k context with alpha 4 rope scaling, can't stand under 70b models anymore. 55bpw Would love a monthly subscription based service that let you pick your model from huggingface and run it on the clouds, really want to try dolphin 2. I got dual 3090s, cheaper than a 4090 too. For example, goliath120b is an incredibly good model and is a merge of Xwin 70B and Euryale 70B. With an infusion of curated Samantha and WizardLM DNA, Dolphin can now give you personal advice and will care about your feelings, and with extra training in long multi-turn conversation. The settings you use use for the sampler, the stuff under parameters>generation for oobabooga drastically effect the Edit: The numbers below are not up to date anymore. 1. To get a HF model to an FP16 gguf file, you use either the convert. May 27, 2024 · Check out our blog post to learn how to run the powerful Llama3 70B AI language model on your PC using picoLLMhttp://picovoice. It's a shame 32GB consumer GPUs aren't out there. 65 seconds (0. 2 is conversation and empathy. Then each card will be responsible for its own half of the work, and they'll work in turn. When a model exceeds 24GB, it should be split among the cards, and 3090s + NVLink exhibit a speed advantage in this aspect. 30B can run, and it's worth trying out just to see if you can tell the difference in practice (I can't, FWIW) but sequences longer than about 800 tokens will tend to OoM on you. q4_1. You are responsible for how you use Synthia. But if you can make it work, I think it's totally worth it. py files depending on the model. It seems like the cheapest 24GB 4090's go for around $1600. The larger models at lower quants appear to perform better, assuming the model merge doesn't break things. Similar on the 4090 vs A6000 Ada case. It follows few shot instructions better and is zippy enough for my taste. The model size you can fit in vram will also be 34b. I’ve found the following options available around the same price point: A Lenovo Legion 7i, with RTX 4090 (16GB VRAM), 32GB RAM. Now, RTX 4090 when doing inference, is 50-70% faster than the RTX 3090. I'm wondering whether a 70b model quantized to 4bit would perform better than a 7b/13b/34b model at…. Llama models were trained on float 16 so, you can use them as 16 bit w/o loss, but that will require 2x70GB. ggmlv3. SynthIA (Synthetic Intelligent Agent) is a LLama-2-70B model trained on Orca style datasets. The above are mostly hard limits (basically, single 4090 sucks if the model is over 24 gb). WizardLM-70B V1. Specifically, I ran an Alpaca-65B-4bit version, courtesy of TheBloke. 6 it/s on 704x704 sd which is good enough for chats. " The soldier, now a veteran, sits on a bench in Central Park. I have 72 total GB of VRAM, so I'm gonna quant at 4bpw and other sizes with EXL2 (exllamav2) and see how it goes. I have a 4090, it rips in inference, but is heavily limited by having only 24 GB of VRAM, you cant even run the 33B model at 16k context, let alone 70B. But the 4090 fe OC’s +1500 mem and +200 clock with temps at 65. PS:The problem revolves around the slower communication speed via PCIe compared to NVLink. Running the Llama 3 70B model with a 8,192 token context length, which requires 41. Just model parallelism is borked in most tools, either slow or it goes OOM. That fits entirely on the NVidia RTX 4090's 24GB VRAM, but is just a bit much for the 4080's 16GB VRAM. As long as you fully fit the whole model into VRAM you should be able to get around or a little more than 5 t/s. The biggest models you can fit fully on your RTX 4090 are 33B parameter models. py --auto-devices --loader exllamav2 --model turboderp_LLama2-70B-chat-2. 5 Flash outperforms Llama 3 70B and Claude 3 Haiku, 1. While having more memory can be beneficial, it's hard to predict what you might need in the future as newer models are released. A layer size depends on model size and quantization, so there is no fixed answer. . This is one chonky boi. If you do XOC, or don't care about money and just want to floss, Galax HoF. For tasks like inference, a greater number of GPU cores is also faster. A 70b model will natively require 4x70 GB VRAM (roughly). I have a 7800XT and 96GB of DDR5 ram. You can get chatgpt speeds locally on some size of model, the question is how small of a model it is. If you can get away with using a single GPU then do that. to offload whatever layers won't fit on the GPU to the CPU, how much will it impact response speed? Is loading the entire model into VRAM overkill on speed? For an average response of, say We would like to show you a description here but the site won’t allow us. 3-1. The only thing I can think of is that it just takes longer to generate responses from 70b models. 5 0514 models added to Chatbot Arena. A 70B_q4_k_m quant (70B parameters quantized to ~4. Still on Llama 1 33B, but I am curious on how that Exllama V2 news from the other day about fitting 70B Q2 onto a single 24GB GPU will pan out. P40 has more bandwidth than macs and it has cuda. Speed wise, I dont think either can get 40 t/s. Unfortunately you can't do 70B models on 24GB of VRAM unless you drop to 2bpw, which is too much quality loss to be practical. The 70b model is excellent and impressive. 65. But the jury is still out if there's some other difference going on that perplexity doesn't reflect. cpp on 24gb VRAM, but you only get 1-2 tokens/second. 1. Need to add a second card if you want to use 70B models in any Just noticed this old post. 8B! Including Base, Chat and Quantized versions! 🌟 Qwen-72B has been trained on high-quality data consisting of 3T tokens, boasting a larger parameter scale and more Maybe look into the Upstage 30b Llama model which ranks higher than Llama 2 70b on the leaderboard and you should be able to run it on one 3090, I can run it on my M1 Max 64GB very fast. 6 GB/s. 85bpw, which enables you to go to 8192 context length comfortably, and you can push 10-12k context with it. py file, and if it errors out then try convert-hf-to-gguf. The sun shines brightly above him, casting long shadows over the grassy lawn. Your hardware is better suited for the 30b and smaller models. And here are the detailed notes, the basis of my ranking, and also additional comments and observations: miqu-1-70b GGUF Q5_K_M, 32K context, Mistral format: Gave correct answers to only 4+4+4+5=17/18 multiple choice questions! Just the questions, no previous information, gave correct answers: 4+3+1+5=13/18. I have a i9-13900K. These factors make the RTX 4090 a superior GPU that can run the LLaMa v-2 70B model for inference using Exllama with more context length and faster speed than the RTX 3090. The other LLM I wanted to 2 bit quantize was an orca model. 8x the rate of token generation) or so. 58 votes, 45 comments. "He was a good man," says the soldier, looking down at his hands. 5 GB. py --ckpt_dir llama-2-70b-chat/ --tokenizer_path tokenizer. Members Online Gemini 1. You would need a 2nd 4090 (or 3090) to run quantized aka "compressed" models at around 4 bit for a 70b. However, I am not sure if this advantage extends to cover the speed advantage of the 4090s. So yeah, you can definitely run things locally. Having a 30 amp circuit would probably be a good idea for anything over 4 cards. 4, ROCm 5. After some tinkering, I finally got a version of LLaMA-65B-4bit working on two RTX 4090's with triton enabled. py . Now, I sadly do not know enough about the 7900 XTX to compare. ~63GB should be fine (to be seen) for 4bit. 9 it/s on a RTX 4090, at 768x768 and batch size 5. ggml (llama. Ending 1: The Soldier. People are creating various public local LLM's at different sizes but what about creating the highest quality LLM that uses every last bit of available VRam of We would like to show you a description here but the site won’t allow us. If you have enough money then get one 4090 to do stuff locally where you don't have to worry about cloud bills and then use cloud or HPC for anything you can't fit (A100 or TPUs or multiple smaller GPUs). What you can do is split the model into two parts. 8B model. At 8 bit you'd need 4x4090s and double that again for the raw 70b model unquantized. 13B 16k model uses 18 GB of VRAM, so the 4080 will have issues if you need the context. 8 bit quantized 15B models for general purpose tasks like WIZARDLM, or 5bit 34B models for coding. I think WizardLM-Uncensored-30B is really performant model so far. 4090 will do 4-bit 30B fast (with exllama, 40 tokens/sec) but can't hold any model larger than that. With a 4090, a significantly more powerful card that has 50% more VRAM and the ability to use CUDA Qwen-72B released. Offloading 38-40 layers to GPU, I get 4-5 tokens per second. cpp didn't support model parallelism, meaning llama 70b spanning across 2 cards' memory is run sequentially. MacBook Pro M1 at steep discount, with 64GB Unified memory. Any ideas on what could be causing this issue For GPU inference, using exllama 70B + 16K context fits comfortably in 48GB A6000 or 2x3090/4090. 05 s/it (not even it/s!). Edit- now you have me downloading this to try on the 4090 lol That's very interesting, this may be relevant for me because I had 2 of my 4 3090s running on a 1x crypto pcie adapter (which worked just fine with model parallel). Next version is in training and will be public together with our new paper soon. If you quantize to 8bit, you still need 70GB VRAM. 4. Myself included. For example, when training a SD LoRA, I get 1. GPU(s) holding the entire model in VRAM is how you get fast speeds. Doing SLI with two GPUs seems like a good strategy to increase VRAM but it does seem to have performance downsides as well as practical concerns like taking up space in a chassis and power/cooling. There's a 34b CodeLlama model based on l2 which is meant to be good, and several merges based on that. 0bpw into 48 GB of VRAM at 4096 context length. I load them entirely into my RTX 3090. This is the command I use. A 70B should be able to mimic your writing style quite well and have better reasoning capabilities than 13, 20, or 30B class models. So I have somehow talked my institution into a $40k check to buy/build an entry-level on-prem So a 70b model you'll still need to run quantized, just at 4. 5. When it comes to token generation speed of the same sized model that fully fits in VRAM, 4090 is +80% faster than 3090 (1. This seems like a solid deal, one of the best gaming laptops around for the price, if I'm going to go that route. If you can effectively make use of 2x3090 with NVlink, they will beat out the single 4090. I have 8Gb too, and I just found the optimum for "Wizard-Vicuna-30B-Uncensored. From experience, I recommend first trying the regular convert. This model is uncensored. This hypothesis should be easily verifiable with cloud hardware. 3 t/s inferencing a Q4_K_M. 4090 is quieter and easier to cool than 3090. Gets about 10 t/s on an old CPU. His voice is quiet but firm. fd rd gh so hr pg sr bd ma tp