Llama 65b rtx 4090 reddit. 30B models aren't too bad though.

Llama 65b rtx 4090 reddit 65b EXL2 with ExllamaV2, or, full size model with transformers, load in 4bit and double I built a small local llm server with 2 rtx 3060 12gb. With the RTX 4090 priced over **$2199 CAD**, my next best option for more than 20Gb of VRAM was to get two RTX 4060ti 16Gb (around $660 CAD each). a 7b better than llama 65b now??? Mistral Orca is OUT! If you're at inferencing/training, 48GB RTX A6000s (Ampere) are available new (from Amazon no less) for $4K - 2 of those are $8K and would easily fit the biggest quantizes and let you run fine-tunes and conversions effectively RTX 4090 Reply reply More replies More replies. Bang for buck 2x3090s is the best setup. Not sure if the results are any good, but I don't even wanna think about trying it with CPU. I still think 3090's are the sweet spot, though they are much wider cards than the RTX A6000's. Motherboard is Asus Pro Art AM5. Basically install the relevant driver with only that card plugged and his device manager showed how each card was using the right driver LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b Over 13b, obviously, yes. Seems like I should getting non OC RTX 4090 cards which are say capped at 450w power draw or so. Is this good idea? Please help me with the decision. The second best option is to setup a A6000 on cloud, setup a 65b 4bit gptq model and connect everyon to it via api. Some OC cards allowed to Subreddit to discuss about Llama, the large language model created by Meta AI. Developer-supported and community-run. An updated bitsandbytes with 4 bit training is about to be released to handle LLaMA 65B with 64 gigs of Get the Reddit app Scan this QR code to download the app now. "What happens if you abliterate positivity on LLaMa?" You get a Mopey Mule. I can run the 65b 4-bit quantized model of LLaMA right now but Loras / open chat models are limited. 4080 + CPU . RTX 4090 all VRAM vs. Internet Culture (Viral) Amazing; Animals & Pets; Cringe & Facepalm; for rtx 4090 gigabyte gaming oc owners Anyone run the LLaMA 65B model on two 4090s in 4-bit mode? RTX 4090 is using a rather significantly cut down AD102 chip, especially in the L2 cache department. I am able to run with llama. A6000 Ada has AD102 (even a better one that on the RTX 4090) so performance will be great. Have a Lenovo P920, which would easily support 3x, if not 4x, but wouldn’t at all support a 4090 easily, let alone two of them. Fully loaded up around 1. Rtx 4070 non ti! Hear me out: The Rtx 3090 is a great GPU with 24gb of vram. 4 bpw should be better then 30 16 bpw, at least when it comes to perplexity. Kind of like a lobotomized Chat GPT4 lol ----- Model: GPT4-X-Alpaca-30b-4bit Env: Intel 13900K, RTX 4090FE 24GB, DDR5 64GB 6000MTs Performance: 25 tokens/s Reason: Fits neatly in a 4090 as well, but I tend to use it more to write stories, something the previous one has a hard time with. Across eight simultaneous sessions this jumps to over 600 tokens/s, with each session getting roughly 75 tokens/s which is still absurdly fast, bordering on unnecessarily fast. Confirmed with Xwin-MLewd-13B-V0. 13B 16k model uses 18 GB of VRAM, so the 4080 will have issues if you need the context. I'll make sure to post learnings here Reply reply more replies More replies More replies. 4090 is gonna run the same stuff as a 3090, as they have the same VRAM. 2-2. Clearly llama 1 here started to think about the content instead of generating it. 144 votes, 48 comments. Valheim; Genshin Impact; Minecraft; Pokimane; Halo Infinite; Call of Duty: Warzone; in my market I found that the cheapest Rtx 4090 are these ones under the brand Gainward and Palit. 2-GGUF. Note: 44 votes, 23 comments. Internet Culture (Viral) Amazing beating llama-30b-supercot and llama-65b among others. 5 on mistral 7b q8 and 2. In this article we will describe how to run the larger LLaMa models variations up to the 65B model on multi-GPU hardware and show some differences in achievable text quality regarding the different model sizes. A potential full AD102 chip graphics card would have 33% more L2 cache (96MB L2 cache total) and 12. The Best GPUs for Deep Learning in 2023 — An In-depth Analysis View community ranking In the Top 5% of largest communities on Reddit. 58 TFLOPS FP32: A 65B model in 4bit will fit in a 48GB GPU. The a6000 is slower here because it's the previous generation comparable to the 3090. cpp. r/nvidia. It won't use both gpus and will be slow but you will be able try the model. And to upgrade all the way to 96GB, you might be better off getting a few 3090s on the cheap Reply reply I have what I consider a good laptop: Scar 18, i9 13980HX, RTX 4090 16GB, 64GB RAM. 25 bpw LLM360 has released K2 65b, a fully reproducible Yes, using exllama lately I can see my 2x4090 at 100% utilization on 65B, with 40 layers (of a total of 80) per GPU. Open comment sort options. GDDR6X is probably slightly more, but should still be Get the Reddit app Scan this QR code to download the app now. While training, it can be up to 2x times Get the Reddit app Scan this QR code to download the app now. Members Online • Xhehab_ 3x 4090's for inference on small models up to 13b? upvotes LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b upvotes It's actually a good value relative to what the current market offers. ADMIN MOD The best chat model for a RTX 4090 ? Question | Help Hello, i saw a lot of new LLM since a month, so i am a bit lost. It would be too slow to run model larger than 65B. It is not about money, but still I cannot afford a100 80GB for this hobby. No need to do more though unless you’re curious. With a 3090/4090 there is not much sense in fine-tuning 13B in 4-bit, though, if you can do it with full LoRA precision (which is using 8bit during fine-tuning, I believe). cpp repo, here are some tips: use --prompt-cache for summarization Also, similar to the Ada Lovelace based RTX 4090, the newer Ada Lovelace based RTX 6000 also dropped support for NVLink. Initially I was unsatisfied with the p40s performance. 8 t/s for a 65b 4bit via pipelining for inference. I assume more than 64gb ram will be needed. 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) Each node has a single RTX 4090. I love and have been using both benk04 Typhon Mixtral and NoromaidxOpenGPT but as all things go AI the LLM scene grows very quickly. I spent half a day conducting a benchmark test of the 65B model on some of the most powerful GPUs For training larger LLMs like LLaMA 65B or Bloom, a multi-GPU setup with each GPU having at least 40GB of VRAM is recommended, such as NVIDIA’s A100 or the new And also, do not repeat my mistake. I get about 5 tokens/sec on Llama 30B based models using llama. With exllamav2, 2x 4090 can run 70B q4 at 15T/s. You could run 65b using llama. 9-4. Particularly for NSFW. A Lenovo Legion 7i, with RTX 4090 (16GB VRAM), 32GB RAM. I found 4 models: Gainward RTX 4090 Phantom Gainward RTX My AMD 7950X3D ( 16 core 32 threads), 64GB DDR5, Single RTX 4090 on 13B Xwin GGUF q8 can run at 45T/S. Get the Reddit app Scan this QR code to download the app now. 4T tokens) is competitive with Chinchilla and Palm-540B. After some tinkering, I finally got a version of LLaMA-65B-4bit working I can run the 30B on a 4090 in 4-bit mode, and it works well. multiple GPUs. 4b-2. /r/StableDiffusion is back open after the protest of Reddit killing open API These are the speeds I get with different LLMs on my 4090 card at half precision. I know the Llama 1 ship has sailed, but I wonder if applying Exllama V2 quantization to a 65B model but just a little higher than 2. cpp the alpaca-lora-65B. Will this run on a 128GB Ram system (ir-13900k) with a RTX 4090? I have 64GB ram total, but will add more RAM if that's needed. ADMIN MOD 7B models cannot fit in RTX 4090 VRAM (24GB) Question | Help that is unless I quantize as follows. 4090 RTX In my short testings the quality and speed is impressive! LLaMA. So do not buy third card, before you will be sure that you have enough PCI-E lanes. 92 4080's , laptops with 4090 and 4080's etc all for sale, no restriction at all. After the initial Subreddit to discuss about Llama, the large language model created by Meta AI. First couple of tests I prompted it with "Hello! Are you working correctly?", and later changed to --mtest to get a benchmark with less room for variance - I hope. 1 4bit) and on the second 3060 12gb I'm running Stable Diffusion. Just fyi there is a Reddit post that describes a solution. That or Llama 3 instruct needs no structure to act like it’s in a chat. 5-4. That is changing fast. I've only assumed 32k is viable because llama-2 has double the context of llama-1 Tips: If your new to the llama. My Question is, however, how good are these models running with the recommended hardware requirements? What configuration would I need to properly run a 13B / 30B or 65B model FAST? Would an RTX 4090 be sufficient for a 13B and Subreddit to discuss about Llama, the large language model created by Meta AI. I'd like to know what I can and can't do well (with respect to all things generative AI, in image generation (training, meaningfully faster generation etc) and text generation (usage of large LLaMA, fine-tuning etc), and 3D rendering (like Vue xStream - faster renders, more objects loaded) so I can decide I’ve added another p40 and two p4s for a total of 64gb vram. 1-GGUF Q8_0 ( Subreddit to discuss about Llama, the large language model created by Meta AI. I have a similar setup, RTX 3060 and RTX 4070, both 12GB. NVIDIA GeForce RTX 4090 Mem: 24GB Mem Bandwidth: 1,018 GB/s CUDA Cores: 16384 Tensor Cores: 512 FP16: 82. We ask that you please take a minute to read through the rules and check out the resources I have a 7950X3D and here are my results for llama. 20 tokens/s, 512 Considering that an high-end desktop with dual-channel DDR5-6400 only does 100 GB/s, and a RTX 4090 has about 1000 GB/s bandwidth but only 24 GB memory, Apple is really well positioned to run local generative AI. the flagship RTX 4090, also based on the ADA architecture, is priced at £1,763, with 24GB of vRAM and 16384 CUDA cores. The market has changed. 65bpw quant of 70B models only to be disappointed by how unstable they tend to be due to their high perplexity. ( a few weeks later Get the Reddit app Scan this QR code to download the app now. Research I'm a hobbyist (albeit with an EE degree, and decades of programming experience), so I really enjoy tinkering oobabooga's textgen codebase. Models Basically the title. I think people have more or less settled on 2x 3090 as being the best current bang for buck. But you probably won't use them as much as you think. cpp option was slow, achieving around 0. Max 60C in gaming load. All of the multi-node multi-gpu tutorials seem to concentrate on training. 7 tok/s on q4_0 65b guanaco, and on the 4090+i9-13900K I was getting around 3. View community ranking In the Top 5% of largest communities on Reddit. 8 t/s on 65B_4bit with 2month old Not seeing 4090 for $1250 in my neck of the woods, even used. Reason: Fits neatly in a 4090 and is great to chat with. Is this the expected behavior? Ask the community and try to help others with their problems as well. Or check it out in the app stores &nbsp; &nbsp; TOPICS. Hi everyone, RTX 4090 RTX 3090 (because it supports NVLINK) If I buy an RTX 4090 or RTX 3090, A6000 I can buy multiple GPUs to fit my budget. Reddit's home for anything and everything related to the NBA 2K series. Members Online. My 4090 gets 50, a 4090 is 60% bigger than a 4080. Your best option for even bigger models is probably offloading with llama. Valheim; Genshin Impact; We tested an RTX 4090 on a Core i9-9900K and the 12900K, for example, and the latter was almost twice as fast. I'm able to get about 1. 65b exl2 Output generated in 5. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will The llama. 3b Polish LLM pretrained on single RTX 4090 for ~3 months on Polish only content Get the Reddit app Scan this QR code to download the app now. LLM360 has released K2 65b, a fully reproducible Get the Reddit app Scan this QR code to download the app now. to best take advantage of my 32 GB RAM, AMD 5600X3D, RTX 4090 system? Thank you. What GPU split should I do for RTX 4090 24GB GPU 0 and RTX A6000 48GB GPU 1 and how much context would I be able to get with Llama-2-70B-GPTQ-4bit-32g-actorder_True? /r/StableDiffusion is back open after the protest of Reddit killing open API My RTX 3060: LLaMA 13b 4bit: 18 tokens per second So far with the 3060's 12GB I can train a LoRA for the 7b 4-bit only. Internet Culture (Viral) Amazing I'm too satisfied with the speed of 65B 4-bit llama on avx-512 cpu to consider a GPU upgrade. I tried a llama 65b ggml model on a ryzen 7950x3d with ddr5 4xxx I get 16-20t/s on 65b split across 4090 + A6000 (ampere) which is actually faster than just running the entire model on the A6000 (13 t/s) In the GitHub I have seen people posting speeds with 2x 4090s around the 20t/s mark, the creator is testing with 4090 + 3090ti and I have seen some people pop in with 2x 3090 but I don’t remember their speeds off the top of my head. He is about to release some fine-tuned models as well, but the key feature is apparently this new approach to fine-tune large models at high performance on consumer-available Nvidia cards like RTX3090 and Subreddit to discuss about Llama, the large language model created by Meta AI. 7900 XTX I am not sure, as that uses ROCM. I use 4x45GB A40s I load the model with model = LlamaForCausalLM. I saw that Lambda labs does offer a machine with 2 4090 cards, but they are about Get the Reddit app Scan this QR code to download the app now. While the 3060 may be more budget-friendly, the 4090's increased CUDA cores, tensor cores, and memory bandwidth give it a significant edge in AI performance. However, those toys are nowhere near chatGPT or new Bing. a 7b better than llama 65b Does that mean if let say, i load llama-3 70b on 4090+3090 vs 4090+4090, I will see bigger speed difference with the 4090+4090 setup? Reply reply Reddit's Official home for Microsoft Flight Simulator. However, at this price point and level of performance, that 12gb of vram WILL be an issue soon if not already in some cases. We are speaking about 5 t/s on Apple vs 15 t/s on Nvidia for 65b llama at the current point in time. Will my SSD be suitable for a rtx 4090 Get the Reddit app Scan this QR code to download the app now. LLama 13B with 16k context, 34B in full GPU mode with 4k context, and 70B still needs to be offset to CPU. You can use llama. Post not showing Get the Reddit app Scan this QR code to download the app now. Members Online • jd_3d. MacBook Pro M1 at steep discount, with 64GB Unified memory. on larger models (that a 4090 can’t load on its own), with the M2 Max I was getting around 4. cpp is adding GPU support. Best. I really like the answers it gives, it's slow Subreddit to discuss about Llama, the large language model created by Meta AI. 5 tokens per second. n_ff = 22016 llama_model_load_internal: n_parts = 1 llama_model_load_internal: model size = 65B llama_model_load_internal: ggml ctx size = 0,18 MB llama_model_load_internal: using CUDA for GPU acceleration The larger the amount of VRAM, the larger the model size (# of parameters) you can work with. The "extra" $500 for an RTX 4090 disappears after a few hours of messing with ROCm - and that's a very, very, very conservative estimate on what it takes to get ROCm to do anything equivalent. Then, in the event you can jump through these hoops, something like a used RTX 3090 at the same cost will stomp all over AMD in performance, even with their latest gen Most people here don't need RTX 4090s. 65B version (trained on 1. In terms of memory bandwidth 1 P40 is I think 66% of an RTX 3090. from_pretrained(model_id, load_in_8bit=True, device_map="auto") I infer with model. It mostly depends on your ram bandwith, with dual channel ddr4 you should have around 3. bin llama_model_load_internal: format = ggjt v3 (latest) Subreddit to discuss about Llama, the large language model created by Meta AI. I don't know what I'm doing. The dataset it was trained on is censored. It's Right now for about $2600 dollars I could a RTX 4090 and I5-13600k. Valheim; Genshin Impact; Minecraft; Pokimane; Halo Infinite; Call of Duty: Warzone; Path of Exile; Hollow Knight: Silksong; I've been running an RTX 4090 with a Ryzen 7700X with 32GB 6000mhz CL30 ram for a couple of months now it works pretty Realistically, I would recommend looking into smaller models, llama 1 had a 65B variant but the speedup would not be worth the performance loss. In your case I would run int8 for the 13b. Multimodal dataset with 1400h of video, multiple perspectives, 7ch audio, annotated by domain experts. 8 on llama 2 13b q8. vLLM is another comparable option. cpp with ggml quantization to share the model between a gpu and cpu. BTW: Don't buy Alienware. Personally, I am Getting around 0. 66 PFLOPS of compute for Get the Reddit app Scan this QR code to download the app now. Now, I sadly do not know enough about the 7900 XTX to compare. I would try exllama first, it can run 65B parameter model in 40 to 45 gigabyte of vram on two GPUs. Internet Culture (Viral) Amazing That's a bit too much for the popular dual rtx 3090 or rtx 4090 configurations that I've often seen mentioned. Now, RTX 4090 when doing inference, is 50-70% faster than the RTX 3090. In addition to training 30B/65B models on single GPUs it seems like this is something that would also make finetuning much large models practical. 2 tokens/s 22+ tokens/s Basically I couldn't believe it when I saw it. I used TheBloke's LLama2-7B quants for benchmarking (Q4_0 GGUF, GS128 No Act Order GPTQ with both llama. I have an rtx 4090 so wanted to use that to get the best local model set up I could. 04 GPU: RTX 4090 CPU: Ryzen 7950X (power usage throttled to 65W in BIOS) RAM: 64GB DDR5 @ 5600 (couldn't get 6000 to be stable yet) Results: model size params backend ngl test t/s llama 8x22B IQ4_XS - 4. GGML. If your question is what model is best for running ON a RTX 4090 and getting its full benefits then nothing is better than Llama 8B Instruct right now. Over 10it/s is overkill, but after role playing with int4 13Bs, I'm starting to get fed up with how dumb they are in comparison. llama_model_load_internal: model size = 65B llama_model_load_internal: ggml ctx size = 0. If you can afford two RTX A6000's, you're in a good place. So just for reference, anyone else considering between the newer RTX 6000 vs RTX A6000, the same consideration regarding NVLink would apply, the same as when considering between the newer RTX 4090 vs RTX 3090. While these models are massive, 65B parameters in some cases, quantization converts the parameters (the connections between neurons) from FP16/32 to 8/4-bit integers. 9 llama. 3deal. ggmlv3. Thanks for the detailed post! trying to run Llama 13B locally on my 4090 and this helped at on. 30B models aren't too bad though. I realize the VRAM reqs for larger models is pretty BEEFY, but Llama 3 3_K_S claims, via LM Studio, that a partial GPU offload is possible. 00 MB per state) llama_model_load_internal: allocating batch_size x 1 MB = 512 MB VRAM for the scratch buffer Get the Reddit app Scan this QR code to download the app now. Seems like a really solid combo for my 42inch LG C2. He is apparently about to unleash a way to fine tune 33B Llama on a RTX4090 (using an enhanced approach to 4 bit parameters), or 65B Llama on two RTX4090's. 5-2 t/s with 6700xt (12 GB) running WizardLM Uncensored 30B. ) I was wondering if adding a used tesla p40 and splitting the model across the vram using ooba booga would be faster than using ggml cpu plus gpu offloading. This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more This may be at an impossible state rn with bad output quality. Exllama by itself is very fast when model fits in VRAM completely. It will cost 0. This Subreddit is community run and does not represent NVIDIA in any capacity unless specified. Internet Culture (Viral) Aeala_VicUnlocked-alpaca-65b-4bit_128g GPTQ-for-LLaMa EXLlama (2X) RTX 4090 HAGPU Disabled 1-1. r/LocalLLaMA We are Reddit's primary hub for all things modding, from troubleshooting for beginners to creation of mods by experts. Now, the Rtx 4070ti does slightly beat out the Rtx 3090 in games. Reply reply RTX 4090 slower in YOLOv6 Subreddit to discuss about Llama, the large language model created by Meta AI. and now with FP8 tensor cores you get 0. cpp with GPU offload (3 t/s). Characters also seem to be more self-aware in 65B. Used RTX 30 series is the best price to performance, and I'd recommend the 3060 12GB (~$300), RTX A4000 16GB (~$500), RTX 3090 24GB (~$700-800). Reply reply Today I actually bought 2 RTX 3060 with 12GB each to start experimenting with this. Single RTX 4090 FE at 40 tokens/s but with penalty if running 2 get only 10 tokens/s. The EXLlama option was significantly faster at around 2. 4090 32GB DDR5 6000 CL30 7800X3D Share Add a Comment. It basically splits the workload between CPU + ram and GPU + vram, the Currently 1x rtx 3090, old i5 CPU, 32GB RAM. ai, and I'm already blowing way too much money (because I don't have much to spare, but it's still significant) doing that. I'm wondering if a setup of 2x3090 can use Will this run on a 128GB Ram system (ir-13900k) with a RTX 4090? We’re on a journey to advance and democratize artificial intelligence through open source and open science. Its most popular types of products Hi, I love the idea of open source. Valheim; Genshin Impact; Minecraft; Pokimane; Subreddit to discuss about Llama, the large language model created by Meta AI. It's not the fastest and the RAM is definitely loaded up to 60-62 GB in total (having some background apps also), but it gets the job done for me, ymmv. This post also conveniently leaves out the fact that CPU and hybrid CPU/GPU inference exists, which can run Llama-2-70B much cheaper then even the affordable 2x TESLA P40 option above. language model created by Meta AI. Some graphs comparing the RTX 4060 ti 16GB and the AMD 6900 XT, RTX 2060 12GB, 3060 12GB, 3080, A2000 12 GB LLaMA 33B / Llama 2 34B ~20GB RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100 ~32 GB LLaMA 65B / Llama 2 70B ~40GB A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000 I'm running a RTX 3090 on Windows 10 with 24 gigs of VRAM. Here is a sample of I keep hearing great things from reputable Discord users about WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GPTQ (these model names keep getting bigger and bigger, lol). I have friends who spend significantly more on other hobbies. Not that you need a 65B model to get good answers. Released Llama-3-8B-Instruct model with a melancholic attitude about everything. Members Online • The-Bloke That is with a 4090, 13900k, and 64GB DDR5 @ 6000 MT/s. 40 per hour for a RTX A6000 or a RTX 4090 and less than $0. cpp, but I still have some tweaking to do and can probably raise the tokens/sec. The activity bounces between GPUs but the load on the P40 is higher. [R] Meta AI open sources new SOTA LLM called LLaMA. I am thinking about buying two more rtx 3090 when I am see how fast community is making progress. Or check it out in the app stores Home; Popular; TOPICS. 1 tok/s (sub 300 context). I tried that with 65B on single 4090 and exllama is much slower (0. It's "only" got 72MB L2 cache. A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more. Internet Culture (Viral) Amazing What token/s would I be looking at with a RTX 4090 and 64GB of RAM? Single 3090 = 4_K_M GGUF with llama. But now, with the right compile flags/settings in llama. So it shouldn't be that bad? Roughly 15 t/s for dual 4090. I've got a choice of buying either the NVidia RTX A6000 or the NVidia RTX 4090. cpp and offloading to gpu. Valheim; Genshin Impact; Minecraft; Subreddit to discuss about Llama, the large language model created by Meta AI. Also, the A6000 can be slower than two 4090, for example for the 65b llama model and its derivates in case of inference. Multi-GPU support would benefit a lot of people, from those who would be able to buy a dirt cheap Tesla K80 to have 24GB VRAM (K80 is actually 2x 12GB GPUs) to those who want to make a workstation with a bunch of RTX 3090s/RTX 4090s. My RTX 4070 also runs my Linux desktop, so I'm effectively limited to 23GB vram. Thanks to patch provided by emvw7yf below, the model now runs at almost 10 tokens per second for 1500 context length. Good news: Turbo, the author of ExLlamaV2, has made a new quant method that decreases the perplexity of low bpw quants, improving performance and making them much more stable. 64G @ 3200 + 16 core ryzen7 gets me ~0. I don’t feel like the cost is completely crazy for a new PC. 6-4. I tried to load 3. you can run large models at decent speed that you cannot even dream of The RTX 4090 is definitely better than the 3060 for AI workloads. Top 1% Rank by size . Even 65B is not ideal but it's much more consistent in more complicated cases. Open With lmdeploy, AWQ, and KV cache quantization on llama 2 13b I’m able to get 115 tokens/s with a single session on an RTX 4090. Internet Culture (Viral) Amazing 20 tokens/s for Llama-2-70b-chat on a RTX 3090 Mod Post Share Add a Comment. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. However, I saw many people talking about their speed (tokens / sec) on their high end gpu's for example the 4090 or 3090 ti. EDIT: I already have the 4090 and 128 GB RAM as my 3D rendering rig, Im not looking to upgrade. I have the opportunity to purchase a new desktop, and one driving factor is a desire to run a LLM locally for roleplay purposes through SillyTavern. It's much better in understanding character's hidden agenda and inner thoughts. I've been stuck pouring over whatever information I can on choosing a graphics card, though. A dual RTX 4090 setup can achieve speeds of around 20 tokens per second with a 65B model, while two RTX 3090s manage about 15 tokens per second. Reply reply 33B models will run at ~same speed on single 4090 and dual 4090. Or check it out in the app stores seed = 1689647281 ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 4090, compute capability 8. I just have a hard time pulling the trigger on a $1600 dollar GPU. If you're ok with 17" and a external water cooling attachment for quieter fans, XMG Neo 17/Eluktronics Mech GP 17 RTX 4090 have great thermals, good build and the water cooler will help keep the fans under load quieter than the very good laptops cooling system. On the first 3060 12gb I'm running a 7b 4bit model (TheBloke's Vicuna 1. There isn't any other consumer hardware that has this amount of memory at this bandwidth, especially in the Max and Ultra tiers. if I want to run the 65b model in 4bit without offloading to CPU I will need to scale a bit further to two 4090s 🥵) Then buy a bigger GPU like RTX 3090 or 4090 for Get the Reddit app Scan this QR code to download the app now. Top. 5 ~56 WizardCoder-3B-V1. 8 tokens/sec with something like Llama-65B and a little faster with the quantized version. So if you need something NOW, just rent a bigger rig. Aside: Reddit fucking up the ability to edit on mobile is pissing me off RTX 4080 or 4090? comments. I am building a PC for deep learning. I am running Llama-65b-4bit locally on Threadripper 3970x, Aorus TRX40 Extreme, 256gb DDR4, 2x Asus 3090 in O11D XL, 4x nvme SSD in Raid0, 1600w Corsair AXi psu. 2. Build a platform around the GPU(s) By platform I mean motherboard+CPU+RAM as these are pretty tightly 12 votes, 20 comments. 8$ per hour, so 8$ per working day. Controversial. It requires ROCM to emulate CUDA, tought I think ooba and llama. Internet Culture (Viral) Amazing; Animals & Pets Best Current Model for RTX 4090 . 2 and 2-2. I haven't run 65b enough to compare it with 30b, as I run these models with services like runpod and vast. This seems like a solid deal, one of the best gaming laptops around for the price, if I'm going to go that route. 78 seconds (19. Not to mention with cloud, it actually scales. In comparison, even the most powerful Apple Silicon chips struggle to Subreddit to discuss about Llama, the large language model created by Meta AI. If gpt4 can be trimmed down somehow just a little, I think that would be the current best under 65B. RTX 4090 + 5800X3D performance way lower than expected on Flight Simulator 2020 65b is technically possible on a 4090 24gb with 64gb of system RAM using GGML, but it's like 50 seconds per reply. There is a reason llama. Isn't that almost a five-fold advantage in favour of 4090, at the 4 or 8 bit precisions typical with local LLMs? Where does one A6000 cost the same as two 4090? Here the A6000 is 50% more expensive. The 6000 Ada is comparable to the 4090 and has more VRAM but is incredibly expensive. Is it even worth running a home LLM for code completion? LLM360 has released K2 65b, a fully reproducible open source LLM matching If you're willing to take a chance with QC and/or coil whine, the Strix Scar 17/18 could be a option. Or check it out in the app stores exaflop beast looks really promising and for open source startups it may be the best chance to get a true open source LLaMA alternative at the 30-65B+ size (hopefully with longer context and more training tokens). For training I would probably prefer the A6000, though (according to current knowledge). Did try some OC, ended up with 3015mhz on core 11500mhz on memory. . I saw a tweet by Nat Friedman mentioning 5 tokens/sec with a Apple M2 max with llama 65B, which required 44GB A RTX 3090 GPU has ~930 GB/s VRAM bandwidth, for comparison. I can even get the 65B model to run, but it eats up a good chunk of my 128gb of cpu ram and will eventually give me out of memory Subreddit to discuss about Llama, the large language model created by Meta AI. I will have to load one and check. 33 MB (+ 5120. People seem to consider them both as about equal for the price / performance. I have read the recommendations regarding the hardware in the Wiki of this Reddit. 55 seconds (18. 2GB Guanaco 65b Lora with q4_0 llama 65B and it didn't feel as censored as I would expect, but it also wasn't totally unrestricted like manticore or some alpacas. RTX 4090 24GB Owner: Stupid, you don't need that much VRAM. cpp and ExLlamaV2: I'm having some trouble running inference on Llama-65B for moderate contexts (~1000 tokens). We ask that you please take a minute to read through the rules and check out the resources provided before creating a post, Subreddit to discuss about Llama, the large language model created by Meta AI. I know 4090 doesn't have any more vram over 3090, but in terms of tensor compute according to the specs 3090 has 142 tflops at fp16 while 4090 has 660 tflops at fp8. 13B version outperforms OPT and GPT-3 175B on most benchmarks. Phi-1. I was able to load 70B GGML model offloading 42 layers onto the GPU using oobabooga. View community ranking In the Top 10% of largest communities on Reddit. GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. q4_3. My preference would be a founders edition card there, and not a gamer light show card - which seem to be closer to $1700. Do you really consider less than $0. 0 ~7. So far I only did SD and splitting 70b+ Here is nous-capybara up to 8k context @4. Ego-Exo4D (Meta FAIR) released. I have an Alienware R15 32G DDR5, i9, RTX4090. So people usually say that unless you forecast your project to go beyond a year, cloud is the winner. Old. Crypto I didn't do a ton of work with the llama-1 65b long context models, but what i did do, i wasn't very impressed with. 5 WizardCoder-Python-7B-V1. Model Revision Average ARC (25-shot) HellaSwag (10-shot) MMLU (5-shot) TruthfulQA (0-shot) I have the same questions but for an RTX 3060 I compared the 7900 XT and 7900 XTX inferencing performance vs my RTX 3090 and RTX 4090. Havent tried undervolting yet. Members Online • b3nsn0w LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b upvotes i9 13900k + RTX 4090 in a Meshroom S? upvotes Get the Reddit app Scan this QR code to download the app now. At the beginning I wanted to go for a dual RTX 4090 build but I discovered NVlink is not supported in this generation and it seems PyTorch only recognizes one of 4090 GPUs in a dual 4090 setup and they can not work together in PyTorch for training You can go to China's website and buy Nvidia cards now off amazon China, like this Asus 4090: ASUS 华硕 ROG Strix GeForce RTX™ 4090 白色 OC 版游戏显卡 (PCIe 4. By getting an upgrade now I would mean getting a 24GB VRAM gpu that would allow me only to run smaller 33B models anyway, but allows you to finetune 30B/65B LLaMA models on a single 24/48 GB GPU (no degradation vs full fine-tuning in 16-bit) That's amazing if true. Similar on the 4090 vs A6000 Ada case. Members Online • DrJokeTech. cpp have it as plug and play. Unlike the RTX solution where you basically cap out at 2x 4090 or 3x 3090 due to thermal and power constraints. ggml. Gaming. cpp, continual improvements and feature expansion in llama. And it's much better in keeping them separated when you do a group chat with multiple characters with different personalities. No traditional fine-tuning, pure steering; source code/walkthrough guide included If you run offloaded partially to the CPU your performance is essentially the same whether you run a Tesla P40 or a RTX 4090 since you will be bottlenecked by your CPU memory speed. According to Reddit, PNY is considered a reputable brand. System: OS: Ubuntu 22. My GPU is often executing 3ds max rendering and SD imagery, so I can't have the Llama chached in the VRAM. To get 100t/s on q8 you would need to have 1. 5% more CUDA cores. generate(input_ids) It's able to do the forward pass for small context sizes (<500 tokens). I used alpaca-lora-65B. More posts you may like r/LocalLLaMA. 12 tokens/s, 512 tokens, context 19, seed 1778944186) Output generated in 36. CPU: i9-9900k GPU: RTX 3090 RAM: 64GB DDR4 Model: Mixtral-8x7B-v0. I didn't want to say it because I only barely remember the performance data for llama 2. 1 t/s) than llama. Reply reply Top 1% Rank by size . 1a, DisplayPort 1. The gpt4-x-alpaca 30B 4 bit is just a little too large at 24. Currently I got 2x rtx 3090 and I amble to run int4 65B llama model. But in SD, 4090 is 70% better though. Check out our 2K24 Wiki for FAQs, Locker Codes & more. We are Reddit's primary hub for all things modding, from troubleshooting for beginners to creation of mods by experts. Not happy with the speed, thinking of trying 4x 4090 AIO with 240mm radiator - should fit in some bigger tower cases like Corsair 1000d. Can someone tell me what is the best local LLM usable in oobabooga yet ? Thanks. Reply reply This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API changes, which break This ruled out the RTX 3090. Currently a likely bottleneck are the remaining CPU only tensors though. I would like to have something similar to ChatGPT running locally. Use case for 4090 + 3080? I just bought a new RTX 4090, should I sell my old 3080 or is there a use case for using both cards at the same time? I have a 850w PSU, will that be enough for both cards? comment a 7b better than llama 65b now??? Mistral Orca is OUT! Subreddit to discuss about Llama, the large language model created by Meta AI. got LLaMa 65b base model converted to int4 working with llama. 4090 has no SLI/NVLink. cpp: loading model from models/Wizard-Vicuna-30B-Uncensored. 18 MB llama_model_load_internal: using CUDA for GPU acceleration llama_model_load_internal: mem required = 20369. The 30b is much smarter, even at in4. 0 ~2 Got the ZOTAC RTX 4090 AMP Extreme AIRO (only one in stock) /Thermals are really good. I've decided to go with an RTX 4090 and a used RTX 3090 for 48GB VRAM for loading larger models as well as a decent enough speed. They suggested looking for bitcoin mining equipment. 65B models technically run at ~same speed on single 4090 and on dual 4090 up until first token reaches ~24GB of model from my experience. I actually got 3 rtx 3090, but one is not working because of PCI-E bandwidth limitations on my AM4 motherboard. A common system config that rocks pretty hard is 2x3090 = 48GB for about $1600 vs 3000-5000$ for the equivilent VRAM in an RTX A6000. This lets you run the models on much smaller harder than you’d have to use for the unquantized models. Maybe I can try something bigger than 65b there. I would like to train/fine-tune ASR, LLM, TTS, stable diffusion, etc deep learning models. In Local LLama, I think you can run similar speed with RTX 3090s. Maybe review a PDF. Q&A. Really though, running gpt4-x 30B on CPU wasn't that bad for me with llama. 4a)3. 72 tokens/s, 104 tokens, context 19, seed 910757120) Output generated in 26. 0 颗星，最多 5 颗星 1¥22,699. A 30B model, which can run in a consumer 24GB card like a 3090 or 4090, can give very good responses. Obviously I'm only able to run 65b models on the cpu/ram (I can't compile the latest llama. It gets only 10 tokens/s. Recommendations for GPU with $25-30k budget . cpp shows most can’t even run 65B at reasonable speeds. Would make it just about 1/4 of the price of the rtx 4090 – a even better deal, if splitting the model actually works well Subreddit to discuss about Llama, the large language model created by Meta AI. 4080 vs 4090 Business, Economics, and Finance. Sort by: Best. However, it draws 350w+ and it doesn't quite compete with the Rtx 4070ti. q5_1. Wonder how the llama 1 models designed for writing would compare. Internet Culture (Viral) Amazing Subreddit to discuss about Llama, the large language model created by Meta AI. ADMIN MOD If an RTX 4090/5090 with 48GB of VRAM were introduced how much extra (over a standard 24GB version) would you pay for it? Discussion As of last year GDDR6 spot price was about $81 for 24GB of VRAM. New. You may be better off spending the money on a used 3090 or saving up for a 4090, both of which have 24GBs of VRAM if you don't care much about running 65B or greater models. It's easily worth the $400 premium over the rtx 4080, which is itself worth the premium over the 4070. 05 seconds (14. 23 per hour for a RTX A5000 to be Subreddit to discuss about Llama, the large language model created by Meta AI. I was disappointed to learn despite having Storytelling in its name, it's still only 2048 context, but oh well. RTX 3090 is a little (1-3%) faster than the RTX A6000, assuming what you're doing fits on 24GB VRAM. Been busy with a PC upgrade, but I'll try it tomorrow. More posts you may like /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will At the moment, m2 ultras run 65b at 5 t/s but a dual 4090 set up runs it at 1-2 t/s, which makes the m2 ultra a significant leader over the dual 4090s! edit: as other commenters have mentioned, i was misinformed and turns out the m2 ultra is worse at inference than dual 3090s (and therefore single/ dual 4090s) because it is largely doing cpu inference Now, about RTX 3090 vs RTX 4090 vs RTX A6000 vs RTX A6000 Ada, since I tested most of them. The exact number cannot be determined at the moment, but the basic direction is: The performance of current graphics cards will be far surpassed. 4GB so the next best would be vicuna 13B. The 4090 isn't just some top bin chip. However, if these benchmarks are confirmed, the GeForce RTX 4090 can be expected to perform slightly less than twice as well as the GeForce RTX 3090. Open comment sort options Was able to load the above model on my RTX-3090 and it works, but I'm not seeing anywhere near Get the Reddit app Scan this QR code to download the app now. That is pretty new though, with GTPQ for llama I get ~50% usage per card on 65B. Internet Culture (Viral) Amazing I get about 700 ms/T with 65b on 16gb vram and an i9 Reply reply I have a single 4090 and want to use a smaller llama version, bun no Idea how to do it (Im a programmer, so non computer illiterate, just new to this) That's a bit disappointing but not entirely unexpected. With streaming it's ok and much much better now than any other way I tried to run the 65b. Interestingly, the RTX 4090 I did few tests using your code on 4090, V100 (SXM2), A100 (SXM4) and H100 (PCIe) with WizardLM-30B-Uncensored-GPTQ Here are my results (avg on 10 runs) with 14 tokens prompt, 110 tokens generated on average and 2048 max Get the Reddit app Scan this QR code to download the app now. As title, I run single RTX 4090 FE at 40 tokens/s but with penalty if running dual 4090s. 0, 24GB GDDR6X, HDMI 2. SillyLilBear Going by perplexity 65B 2. I setup WSL and text-webui, was able to get base llama models working and thought I was already up against the limit for my VRAM as 30b would go out of memory before fully loading to my 4090. Exllama does fine with multi-GPU inferencing (llama-65b at 18t/s on a 4090+3090Ti from the README) so for someone looking just for fast inferencing, 2 x 3090s can be had for <$1500 used now, so the cheapest high performance option for someone looking to run a 40b/65b. Subreddit to discuss about Llama, the large language model created by Meta AI. Internet Culture (Viral) Amazing; Animals & Pets and if it is possible to run llama 70b on rtx 4090, what is the predicted speed of text generation? Thanks in advance LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b I want to buy a computer to run local LLaMa models. Various vendors told me that only 1 RTX 4090 can fit in their desktops simply because it's so physically big that it blocks the other PCIe slot on the motherboard. I had 2 rtx 3090 and bought third one, but I cannot use it properly because of PCI-e bandwidth limit on my motherboard, please take it into account. I have an opportunity to acquire two used RTX A4000 for roughly the same price as a used 3090 ($700USD). Its mostly used to improve emails and social media posts. Internet Culture (Viral) Amazing (I also have a 4090 and use GGMLs for 65b and 70b models, sometimes even the 33b ones too), then having stronger single-threaded performance is a boost. What motherboard, PSU and Cabinet should I choose? Ideally I'd want to run both cards at least with x8 PCIe slots and will also add 128GB DDR5 RAM in this build. bin model, for example, but it's on the CPU. Internet Culture (Viral) a 30b or 65b llama that only has to attend to 800 or so tokens seems to produce more coherent, interesting results (again, in a chat/companion kind of conversation) I am planning on getting myself a RTX 4090. Or 2 x 24GB GPUs, which some people do have at home. Finetuning could be done with Lora. If you have a single 3090 or 4090, chances are you have tried to run a 2. cpp Dual 3090 = 4. cpp and the advent of large-but-fast Mixtral-8x7b type models, I find that this box does the job very well. 55 bpw would give more acceptable performance due to slight size difference between 65B and 70B. Multi GPU usage isn't solid like single. The next lowest size is 34B, which is capable for the speed with the newest fine tunes but may lack the long range in depth insights the larger models can provide. I think there's a 65b 4-bit gptq available; try it and see for yourself. ccp to enable gpu offloading for ggml due to a weird but but that's unrelated to this post. Share Add a Comment. LLaMA-30B on RTX 3090 is really amazing, and I already orderd one RTX A6000 to access LLaMA-65B. 2 tokens/s 13 tokens/s (2X) RTX 4090 HAGPU Enabled 2-2. zpmf lmg mtit ssnid jndbx diyywm yxw kxu avf trgqjh