Llama 65b rtx 4090 reddit. It's "only" got 72MB L2 cache.

Llama 65b rtx 4090 reddit I have read the recommendations regarding the hardware in the Wiki of this Reddit. A 65B model in 4bit will fit in a 48GB GPU. Hope this is helpful! My RTX 3060: LLaMA 13b 4bit: 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 inference. ggml. I haven't run 65b enough to compare it with 30b, as I run these models with services like runpod and vast. 25 votes, 24 comments. Even with 4 bit quantization, it won't fit in 24GB, so I'm having to run that one on the CPU with llama. Members Online. 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. Hey everyone, I'm in the market for an RTX 4090 and I'm having a tough time deciding which one to get. LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b Where does one A6000 cost the same as two 4090? Here the A6000 is 50% more expensive. 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. 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. I didn't do a ton of work with the llama-1 65b long context models, 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 /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app Get the Reddit app Scan this QR code to download the app now. Hi All, I bought a Mac Studio m2 ultra (partially) for the purpose of doing inference on 65b LLM models in llama. cpp on 24gb VRAM, but you only get 1-2 tokens/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) 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. 18 MB llama_model_load_internal: using CUDA for GPU acceleration llama_model_load_internal: mem required = 20369. You only need 2 cards to run alpaca-lora-65B-GPTQ-4bit with short prompts, and three cards if you want to use prompts taking up the full context or using beam searching. Characters also seem to be more self-aware in 65B. Being built on the new Ada Lovelace architecture vs Ampere, the RTX 4090 has 2x the Tensor TFLOPS of the 3090. If you have a single 3090 or 4090, chances are you have tried to run a 2. I believe this is not very well optimized and tomorrow I'll see what I can do using a triton kernel to load the model. 65b EXL2 with ExllamaV2, or, full size model with transformers, Also, similar to the Ada Lovelace based RTX 4090, the newer Ada Lovelace based RTX 6000 also dropped support for NVLink. 65bpw quant of 70B models only to be disappointed by how unstable they tend to be due to their high perplexity. RTX 4090 RTX 3090 (because it supports NVLINK) An updated bitsandbytes with 4 bit training is about to be released to handle LLaMA 65B with 64 gigs of VRAM. If you're willing to take a chance with QC and/or coil whine, the Strix Scar 17/18 could be a option. A 4090 card would not use it's full potential when used with an eGPU and Thunderbolt 3 or TB4. I've got a choice of buying either the NVidia RTX A6000 or the NVidia RTX 4090. They suggested looking for bitcoin mining equipment. Just use cloud if model goes bigger than 24 GB GPU RTX 4090 is using a rather significantly cut down AD102 chip, especially in the L2 cache department. If gpt4 can be trimmed down somehow just a little, I think that would be the current best under 65B. Place to discuss news about video cards Members Online. 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. I use 4x45GB A40s This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API changes, and a "While the top-of-the-line LLaMA model (LLaMA-65B, with 65 billion parameters) goes toe-to-toe with similar offerings from competing AI labs DeepMind, Google, and OpenAI, arguably the most interesting development comes from the LLaMA-13B model, which, as previously mentioned, can reportedly outperform GPT-3 while running on a single GPU. 6-4. Your setup won't treat 2x Nvlinked 3090s as one 96GB VRAM core, but you can do larger models with quantization which Dettmers argue is optimal in most cases. 72 tokens/s, 104 tokens, context 19, seed 910757120) Output generated in 26. 9-4. The 4090 isn't just some top bin chip. It won't use both gpus and will be slow but you will be able try the model. Unlike the RTX solution where you basically cap out at 2x 4090 or 3x 3090 due to thermal and power constraints. LLaMA-30B on RTX 3090 is really amazing, and I already orderd one RTX A6000 to access LLaMA-65B. Or 2 x 24GB GPUs, which some people do have at home. I will have to load one and check. 0 ~7. 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. The only issue I am having is the wires to the GPU are causing difficulty closing the case. On the first 3060 12gb I'm running a 7b 4bit model (TheBloke's Vicuna 1. LLM360 has released K2 65b, I have a 5950x and 2 x 3090s running x8 and x4 on PCIE 3. Do people really open random spreadsheets found on Reddit? The OP might be totally genuine but this doesn’t seem smart. cpp is adding GPU support. 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. 1 4bit) and on the second 3060 12gb I'm running Stable Diffusion. This is consistent with what I’ve observed locally. 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. 65B (2x4090) - 15-20 tokens/s Reply reply (GPU 0 is an ASUS RTX 4090 TUF, GPU 1 is a Gigabyte 4090 Gaming OC) And actually, exllama is the only one that pegs my GPU utilization at 100%. 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. You should try running more modern models than the ones linked in the koboldAI main github. RTX 4090 Comparison Table - 12/2023 Discussion This is probably the most complete and up to date table of its kind at the moment. 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 The 4090’s efficency and cooling performance is impressive. However, those toys are nowhere near chatGPT or new Bing. Motherboard is Asus Pro Art AM5. Buying both cost about same as a 4090 does now and you pay it over longer time span and you get new up to date features too that 4090 doesnt, like dp2 for example. For more information: Subreddit to discuss about Llama, the large language model created by Meta AI. Or check it out in the app stores Subreddit to discuss about Llama, Seems like I should getting non OC RTX 4090 cards which are say capped at 450w power draw or Subreddit to discuss about Llama, DrJokeTech. The market has changed. Not seeing 4090 for $1250 in my neck of the woods, even used. Not that you need a 65B model to get good answers. Weirdly, inference seems to speed up over time. Today I actually bought 2 RTX 3060 with 12GB each to start experimenting with He is apparently about to unleash a way to fine tune 33B Llama on a RTX4090 (using an enhanced approach to 4 bit parameters), or 65B Llama on two RTX4090's. That's a bit too much for the popular dual rtx 3090 or rtx 4090 configurations that I've often seen mentioned. a 7b better than llama 65b now??? The RTX 4090 is definitely better than the 3060 for AI workloads. RTX 4090's Training throughput and Training throughput/$ are significantly higher than RTX 3090 across the deep learning models we tested, including use cases in vision, language, speech, and recommendation system. 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 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, What are some of the best LLMs (exact model name/size please) to use (along with the settings for gpu layers and context length) to best take advantage of my 32 GB RAM, AMD 5600X3D, RTX 4090 system? Thank you. For example, when training a SD LoRA, I get 1. I didn't want to say it because I only barely remember the performance data for llama 2. 0 ~2 In terms of memory bandwidth 1 P40 is I think 66% of an RTX 3090. 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. 33 MB (+ 5120. bin model, for example, but it's on the CPU. This seems like a solid deal, one of the best gaming laptops around for the price, LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b Reason: Fits neatly in a 4090 and is great to chat with. 6950 xt is good enough for 4k These are the speeds I get with different LLMs on my 4090 card at half precision. Or What token/s would I be looking at with a RTX 4090 and 64GB of RAM? Single 3090 = 4_K_M GGUF with llama. I think there's a 65b 4-bit gptq available; try it and see for yourself. 78 seconds (19. A RTX 3090 GPU has ~930 GB/s VRAM bandwidth, for 33B models will run at ~same speed on single 4090 and dual 4090. r/LocalLLaMA. A6000 Ada has AD102 (even a better one that on the RTX 4090) so performance will be great. 5 ~56 WizardCoder-3B-V1. A potential full AD102 chip graphics card would have 33% more L2 cache (96MB L2 cache total) and 12. Ti Super is better than the $1500 3090 The next gen equivalent let say cost $1000 in a year and better than 4090. I can run the 65b 4-bit quantized model of LLaMA right now but Loras / open chat models are limited. 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. 4090 is more powerful overall, a big improvement over the 3090ti, while 7900 xtx is weaker and smaller improvement over 6950 xt. No model after that ever worked, until Qwen-Coder-32B. 2-GGUF. Next upgrade maybe in 2027-2028. 0 颗星，最多 5 颗星 1¥22,699. Thanks to patch provided by emvw7yf below, the model now runs at almost 10 tokens per second for 1500 context length. For 60W of power consumption that is excellent. 8-1. 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. 05 seconds (14. cpp and offloading to gpu. 5 WizardCoder-Python-7B-V1. 7900 XTX I am not sure, as that uses ROCM. Not to mention with cloud, it actually scales. I would want to run models like Command R and maybe some of mixtral models also. bin: 5. 92 4080's , laptops with 4090 and 4080's etc all for sale, no restriction at all. 20 tokens/s, 512 Get the Reddit app Scan this QR code to download the app now. Or check it out in the app stores &nbsp; &nbsp; TOPICS. 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 Reddit iOS Reddit Android Reddit Premium About Reddit Advertise Blog Careers Press. g. ADMIN MOD microsoft/Phi-3-medium-128k-instruct-onnx-directml with RTX-4090 . 8 t/s on 65B_4bit with 2month old llama. Gaming Subreddit to discuss about Llama, the large language model created by Meta AI. I get around 10 t/s, In general, 2 bpw 65B models tend to be better then 16 bpw 30B models, let alone 8 bpw 8B models. RTX 4090 HAGPU Disabled 6-7 tokens/s 30 tokens/s 4-6 tokens/s 40+ tokens/s Speed Comparison:Aeala_VicUnlocked-alpaca-65b-4bit_128g GPTQ-for-LLaMa EXLlama (2X) RTX 4090 HAGPU Disabled 1 I'm running LLaMA-65B-4bit at roughly 2. But an off-the-shelf gaming pc with a RTX-3090 or RTX-4090 and two extra RTX-3090 installed (or hanging off the back in riser cables orin an eGPU enclosure) is reasonable for the dedicated hobbyist. cpp with GPU offload (3 t/s). The a6000 is slower here because it's the previous generation comparable to the 3090. Clearly llama 1 here started to think about the content instead of generating it. I saw a tweet by Nat Friedman mentioning 5 tokens/sec with a Apple M2 max with llama 65B, which required 44GB of ram or so. But it’s easily located on online retail stores. Running the Q4_K_M on an RTX 4090, it got it first try. Get the Reddit app Scan this QR code to download the app now. It mostly depends on your ram bandwith, with dual channel ddr4 you should have around 3. Multi GPU usage isn't solid like single. Subreddit to discuss about Llama, A Lenovo Legion 7i, with RTX 4090 (16GB VRAM), 32GB RAM. Or 70b/65b models work with llama. q2_K. That is pretty new though, with GTPQ for llama I get ~50% usage per card on 65B. 4a)3. 1a, DisplayPort 1. ) but there are ways now to offload this to CPU memory or even disk. Expand user menu Open settings menu. 0 GB/s. I am debating whether to get an RTX 4090 or a 4070 Ti Super Money is not really an issue but I also don’t want to throw money away, I just want to know if you think the 4090 is worth it or if I should buy a 4070 Ti Super at almost half the price and just spend the money on a 4090 when the new cards drop and the 4090 devalues (If it does get cheaper 😢) or just buy a 5090 or whatever Realistically, I would recommend looking into smaller models, llama 1 had a 65B variant but the speedup would not be worth the performance loss. 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. cpp Dual 3090 = 4. Reply reply What's the reasonable tks/s running 30B q5 with llama. Sure, it can happen on a 13B llama model on occation, but not so often that none of my attempts at that scenario succeeded. 21557 llama-65b. I just have a hard time pulling the trigger on a $1600 dollar GPU. 2-2. Or On my RTX 3090 setting LLAMA_CUDA_DMMV_X=64 LLAMA_CUDA_DMMV_Y=2 increases performance by 20%. 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 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. I'm having some trouble running inference on Llama-65B for moderate contexts (~1000 tokens). 6t/s 😂🤣 Reply reply LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b Get the Reddit app Scan this QR code to download the app now. The LLM climate is changing so quickly but I'm looking for suggestions for RP quality For Best Performance: Opt for a machine with a high-end GPU (like NVIDIA's latest RTX 3090 or RTX 4090) or dual GPU setup to accommodate the largest models (65B Add to this about 2 to 4 GB of additional VRAM for larger answers (Llama supports up to 2048 tokens max. LLM360 has released K2 65b, a fully Get the Reddit app Scan this QR code to download the app now. Please use our Discord server instead of supporting a company that acts against its users and unpaid Overall I do think that apple is definitely more impressive, inference wise I was personally getting more tok/s on the M2 with gpu accel, I hadn’t tried GPTQ though as I was mainly focusing on larger models (that a 4090 can’t load on its own), with the M2 Max I was getting around 4. It's actually a good value relative to what the current market offers. cpp: loading model from models/Wizard-Vicuna-30B-Uncensored. You may be better off spending the money on a used 3090 or saving up for a 4090, 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. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. Confirmed with Xwin-MLewd-13B-V0. But you can also connect 4, and be fine. Works fine on my machine but it's token per second speed is like 20-40% of my 3080 or 4090. Or check it out in the Subreddit to discuss about Llama, ADMIN MOD How many tokens per second do you guys get with GPUs like 3090 or 4090? (rtx 3060 12gb owner My AMD 7950X3D ( 16 core 32 threads), 64GB DDR5, Single RTX 4090 on 13B Xwin GGUF q8 can run at 45T/S. I don't see how you can prefer this. 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. i am thinking of getting a pc for running llama 70b locally, and do all sort of projects with it, sooo the thing is, i am confused on the hardware, i see rtx 4090 has 24 gb vram, and a6000 has 48gb, which can be spooled into 96gb by adding a second a6000, and rtx 4090 cannot spool vram like a6000, soo i mean does having 4 rtx 4090 make it possible in any way to run llama 70b, and is 144 votes, 48 comments. So far I only did SD and splitting 70b+ Here is nous-capybara up to 8k context @4. 00 MB per state) llama_model_load_internal: allocating batch_size x 1 MB = 512 MB VRAM for the scratch buffer Subreddit to discuss about Llama, I tried that with 65B on single 4090 and exllama is much slower (0. 64G @ 3200 + 16 core ryzen7 gets me ~0. Does that mean if let say, i load llama-3 70b on 4090+3090 vs 4090+4090, RTX 4090 + 5800X3D performance way lower than expected on Flight Simulator 2020 Yes, using exllama lately I can see my 2x4090 at 100% utilization on 65B, with 40 layers (of a total of 80) per GPU. Subreddit to discuss about Llama, While these models are massive, 65B parameters in some cases, quantization converts the parameters (the connections between neurons) I am running a 7950X with 64 gigs of RAM and a RTX 4090, and have had little issue doing anything I’d like to with 13B and 30B models. 12 tokens/s, 512 tokens, context 19, seed 1778944186) Output generated in 36. Never go down the way of buying datacenter gpus to make it work locally. This is the new Aurora R16 running a RTX 4090 FE. 2x RTX 3090 or RTX A6000 - 16-10 t/s depending on the context size (up to 4096) with exllamav2 using oobabooga (didn't notice any difference with exllama though but v2 sounds more cool) 2x RTX 4090 - ~20-16t/s but I use it rarely because it costs $$$ so don't remember the exact speed The base llama one is good for normal (official) stuff Get the Reddit app Scan this QR code to download the app now. Other 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. 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. With streaming it's ok and much much better now than any other way I tried to run the 65b. 1 t/s) than llama. 120 votes, 112 comments. CPU: i9-9900k GPU: RTX 3090 RAM: 64GB DDR4 Model: 14900k + 64gb ddr5 @ 6400 + 4090 LM Studio 4-8 experts activated, 16 cores offloaded, 23 layers - 3. 4090 32GB DDR5 6000 CL30 7800X3D Share it at 2. 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. The biggest models you can fit fully on your RTX 4090 are 33B parameter models. Models Subreddit to discuss about Llama, the large language model created by Meta AI. 5 on mistral 7b q8 and 2. Subreddit to discuss about Llama, RTX 4090 when doing inference, is 50-70% faster than the RTX 3090. cpp. I have what I consider a good laptop: Scar 18, i9 13980HX, RTX 4090 16GB, 64GB RAM. Just fyi there is a Reddit post that describes a solution. Your best option for even bigger your case, is treated as a single block of 48GB, hence the name unified memory. it takes over a minute (usually) per response using 65b GGML models on my rtx 4090, i9-13900k with 96GB of DDR5 memoryhow do you stand it? llama-30b-supercot surpassed 65b models on HF leaderboard Subreddit to discuss about Llama, by Meta AI. The activity bounces between GPUs but the load on the P40 is higher. 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. You can also undervolt, which will save on power consumption and heat, but barely lose any performance. 61K subscribers in the LocalLLaMA community. And it's much better in keeping them separated when you do a group chat with multiple characters with different personalities. Released Llama-3-8B-Instruct model with a melancholic attitude about everything. 75/hr setups, and the inference speed is faster than the 30B models I run on my local RTX 4090. llama. ggmlv3. RTX 4090 slower in YOLOv6 training than RTX 3080? You can connect 3, that would provide 4090 with max 450W of power, and in general gaming scenarios, 4090 doesn't hit that limit. Ego-Exo4D (Meta FAIR) released This subreddit is temporarily closed in protest of Reddit killing third party apps, (Ryzen 7 7700X + RTX 4090) and need some advices upvote r/LocalLLaMA. Here is a sample of Airoboros 65b, with the Coherent Creativity Get the Reddit app Scan this seed = 1689647281 ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 4090, compute capability 8. 9 it/s on a RTX 4090, at 768x768 and batch size 5. Subreddit to discuss Get the Reddit app Scan this QR code to download the app now. I want to buy a computer to run local LLaMa models. The cuda capability rating is really high too, just a little less than my 4090. To get 100t/s on q8 you would need to have 1. 4x RTX 4090 with FP8 compute rival the faster supercomputer in the world in Over 13b, obviously, yes. In Local LLama, I think you can run similar speed with RTX 3090s. 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 The answer is no, it performs worse than llama-30b 4-bit. 0 with no NVLINK. 5 tokens/sec using oobaboogas web hosting UI in a docker container. cpp have it as plug and play. This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API changes, Get the Reddit app Scan this QR code to download the app now. r/gpu. is something like 20GB, right? That fits entirely on the NVidia RTX 4090's 24GB VRAM, but is just a bit much for the 4080's 16GB VRAM. 3b Polish LLM pretrained on single RTX 4090 for ~3 months on Polish only content Subreddit to discuss about Llama, LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b and now with FP8 tensor cores you get 0. 5% more CUDA cores. I am able to run with llama. 5-2 t/s with 6700xt (12 GB) running WizardLM Uncensored 30B. I'm running a RTX 3090 on Windows 10 with 24 gigs of VRAM. Reply reply Get the Reddit app Scan this QR code to download the app now. I'm currently Anyone tried to run this model off their local machine? Does it require 4090 RTX to do so? I've come to the decision of having to decide between two rtx 3090s with nvlink or a single rtx 4090. cpp (13900K + 4090) ? Im gaming on 27" 1440p 165hz monitor, currently got RTX 2070 super OC. Discussion I thought I'd share this because, based on the number of downloads, maybe a couple dozen of us have tried this. RTX 3070 8GB GPTQ-for-LLaMA: Three Subreddit to discuss about Llama, the large language model created by Meta AI. My test prompt that only the og GPT-4 ever got right. q4_K_M. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then View community ranking In the Top 5% of largest communities on Reddit. More posts you may We are Reddit's primary hub for all things modding, LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b Reddit iOS Reddit Android Reddit Premium About Reddit Advertise Blog Careers Press. Really though, running gpt4-x 30B on CPU wasn't that bad for me with llama. 4090 has no SLI/NVLink. 8 on llama 2 13b q8. 3x 4090's for inference on small models up to 13b? LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b upvotes 65b is technically possible on a 4090 24gb with 64gb of system RAM using GGML, You could run 65b using llama. 65B models technically run at ~same speed on single 4090 and on dual 4090 up until from my experience. I know, I know, before you rip into me, I realize I could have bought something with CUDA support for less money but I use the Mac for other things and love the OS, energy use, form factor, noise level (I do music), etc. On the RTX 4090, NVIDIA has enabled 16384 GPU cores (+88% vs RTX 3080, +52% vs RTX 3090 Ti)—this alone will achieve a big performance boost. Get app Get the Reddit app Log In Log in to Reddit. LLM360 has released K2 65b, 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. It's an entirely new beast. If money is no issue go for 4090, its the only current generation gpu worth the cost, else i recommended the rx 6950 xt as it is about 25 percent weaker than 7900 xtx but much cheaper. The 6000 Ada is comparable to the 4090 and has more VRAM but is incredibly expensive. I was thinking about building the machine around the RTX 4090, but I keep seeing posts about awesome performances from MAC PCs. 4T tokens) is competitive with Chinchilla and Palm-540B. cpp with ggml quantization to share the model between a gpu and cpu. I've read some reviews and they all seem pretty solid, but I was wondering if anyone has any personal experience with any of these cards? In the same vein, Lama-65B wants 130GB of RAM to run. I saw that Lambda labs does offer a machine with 2 4090 cards, but they are about double the cost of unbranded workstation. 4090 RTX In my short testings "What happens if you abliterate positivity on LLaMa?" You get a Mopey Mule. bin llama I like being able to split a 65B 4_1 quantized ggml model between my 4090 with 24GB and my CPU I'm a hobbyist (albeit with an EE degree, and decades of programming experience), so I really enjoy tinkering oobabooga's textgen codebase. I have friends who spend significantly more on other hobbies. Or check it out in the app stores Subreddit to discuss about Llama, NVIDIA GeForce RTX 4090 Mem: 24GB Mem Bandwidth: 1,018 GB/s CUDA Cores: 16384 Tensor Cores: 512 FP16: LLaMA 65B / Llama 2 70B ~80GB A100 80GB ~128 GB RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100 ~32 GB /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, 144 votes, 71 comments. I'm able to get about 1. 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. Subreddit to discuss about Llama, Members Online. 13B 16k model uses 18 GB of VRAM, so the 4080 will have issues if you need the context. 4 bpw on a 4090. a lot of your questions related to dual GPUs are application specific. 5-4. cpp, The new GeForce RTX 4090 is based on the AD102 graphics processor, which is the world's first 4 nanometer GPU, fabricated at TSMC Taiwan. It's "only" got 72MB L2 cache. I'm trying to understand how the consumer-grade RTX 4090 can be faster and more affordable than the professional-grade RTX 4500 ADA. Single RTX but with penalty if running 2 get only 10 tokens/s. Some common machine learning libraries (e. 7 tok/s on q4_0 65b guanaco, and on the 4090+i9-13900K I was getting around 3. Also, the A6000 can be slower than two 4090, for example for the 65b llama model and its derivates in case of inference. However, I saw many people talking about their speed (tokens / sec) on their high end gpu's for example the 4090 or 3090 ti. In the future I would maybe want also simultaneous users. LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b There is a reason llama. vLLM is another comparable option. 13B version outperforms OPT and GPT-3 175B on most benchmarks. 1 tok/s You can use llama. Now, I sadly do not know enough about the 7900 XTX to compare. Even 65B is not ideal but it's much more consistent in more complicated cases. cpp the alpaca-lora-65B. My 4090 gets 50, a 4090 is 60% bigger than a 4080. Or check it out in the app stores Best Current Model for RTX 4090 . I have an rtx 4090 so wanted to use that to get the best local model set up I could. true. I use Vast. 2 and 2-2. Research Subreddit to discuss about Llama, Upgraded to RTX 4090 Laptop. NVIDIA didn't just add "more", they also made their units smarter. Terms & Policies 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. So people usually say that unless you forecast your project to go beyond a year, cloud is the winner. So if you need something NOW, just rent a bigger rig. The dual 6 pin to 8 pin connector adapter is required. It's easily worth the $400 premium over the rtx 4080, which is itself worth the premium over the 4070. Fully loaded up around 1. My own GPU-only version runs Llama-30B at 32 tokens/s on a 4090 right now, Apparently you can get about a 30% 50% boost on 65b using a 3090 right now, Each node has a single RTX 4090. a 7b Subreddit to discuss about Llama, System: OS: Ubuntu 22. Most people here don't need RTX 4090s. Subreddit to discuss about Llama, the large language model created by Meta AI. Or check it out in the app stores &nbsp; &nbsp; TOPICS Subreddit to discuss about Llama, Would make it just about 1/4 of the price of the rtx 4090 – a even better deal, I am in the process of buying a machine solely to run LLMs and RAG. cpp and not all the cores RTX 4090 Reply reply More replies More replies. A 3060 or 4060 is perfectly good for this scenario, will be getting close to 100fps in most games as long as you have an i5 or above CPU and 16GB min RAM. 133K subscribers in the LocalLLaMA community. 66 PFLOPS of compute for a RTX 4090 — this is more FLOPS then the entirety of the worlds fastest supercomputer in year 2007. Not worth replacing unless I need more performance for work and I can make the money back. . 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 30B model? The gpt4-x-alpaca 30B 4 bit is just a little too large at 24. I'm considering the MSI Gaming X Trio, Zotac Amp Extreme Airo, Gigabyte Gaming OC, and the MSI Suprim X. 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 cards: RTX 4090 vs RTX 3090 Deep Learning Benchmarks. Wonder how the llama 1 models designed for writing would compare. 8 tokens/sec with something like Llama-65B and a little faster with the quantized version. This subreddit is in protest due to Reddit's API policies. Dual 4090s can be placed on a motherboard with two slots spaced 4 slots In terms of hardware, I have a NVIDIA RTX 4090, 24GB GDDR6 and for RAM 64GB, 2x32GB, DDR5, 5200MHz. 55 seconds (18. Subreddit to discuss about Llama, 65B models to run on the $0. This way the software, like exllama or llama. 8 t/s for a 65b 4bit via pipelining for inference. Exllama by itself is very fast when model fits in VRAM completely. Currently I got 2x rtx 3090 and I amble to run int4 65B llama model. Interestingly, the RTX 4090 utilises GDDR6X memory, boasting a bandwidth of 1,008 GB/s, whereas the RTX 4500 ADA uses GDDR6 memory with a bandwidth of 432. We're now read-only indefinitely due to Reddit Incorporated's poor management and decisions related to third party platforms and content management. Phi-1. 0, 24GB GDDR6X, HDMI 2. 40 tokens/s, 511 tokens, 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. 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 Roughly 15 t/s for dual 4090. Or Thank you very much for the reply. I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. Getting around 0. While training, it can be up to 2x times faster. As title, I run single RTX 4090 FE at 40 tokens/s but with penalty if running dual 4090s. ) Still, anything that's aimed at hobbyists will usually fit in 24GB, so that'd generally eliminate that concern. With exllamav2, 2x 4090 can run 70B q4 at 15T/s. I was LLM360 has released K2 65b, 31 votes, 16 comments. q5_1. llama-30b. 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. I am thinking about buying two more rtx 3090 when I am /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will Just a word of warning for those of you wondering whether you should go ahead and splurge on a RTX 4090 It's much better in understanding character's hidden agenda and inner thoughts. Top 1% Rank by size . Much different than going up from a normal to a TI or to a super or whatever. Anything it did well for fictional content GPT4-X-Alpaca does better, anything it did well for factual content sft-do2 seems to be able to do unfiltered. 30B models aren't too bad though. Currently on a RTX 3070 ti and my CPU is 12th gen i7-12700k 12 core. No traditional fine-tuning, pure steering; source code/walkthrough guide included That or Llama 3 instruct needs no structure to act like it’s in a chat. Or check it out in the app stores RTX 4090 with 24GB VRAM i9-13900K maybe with the exception of Llama-65B with modified (higher) context size, due to its less efficient KV cache. GPU You do not need a 4090 for an eGPU set-up because of the 40Gps bandwith limit. Or 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, llama_model_load_internal: model size = 65B llama_model_load_internal: ggml ctx size = 0. Seems like a really solid combo for my 42inch LG C2. ADMIN MOD 7B models cannot fit in RTX 4090 VRAM (24GB) Question | Help that is unless I quantize Note: Reddit is dying due to terrible leadership from CEO /u/spez. It is REALLY slow with GPTQ for llama and multiGPU, like painfully slow, and I can't do 4K without waiting minutes for an answer lol Here is the speeds I got at 2048 context Output generated in 212. 51 seconds (2. Or check it out in the app but man, a 30b or 65b llama that only has to attend to 800 or so tokens seems to produce more coherent, interesting results I am planning on getting myself a RTX 4090. ai the most, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app I built a small local llm server with 2 rtx 3060 12gb. RTX 4090's Training throughput/Watt is Subreddit to discuss about Llama, the large language model created by Meta AI. Is RTX 4090 overkill for 1440p and would RTX 3090Ti be better for it? Even thou 4090 gets 60% better performance if not more,and would probably be future proof for 5 years if not more. But in I'm interested in the best hardware for inference requiring up to 64GB of memory. A 30B model, which can run in a consumer 24GB card like a 3090 or 4090, can give very good responses. 4b-2. Guanaco 7B, 13B, 33B and 65B models by Tim Dettmers: now for your local LLM pleasure Resources That is with a 4090, 13900k, and 64GB DDR5 @ 6000 MT/s. 65b exl2 Output generated in 5. I have a 4090 24gb and I run llama 3 70B instruct IQ2_S loading 77 layers on GPU. 65B version (trained on 1. 4GB so the next best would be vicuna 13B. Members Online What open source LLMs are your “daily driver” models that you use most often? Obviously I'm only able to run 65b models on the cpu/ram Llama 3 70B wins against GPT-4 Turbo in test code generation eval (and other +130 LLMs) New build for RTX 4090 comments. 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) LLM360 has released K2 65b, a fully 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. This adapter was included in the older Aurora R13 3090TI 1000W (late 2022 model). 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 [R] Meta AI open sources new SOTA LLM called LLaMA. 154K subscribers in the LocalLLaMA community. I don’t feel like the cost is completely crazy for a new PC. It requires ROCM to emulate CUDA, tought I think ooba and llama. Buy 4090 if you just want the best now. Or check it out in the app stores We tested an RTX 4090 on a Core i9-9900K and the 12900K, for example, got LLaMa 65b base model converted to int4 working with llama. (granted, it's not actually open source. Some RTX 4090 Highlights: 24 GB memory, priced at $1599. My preference would be a founders edition card there, and not a gamer light show card - which seem to be closer to $1700. For training I would probably prefer the A6000, though (according to current knowledge). 4 bpw should be better then llama 2 skipped the entire 30B~size base model for "reasons" and it looks like they are doing the same shit for llama3 Right now for about $2600 dollars I could a RTX 4090 and I5-13600k. cpp) support multi-GPU setups out of the Reddit's #1 spot for Pokémon GO™ discoveries and RTX A5000, RTX A6000, A40, A10, RTX 3090/4090 are all good choices for doing inference on this class of model. The RAM size is fabulous, the GPU speed doesn't compare to desktop. Members Online • awitod. Do you have solid data to say that it's going to give bad quality answers? Going by perplexity 65B 2. bin Reddit iOS Reddit Android Reddit Premium About Reddit Advertise Blog Careers Press. 9 llama. No need to do more though unless you’re curious. 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 /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will I have an opportunity to acquire two used RTX A4000 for roughly the same price as a used 3090 ($700USD). Finetuning could be done with Lora. I have recently built a full new PC with 64GB Ram, 24GB VRAM, and R9-7900xd3 CPU. All of the multi-node multi-gpu tutorials seem to it can run 65B parameter model in 40 to 45 gigabyte of vram on two GPUs. BTW: Don't buy Alienware. dnpoirl kpd qucbu fqml neq wesy apijy xsra jjgbf jadmq