Llama 65b rtx 4090 reddit a 7b better than llama 65b now??? Mistral Orca is OUT! See more posts like this in r 65b is technically possible on a 4090 24gb with 64gb of system RAM using GGML, but it's like 50 seconds per reply. 33B models will run at ~same speed on single 4090 and dual 4090. 4090 has no SLI/NVLink. 5 tokens/sec using oobaboogas web hosting UI in a docker container. 2x 4090 Multiple m. 58 TFLOPS FP32: 82. Hi, I love the idea of open source. 2 tokens/s 22+ tokens/s Basically I couldn't believe it when I saw it. Really though, running gpt4-x 30B on CPU wasn't that bad for me with llama. 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. Mobo is z690. Now, RTX 4090 when doing inference, is 50-70% faster than the RTX 3090. After the initial This chart showcases a range of benchmarks for GPU performance while running large language models like LLaMA and Llama-2, using various quantizations. . got LLaMa 65b base model converted to int4 working with llama. 65B models technically run at ~same speed on single 4090 and on dual 4090 up until first from my experience. cpp and offloading to gpu. cpp is adding GPU support. 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. Not that you need a 65B model to get good answers. A6000 Ada has AD102 (even a better one that on the RTX 4090) so performance will be great. I have an Alienware R15 32G DDR5, i9, RTX4090. In comparison, even the most powerful Apple Silicon chips struggle to Subreddit to discuss about Llama, the large language model created by Meta AI. Or 2 x 24GB GPUs, which some people do have at home. 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 A 65B model in 4bit will fit in a 48GB GPU. I haven't run 65b enough to compare it with 30b, as I run these models with services like runpod and vast. 64G @ 3200 + 16 core ryzen7 gets me ~0. Will this 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. Just fyi there is a Reddit post that describes a solution I want to buy a computer to run local LLaMa models. 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. Premium Powerups RTX 4090 24GB Owner: Stupid, you don't need that much VRAM. Characters also seem to be more self-aware in 65B. 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. Even 65B is not ideal but it's much more consistent in more complicated cases. Or check it out in the app stores We tested an RTX 4090 on a Core i9-9900K and the 12900K, for example, and the latter was almost twice as fast. Also, the A6000 can be slower than two 4090, for example for the 65b llama model and its derivates in case of inference. Currently I got 2x rtx 3090 and I amble to run int4 65B llama model. I was able to load 70B GGML model offloading 42 layers onto the GPU using oobabooga. Limiting the I'm running a pretty similar setup (13700k instead of the Ryzen, but also 4090 and 64GB RAM) and I've been getting some pretty impressive results using If you want to play video games too, the 4090 is the way to go. 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. Reddit iOS Reddit Android Reddit Premium About Reddit Advertise Blog Careers Press. NVIDIA GeForce RTX 4090 Mem: 24GB Mem Bandwidth: 1,018 GB/s CUDA Cores: 16384 Tensor Cores: 512 FP16: 82. Finetuning could be done with Lora. This seems like a solid deal, one of the best gaming laptops around for the price, if I'm going to go that route. Model card Files Files Use in Transformers. For the 4090, it is best to opt for a thinner version, like PNY’s 3-slot 4090. 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. 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 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. 8 tokens/sec with something like Llama-65B and a little faster with the quantized version. A Lenovo Legion 7i, with RTX 4090 (16GB VRAM), 32GB RAM. 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. I really like the answers it gives, it's slow tho. We're now read-only indefinitely due to Reddit Where does one A6000 cost the same as two 4090? Here the A6000 is 50% more expensive. Similar on the 4090 vs A6000 Ada case. And it's much better in keeping them separated when you do a group chat with multiple characters with different personalities. That is pretty new though, with GTPQ for llama I get ~50% usage per card on 65B. As you say, the A6000 (but even more so dual 3090/4090) is the better option. 0 coins. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation Subreddit to discuss about Llama, the large language model created by Meta AI. ( a few weeks later ) RTX 4090 24GB Owner: F**k Subreddit to discuss about Llama, the large language model created by Meta AI. Advertisement Coins. With streaming it's ok and much much better now than any other way I tried to run the 65b. Sort by: I'm running LLaMA-65B-4bit at roughly 2. A 30B model, which can run in a consumer 24GB card like a 3090 or 4090, can give very good responses. From other results we know that exllama inference scales inverse linearly with the model size, so 65b on w7900 (assuming the only difference being the vram, let's ignore the 10% on memory bandwidth for now) should be around 12 t/s. 8 t/s on 65B_4bit with 2month old llama. For AI: the 3090 and 4090 are both so fast that you won't really feel a huge difference in speed jumping up from the 3090 to 4090 in terms of inference. Or check it out in the app stores but man, 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. 58 TFLOPS FP64: 0. If gpt4 can be trimmed down somehow just a little, I think that would be the current best under 65B. Get the Reddit app Scan this QR code to download the app now. 4GB so the next best would be vicuna 13B. Multi GPU usage isn't solid like single. 30B models aren't too bad though. 2 SSDs Share Add a Comment. Text Generation Transformers llama Inference Endpoints text-generation-inference. Or check it out in the app stores TOPICS Subreddit to discuss about Llama, the large language model created by Meta AI. Fully loaded up around 1. 8 t/s for a 65b 4bit via pipelining for inference. 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. It's much better in understanding character's hidden agenda and inner thoughts. 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 reproducible open source LLM matching Llama 2 70b 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, Get the Reddit app Scan this QR code to download the app now. 3b Polish LLM pretrained on single RTX 4090 for ~3 months on Polish only content and now with FP8 tensor cores you get 0. Alternatively- VRAM is life, so you'll feel a HUGE quality of life improvement by going from 24GB VRAM to 48GB VRAM. 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 I'm running a RTX 3090 on Windows 10 with 24 gigs of VRAM. Though A6000 Ada clocks lower and VRAM is slower, but it will perform pretty similarly to the RTX 4090. Getting around 0. I think there's a 65b 4-bit gptq available; try it and see for yourself. cpp. Subreddit to discuss about Llama, the large language model created by Meta AI. 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 There is a reason llama. I have an rtx 4090 so wanted to use that to get the best local model set up I could. 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. The activity bounces between GPUs but the load on the P40 is higher. Yes, using exllama lately I can see my 2x4090 at 100% utilization on 65B, with 40 layers (of a total of 80) per GPU. RTX 4090 all VRAM vs. 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 A RTX 3090 GPU has ~930 GB/s VRAM bandwidth, for comparison. 66 PFLOPS of compute for a RTX 4090 — this is more FLOPS then the entirety of the worlds fastest supercomputer in year 2007. While training, it can be up to 2x times 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 View community ranking In the Top 10% of largest communities on Reddit. System: OS: Ubuntu 22. 2 tokens/s 13 tokens/s (2X) RTX 4090 HAGPU Enabled 2-2. Aeala_VicUnlocked-alpaca-65b-4bit_128g GPTQ-for-LLaMa EXLlama (2X) RTX 4090 HAGPU Disabled 1-1. Here is a sample 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 (although 2 x 4090 would fit a llama-65b GPTQ as well, right, are you inferencing bigger than that?). like 9. 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. 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. Thanks to patch provided by emvw7yf below, the model now runs at almost 10 tokens per second for 1500 context length. cpp and not all the Currently on a RTX 3070 ti and my CPU is 12th gen i7-12700k 12 core. 4x RTX 4090 with FP8 compute rival the faster supercomputer in the world in Over 13b, obviously, yes. Dual 4090s can be placed on a motherboard with two slots spaced 4 slots apart, without being loud. You could run 65b using llama. I am running a 7950X with 64 gigs of RAM and a RTX 4090, and have had little issue doing Get the Reddit app Scan this QR code to download the app now. The speed increment is HUGE, even the GPU has very 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. I am thinking about buying two more rtx 3090 when I am see how fast community is making progress. 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. Or check it out in the app stores i load llama-3 70b on 4090+3090 vs 4090+4090, I will see bigger speed difference with the 4090+4090 setup? RTX 4090 + 5800X3D performance way Now, about RTX 3090 vs RTX 4090 vs RTX A6000 vs RTX A6000 Ada, since I tested most of them. 4080 + CPU . /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper Get the Reddit app Scan this QR code to download the app now. I have read the recommendations regarding the hardware in the Wiki of this Reddit. It would be too slow to run model larger than 65B. The data covers a set of GPUs, from Apple Silicon M series LLaMA-65B-4bit-32g. For training I would probably prefer the A6000, though (according to current knowledge). RTX 3090 is a little (1-3%) faster than the RTX A6000, assuming what you're doing fits on 24GB VRAM. The gpt4-x-alpaca 30B 4 bit is just a little too large at 24. zbtnc ewi swcb avrmc vlfbf xcew ydeot hqphg edsvmoxe stzcxn