Rtx 4090 llm reddit. I am about to cough up $2K for a 4090.


  • Rtx 4090 llm reddit While training, it can be up to 2x times Nvidia just announced a 4090D. So far ive ran llama2 13B gptq, codellama 33b gguf, and llama2 70b ggml. 3b Polish LLM pretrained on single RTX 4090 for ~3 months on Polish only content…. After the initial load and first text generation which is extremely slow at ~0. Now, RTX 4090 when doing inference, is 50-70% faster than the RTX 3090. But for LLM, we don't need that much compute. I have used this 5. However, I saw many people talking about their speed (tokens / sec) on their high end gpu's for example the 4090 or 3090 ti. 0 GB/s. I have a 13700+4090+64gb ram, and ive been getting the 13B 6bit models and my PC can run them. 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. E. I'm thinking it should be cheaper than the normal 4090. On inference the 4090 can be between 15% to 60% faster (I think on LLMs the difference is less, on image generation it is most of the time 60% faster) For training, both LLM or t2i, the 4090 is 2x times faster or more. A problem is that Nvidia says it's for China only. 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 purposes( Although I built a small local llm server with 2 rtx 3060 12gb. 1 4bit) and on the second 3060 12gb I'm running Stable Diffusion. I would like to train/fine-tune ASR, LLM, TTS, stable diffusion, etc deep learning models. The outcomes are the same, you get 80% performance at a 50% power limit. I have an Alienware R15 32G DDR5, i9, RTX4090. The LLM climate is changing so quickly but I'm looking for suggestions for RP quality and also models I could get away with higher context sizes with. Subreddit to discuss about Llama, the large language model created by Meta AI. My advice build a desktop with a 4090 (what I have currently) or 2x3090 (for more VRAM at lower cost, seen people talking about this setup on here). 2t/s. 2t/s, suhsequent text generation is about 1. I'm interested in running AI apps like Whisper, Vicuna, and Stable Diffusion on it. 2xlarge EC2 (with Tesla V100) or invest in building a high-performance rig at home with multiple RTX 4090s for training a large language model? Not seeing 4090 for $1250 in my neck of the woods, even used. . Get the Reddit app Scan this QR code to download the app now LLM to Brainstorm Videogame Quests (Rtx 4090) Question | Help Hello, (Ryzen 7 7700X + RTX 4090 Now, about RTX 3090 vs RTX 4090 vs RTX A6000 vs RTX A6000 Ada, since I tested most of them. The GPU, an RTX 4090, looks great, but I'm unsure if the CPU is powerful enough. xxx instance on AWS with two GPUs to play around with; it will be a lot cheaper, and you'll learn the actual infrastructure that this technology revolves around. The 4090 is incredibly efficient given its performance, it also draws way less power when idling than the 3090 or 3090Ti. 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. The 24GB of VRAM will still be there. Aug 1, 2023 · I have recently built a full new PC with 64GB Ram, 24GB VRAM, and R9-7900xd3 CPU. I have to build a pc for fine tuning purpose i am going with top of the line RTX 4090 with 14th gen i9 cpu. Apologies for reviving this post a month later, but using a 4090 with 13900k and 32 Gb of DDR5 ram My speed is abysmal! Output generated in 41. May 29, 2024 · The diminishing performance returns of 4090 have been evaluated before. It won't be missed for inference. 37 tokens/s, 98 tokens, context 471, seed 1804586797) If you want to play video games too, the 4090 is the way to go. vLLM is another comparable option. Mar 11, 2024 · LM Studio allows you to pick whether to run the model using CPU and RAM or using GPU and VRAM. 39 seconds (2. I plan to upgrade the RAM to 64 GB and also use the PC for gaming. g. It also shows the tok/s metric at the bottom of the chat dialog. Won't be able to fit as big of models on the laptop and I'm guessing it's more expensive than a desktop. Since you seem to want a laptop, use any laptop to ssh into the desktop, clone text-generation-w A Lenovo Legion 7i, with RTX 4090 (16GB VRAM), 32GB RAM. I am about to cough up $2K for a 4090. 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. Any recommendations would be great. For LLM workloads and FP8 performance, 4x 4090 is basically equivalent to 3x A6000 when it comes to VRAM size and 8x A6000 when it comes raw processing power. Nov 11, 2024 · But occasionally, when you see an LLM take a complex, highly detailed, convoluted instruction in natural language and produce code that instantly works, without any modification, it's a good idea to take a few minutes to piss your pants out of sheer terror. Alternatively- VRAM is life, so you'll feel a HUGE quality of life improvement by going from 24GB VRAM to 48GB VRAM. 3090 is either 2nd hands or new for the similar price as 4090 Mac with unified memory is expensive and limited support. Some RTX 4090 Highlights: 24 GB memory, priced at $1599. The goal is a reasonable configuration for running LLMs, like a quantized 70B llama2, or multiple smaller models in a crude Mixture of Experts layout. 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. It will have 10% less cores than the normal 4090. If your case, mobo, and budget can fit them, get 4090s. Want to confirm with the community this is a good choice. Hi, We're doing LLM these days, like everyone it seems, and I'm building some workstations for software and prompt engineers to increase productivity; yes, cloud resources exist, but a box under the desk is very hard to beat for fast iterations; read a new Arxiv pre-print about a chain-of-thoughts variant and hack together a quick prototype in Python, etc. I was able to load 70B GGML model offloading 42 layers onto the GPU using oobabooga. For FP16, the 4090 ends up being bandwidth limited most of the time and you won't actually get close to those 330 TFLOPS anyway. In practice the 3090 ends up being only about 30% slower than the 4090, so the price/performance ratio is still better, with the available software and models. in this reddit post a user shared 3DMark FireStrike scores from RTX 4090. Commercial-scale ML with distributed compute is a skillset best developed using a cloud compute solution, not two 4090s on your desktop. My preference would be a founders edition card there, and not a gamer light show card - which seem to be closer to $1700. Similar on the 4090 vs A6000 Ada case. The LLM Creativity I am building a PC for deep learning. A6000 for LLM is a bad deal. This seems like a solid deal, one of the best gaming laptops around for the price, if I'm going to go that route. I would want to run models like Command R and maybe some of mixtral models also. Anything better than 4090 from Nvidia is too expensive. With lmdeploy, AWQ, and KV cache quantization on llama 2 13b I’m able to get 115 tokens/s with a single session on an RTX 4090. MacBook Pro M1 at steep discount, with 64GB Unified memory. Yes, it's two generations old, but it's discounted. Had no idea the price gap was that small haha otherwise I would've recommended the 4090 straight away especially given the price in energy increase you've more than likely experienced RTX 4090 vs RTX 3090 Deep Learning Benchmarks. 94GB version of fine-tuned Mistral 7B and did a quick test of both options (CPU vs GPU) and here're the results. In the future I would maybe want also simultaneous users. I'm trying to understand how the consumer-grade RTX 4090 can be faster and more affordable than the professional-grade RTX 4500 ADA. RTX 4090's Training throughput and Training throughput/$ are significantly higher than RTX 3090 across the deep learning models we tested, including use cases in vision, language, speech, and recommendation system. Just use the cheapest g. 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. Here are the specs: CPU: AMD Ryzen 9 5950X (16 x 3. Speed wise, ive been dumping as much layers I can into my RTX and getting decent performance , i havent benchmarked it yet but im getting like 20-40 tokens/ second. I’m building a dual 4090 setup for local genAI experiments. 5090 is still 1. Skip to main content Open menu Open navigation Go to Reddit Home 144 votes, 48 comments. I am in the process of buying a machine solely to run LLMs and RAG. RTX 3090 is a little (1-3%) faster than the RTX A6000, assuming what you're doing fits on 24GB VRAM. 4 GHz) GPU: RTX 4090 24 GB RAM: 32 GB DDR4-3600MHz I'm afraid the only answer I'm going to get is that I need to buy another 4090 to speed up the 70b model. I was thinking about building the machine around the RTX 4090, but I keep seeing posts about awesome performances from MAC PCs. Hopefully that isn't the case. 5 years away, maybe 2 years. On the first 3060 12gb I'm running a 7b 4bit model (TheBloke's Vicuna 1. Hello everyone, I'm currently at a crossroads with a decision that I believe many in this community might have faced or will face at some point: Should I use cloud-based GPU instances like AWS's p3. help me out with the benchmarks. roixhdpr nenkkulo ggdjh xgbmoa myyv nvathnkt ylfg riqmrgk orcyn jcxld