Stable diffusion nvlink reddit. bat files), and switch between .


  • Stable diffusion nvlink reddit I've few questions about fine tuning of Stable Diffusion XL edit subscriptions. Bruh this comment is old and second you seem to have a hard on for feeling better for larping as a rich mf. This is strictly for personal interest (I have a degree in AI but I got it in 1989 when the field was quite different) so I am not looking at plunking down a huge amount of money. I've heard a lot of talk about the people making it run on 8GB just making stuff up, but the truth is, because of the way machine learning models work, there is no way to predict with certainty how much memory it's going to take. I'm considering purchasing two RTX A6000s and NVLink-ing them, would Stable Diffusion be able to use both at the same time to generate a single image and use all 96GB of VRAM? I had the proof that my NVLink setup was working properly with Redshift, but I never managed to find a solution to make it work with Stable Diffusion. 3 days ago · Hello all, I'm approaching now to Stable Diffusion and generative AI on images. At half the price of a used 3090 I am extremely tempted and the 40 series don't support nvlink so not only are they too expensive but you don't get as much memory space. Anyone here running stable diffusion on dual 3090 (nvlink)? Can you please share your experience and some benchmarks? I am building a new pc for 3d work, mostly blender, and I need to figure out what is the best way to go. I have tried to make them work together in some way and while it seems possible in theory, it just doesn't work in practice. Edit #2: Hmm, I'm getting conflicting information, telling me that NVLink / shared VRAM is… Must be related to Stable Diffusion in some way, comparisons with other AI generation platforms are accepted. Indeed, if you do that, your system will lower your 4090 performances to match the 3090 perfs (4090 will be waiting for the 3090 to finish its calculations), so it will be a waste of power compute. For a good idea of how the PCIe vs NVlink bandwidth compare, I'm playing with making LORAs using oobabooga with 2x3090. Edit: Apparently, there is no NVLink for 4090s, whoops. I think there are multigpu generation libraries, but stuff needs to be made to work specifically for them. 0 - or with the one before, NVLink 3. I would like to train/fine-tune ASR, LLM, TTS, stable diffusion, etc deep learning models. There's people who've reported getting it to work, but most people haven't been able to. The average price of a P100 is about $300-$350 USD, you can buy two P100's for the price of a 3090 and still have a bit of change left over. It's obviously slower than Nvidia GPUs , but still easily fast enough to play around with. Second not everyone is gonna buy a100s for stable diffusion as a hobby. You don't need the 4070 just to drive the displays, and you'd need to purchase a much lager PSU, case and maybe motherboard to fit dual GPUs so you'd be better off building a whole 2nd machine. I see around a 40-50% speedup when running with NVlink on Ubuntu with everything but the OS and p2p being the same. bat files), and switch between I am getting into Stable Diffusion and while my AMD RX 6800 does an okay job creating images getting it to train models is painful. popular-all-random-usersAskReddit-pics-funny-movies-gaming-worldnews-news-todayilearned-nottheonion-explainlikeimfive-mildlyinteresting-DIY-videos-OldSchoolCool Hi all, I was lucky enough to find a used dell C4130 with dual 14 core xeons, 256GB memory and 4xP100 tesla's today. . Summary: Single 4090 is better for general use, but two 3090s could be better for iteration. 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 have a machine with 2 Nvidia 3090 cards (no NVlink). The use case for this build is mainly for training/inferencing stable-diffusion models, deepfacelab, and running local large language models on GNU+Linux Debian 12 Bookworm x86_64. With a bridge, you have access to 44GB for models. I believe this is usually done to improve performance but I was wondering if it could be done to increase memory as well? I got into AI via robotics and I'm choosing my first GPU for Stable Diffusion. 2x the performance of the A100 in AI inference (512x512 image generation with stable diffusion 2. Note: The A100 was Nvidia's previous generation top of the line GPU for AI applications. 1). So if Rendering/Generating/etc is taking 10 seconds on 1 GPU, it will take close to 5 seconds on 2 GPUs connected with NVlink, but it will still have just 24GB VRAM. What this gets you is 32GB HBM2 VRAM (much faster than the 3090) split over two cards and performance that if able to be used by your workflow exceeds that of a single 3090. just a correction, NVlink does not add VRAMs of the cards, 2 24GB GPUs connected with NVlink will still have 24GB overall VRAM. This Subreddit is community run and does not represent NVIDIA in any capacity unless specified. 1 for a bit, but it's barely using half the vram of a single card. 16Gb is the minimum for me, and the maximum I can afford (I think). Hi, I would definitely not recommend you to combine a 3090 and a 4090 in the same build for deep learning purposes. If you want to compare the latest versions of each tech, then you should match PCIe with NVLink 4. Paper: "Generative Models: What do they know? Do they know things? Let's find out!" See my comment for details. I'm also thinking of picking up a couple of these over a single 3090. NVIDIA claims it's got 1. If there is a solution, I would be glad to hear about it ! No, stable diffusion cannot use nvlink for shared vram. I am building a PC for deep learning. The first is 100 times faster than PCIe 5, the second, 10 times. What is the best way to take advantage my setup with Stable Diffusion? I was playing around with Grisk 0. 0. I just don't think 12Gb would cut it. And there is no other traffic on the NVLink. That said, I can run two instances of Automatic1111, one for each GPU (using "set CUDA_VISIBLE_DEVICES=#" in my launcher . Please help me to get to my final decision! Evidence has been found that generative image models - including Stable Diffusion - have representations of these scene characteristics: surface normals, depth, albedo, and shading. Just upgraded this week to the 4060ti w/16gb…not much of a speed increase, but the 5 extra gb has been great. Beyond the money aspect and whats the best bang for your buck, would running 2 x 3090 with a sli link give you full access to the 48GB or VRAM, or would the system still not know what to do with the second card? Some Nvidia GPU's provide a hardware option called NVLink which allows you to connect two cards together. I don't care much about speed, I care a lot about memory. Blender will benefit from the dual gpu's but, is it the same for SD? Thank you. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. Third you're talking about bare minimum and bare minimum for stable diffusion is like a 1660 , even laptop grade one works just fine. it distributes the processing between 2 GPUs. Obviously if you want to consistently use it getting dedicated hardware would be better, but I would give it a try before putting too much effort and money into it. I've used Stable Diffusion myself on a 6900xt, and it works without much issue. I heard these can still use Nov 28, 2023 · I like to run Stable Video Diffusion, Tortoise TTS, Falcon 7B LLM, OpenAI Whisper, etc. I have a pair of 1080's. I'm leaning heavily towards the RTX 2000 Ada Gen. It's not worth trying to keep using the 4070 in the same machine if you want to install a 4090. Hi all, I'm looking at building a new home system specifically for AI, Deep learning and Stable diffusion/swarm I'm looking to stack 6(?) RTX 2080Tis with 2 Intel Xeon E5-2699-v3s, 512GB 2400Mhz RAM. In Windows, I don't have NVlink working, on Ubuntu, I do. You would need to set up custom software specific for animatediff to function across nvlink. 7x the performance of the A100 in training a LoRA for GPT-40B, and 1. I wonder if there are any better values out there. Not reliably. Questions: I am in Canada with 120V per power outlet, the estimated wattage of this build is ~1200W, to not overload the outlet, should I use 2 PSUs instead and plug A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more. I didn’t think 3090s did NVLink? I thought the latest model to support that was the 2080… And BTW: I’ve had 2x 2080ti, and use NVLink for some software (Octane Renderer), but it’s never been supported with Stable Diffusion. and be able to train(or at least fine tune) them in my local computer at the fastest speed. 2 Be respectful and follow Reddit's Content Policy. oeur uuh arc moryd zke eyvnm ttxq edzafw ohjpnq ewdwth