Best llm for 24gb vram reddit. I accept no other answer lol.

Best llm for 24gb vram reddit I'm particularly interested in running models like LLMs 7B, 13B, and even 30B. Alltogether, you can build a machine that will run a lot of the recent models up to 30B parameter size for under $800 USD, and it will run the smaller ones relativily easily. GPU: MSI RX7900-XTX Gaming trio classic (24GB VRAM) RAM: Corsair Vengeance 32GBx2 5200MHz I think the setup is one of the best VFM but only if it works for GenAI :( Exploration: After spending nearly 10 days with my setup, these are my observations: AMD has a lot to do in terms of catching up to Nvidia's software usability. So I took the best 70B according to my previous tests, and re-tested that again with various formats and quants. Its just night and day. In a single 3090 24gb card, you can get 37 tps with 8bit wizard coder 15b and 6k context OR phind v2 codellama 34b in 4bit with 20 tps and 2k context. Larger models that don't fully fit on the card are obviously much slower and the biggest slowdown is in context/prompt ingestion more than inference/text generation, at least on my setup. You could probably get A LOT faster since your 3090 has twice as much vram as my 3080ti, and most stuff would be running on it. LLM List LLM Hosting LLM Leaderboards Blog Newsfeed Advertise. If so, first run Skyrim and check in task manager how much VRAM you have free. They’re going for as low as $700 if you watch for deals, and have 24gb v-ram, or you can get one for $800 all day long with buy-it-now. Fimbulvetr-v2 or Kaiju are my usual recommendations at that size, but there are other good ones. with 24GB VRAM you can run exl2 for 34b, 4x8b, 8x7b, and with RAM too (Kobold) you could run 70b at low speeds. 7B model uses 21-28GB VRAM. RAM isn't much of an issue as I have 32GB, but the 10GB of VRAM in my 3080 seems to be pushing the bare minimum of Good LLMs don't even fit in 4090 24GB as they are approx 50-70GBs. Certainly 2x7b models would fit (for example Darebeagel or Blue-Orchid), probably 2x11b models (such as PrimaMonarch-EroSumika), and maybe 4x7b models too (laserxtral has some small For example, if you try to make a simple 2-dimensional SNN to make cat detector for the picture collection, you don't need RTX 4090 even for training, let alone use. Because your 24gb Vram with offload This is somewhat similar to the previous option, but with the purchase of some used 3090s, you get 24GB RAM, allowing you to split models and have 48GB worth of VRAM for inference. 75 = 24GB VRAM) at this point in time? But I can say that the iq2xs is surprisingly good and so far the best llm I can run with 24gb. IMO if it's just for personal hobbyist usage I would not go for an A4000, get a used 3090 with 24GB of VRAM. As ever, however, your speed is still dramatically tied to VRAM. 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). 4bpw runs, but imo is not worth it, as the quality loss is kinda big. If you find you’re still a little tight in VRAM, that same HF account has a 3. An IQ4xs of the model is roughly around 53gb before taking context size into account. it's also true that a single GPU setup with a consumer card quite often doesn't let you run much of anything better than Joe Schmoe with practically any available card. During my research, I came across the RTX 4500 ADA, priced at approximately £2,519, featuring 24GB of vRAM and 7680 CUDA cores. 1 GPTQ 4bit runs well and fast, but some GGML models with 13B 4bit/5bit quantization are also good. CPU implementations are going to be very slow. 5 bpw that run fast but the perplexity was unbearable. I have a m40 with 24gb vram. Given some of the processing is limited by vram, is the P40 24GB line still useable? Thats as much vram as the 4090 and 309 Exactly! RTX 3090 has the best or at least one of the best vram/dollar value (rtx 3060 and p 40 are also good choices, but the first is smaller and the latter is slower). the model name the quant type (GGUF and EXL2 for now, GPTQ later) the quant size the context size cache type ---> Not my work, all the glory belongs to NyxKrage <--- Posted by u/yupignome - 1 vote and no comments As above, a GPTQ 7B 4bit 32G quantized model if you're running it in VRAM (like Longjumping posted above). Even The idea of being able to run a LLM locally seems almost too good to be true so I'd like to try it out but as far as I know this requires a lot of RAM and VRAM. 70b as exl2 2. At IQ3_S or Q3_K_S, it can run on a laptop with 16GB RAM and 8GB VRAM with 10-11 layers offloaded at 4096 ctx, but I recommend Q3_K_S at 11 layers which generates faster than ~5 tokens/second. Setup: 13700k + 64 GB RAM + RTX 4060 Ti 16 GB VRAM Which quantizations, layer offloading and settings can you recommend? About 5 t/s with Q4 is the best I was able to achieve so far. Then if you want to get more serious later with more vram, the market has gotten that much better by then and you can look at more expensive models with more vram. The first runs into memory issues, the second, loaded with llama. It appears to me that having 24gb VRAM gets you access to a lot of really great models, but 48gb VRAM really opens the door towards the impressive Yesterday I tested 70B like Twix, Dawn, and lvlz (exl2 2. I don't even need more compute power, I just need more vram so I can run both sd an a prompt generating llm of Command-R was the only closish contender in my testing but it's still not that close, and it doesn't have gqa either, context will take up a ton of space on vram. I know 24GB is not alot in LLM realm, but that's what I can afford. This subreddit has been temporarily closed in protest of Reddit's attempt to kill third-party apps through Note how op was wishing for an a2000 with 24gb vram instead of an "openCL"-compatible card with 24gb vram? There's a good reason for this. L3 based 8B models can be really good, I'd recommend Stheno. I'm wondering what the latency is from prompt sending to first token generation. As for best option with 16gb vram I would probably say it's either mixtral or a yi model for short context or a mistral fine tune. LLMs for 24GB VRAM: Large Language Models (Open-Source LLMs) Fit in 24GB VRAM with Dynamic Sorting and Filtering. The speed will be pretty decent, far faster than using the CPU. This VRAM calculator helps you figure out the required memory to run an LLM, given . Or check it out in the app stores i7 13700KF, 128GB RAM, 3090 24GB VRAM koboldcpp for initial testing llama-cpp-python for coding my own stuff Members Online. For comparison on my 64Gb laptop using an Intel Core i5 1235U, I have to wait almost 1 hour to get the top current model, Smaug 0. What is your best guide to train LLM from your customised dataset? upvotes r/LocalLLaMA. cpp. In contrast, the flagship RTX 4090, also based on the ADA architecture, is priced at £1,763, with 24GB of vRAM and 16384 CUDA cores. With that if you want SDXL as well, you would easily be needing over 100GB VRAM for best use. Discussion The tradition must not die! IceCoffeeRP, or RPStew for a bigger model (200K context possible, but 24GB of VRAM means I'm around 40K without going over). Basically, I’m currently building customer service AI assistants for a client, exploring the open sources alternatives and best practices for great results outside of bigger models subscriptions. I'm currently working on a MacBook Air equipped with an M3 chip, 24 GB of unified memory, and a 256 GB SSD. I have tried llama3-8b and phi3-3. It's about as fast as my natural reading speed, so way faster than 1T/s. x quantization allows me to load it to vram ) and only Opus eventually reached a similar level of creativity and following prompt as MxLewd, but there were some flaws (it gave up when I should write about cow :D so I expect it's limited to human-like scenarios only) This thread should be pinned or reposted once a week, or something. Question | Help I tried using Dolphin-mixtral but having to input that the kittens will die a lot of times is very annoying , just want something that :) Yeah it's $1600 for 24 gigs, but $7k for 48? That's just silly. No stupid questions, we are all learning as we go in this space. What do you think is the most cost effective way to buy a Mac with 32GB Unified Memory (32 * 0. I’ve tested a decent amount of stuff and this has felt like the best balance of speed and performance on 24gb vram by far for rp. I'm planning to build a server focused on machine learning, inferencing, and LLM chatbot experiments. I have 24GB VRAM and mainly uses GGUF 7-30B model. I'm using the ASUS TUF 4090 which is considerably more bulky compared to a100. LLM was barely coherent. My goal was to find out which format and quant to focus on. , NVIDIA RTX 4090) without model parallel, checkpointing, or offloading strategies! With a Windows machine, the go-to is to run the models in VRAM - so the GPU is pretty much everything. hi all, what is the best model for writing? I have a 4090 with 24gb ram and 64gb ram. I've been trying to try different ones, and the speed of GPTQ models are pretty good since they're loaded on GPU, however I'm not sure which one would be the best option for what purpose. For example, LLM with 37B params or more even in 4bit quantization form don't fit in low-end card's The obvious budget pick is the Nvidia Tesla P40, which has 24gb of vram (but around a third of the CUDA cores of a 3090). Also the 33b (all the 30b labeled models) are going to require you use your mainboards hdmi out or ssh into your server headless so that the nvidia gpu is fully free. Im trying to buy a new card but torn between faster gpu vs higher vram. 3B Models work fast, 7B Models are slow but doable. Probably about 1/4 the speed at absolute best! Anything larger than 13B, would be a push! Best LLM to run locally . Although, a little note here — I read on Reddit that any Nous-Capy models work best with recalling context to up to 43k For LLMs, you absolutely need as much VRAM as possible to run/train/do basically everything with models. For the 34b, I suggest you choose Exllama 2 quants, 20b and 13b you can use other formats and they should still fit in the 24gb of VRAM. NVidia is rumored to launch 5090 with 36/48GB VRAM, it might be helpful to grow AI in this direction but still we definitely are limited by VRAM now. 0bpw exl2 quant with 43k context on my single 3090 with 24GB of VRAM using Ooba as my loader and SillyTavern as the front end. I've tested a lot of models, for different things a lot of times different base models but trained on same datasets, other times using opus, gpt4o, and Gemini pro as judges, or just using chat arena to compare stuff. LLM Comparison/Test: Ranking updated with 10 new models (the best 7Bs)! LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with **17** different instruct templates. GPT4-X-Vicuna-13B q4_0 and you could maybe offload like 10 layers (40 is whole model) to the GPU using the -ngl argument in llama. That’s by far the main bottleneck. I will be using koboldcpp on Windows 10. Just a heads up though, make sure you’re getting the original Mixtral. The unquantized Llama 3 8B model performed well for its size, making Get the Reddit app Scan this QR code to download the app now. I tried TheBloke's GPTQ and GGUF (4bit) versions. I rub 4 bit, no groupsize, and it fits in a 24GB vram with full 2048 context. I would recommend 7B right now, like Nyanade Stunna Maid or Kunoichi, at a low quant, maybe Q5 or Q4. Running with offload_per_layer = 6 It used 10GB VRAM + 2GB shared VRAM and 20GB RAM all within WSL2 Ubuntu on Windows 11. A used RTX 3090 with 24GB VRAM is usually recommended, since it's much cheaper than a 4090 and offers the same VRAM. 8b for using function calling. true. RTX A6000 won't be any faster than a 3090 provided that you can fit your model in 24GB of VRAM - they are both based on the same die (GA102, though the 3090 has a very minimally cut down version with 3% fewer CUDA cores). It can offer amazing generation speed even up to around ~30-50 t/s I have a 3090 with 24GB VRAM and 64GB RAM on the system. Hello, I wanted to weigh in here cause I see a number of prompts for good models for 8GB VRAM etc. I can run the 70b 3bit models at around 4 t/s. Kinda sorta. SD isn't really utilizing the vram unless I do like inpainting or more intensive upscaling. Still, with ~100GBs bandwidth, it should be able to manage 10 tokens/s from an 8-bit quantization of Llama3 8B. 3090 is king if you can find and afford it. I randomly made somehow 70B run with a variation of RAM/VRAM offloading but it run with 0. Best non-chatgpt experience. They go for about $700. 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 The 3090 has 24gb vram I believe so I reckon you may just about be able to fit a 4bit 33b model in VRAM with that card. Right now the most popular setup is buying a couple of 24gb 3090s and hooking them together, just for the VRAM, or getting a last-gen M series Mac because the processor has distributed VRAM. It would be quite fast and the quality would be the best for that small model. If unlimited budget/don't care about cost effectiveness than multi 4090 is fastest for scalable consumer stuff. 48 tokens a second with 32k context. You should be able to fit an 8B rope scaled to 16k context in your VRAM - I think a Q8 GGUF would be alright, at least this is what I checked for myself in HF's VRAM calculator. The various 70B models probably have higher quality output, but are painfully slow due to spilling into main memory. An Ada Lovelace A6000, 48GB VRAM, running on an AMD Threadripper with the appropriate board to support it. The best thing I can recommend is Stheno, llama 3 8b, Although it is quite small but it works well! In the end that's what matters right? I think you could run the unquantized version at 8k context totally on the gpu. Dark Theme . . 1 72B parameters, spitting out its first token, under LM Studio with a 50Gb Q6 quantized version. 70Bpw set of weights to help people squeeze every drop out of their VRAM. 1 Updated LLM Comparison/Test with new RP model: Rogue Rose 103B Best way to phrase the question would be to ask one question about the 48GB card. q4_K_M which is the quantization of the top model on LLM leaderboard. Realistically you should look for a higher than 8gb vram nvidia laptop, and then look for the 14 in m1 pro renewed on amazon, then last of all the 24gb m2 air on apples site (availability differs a ton by region). . The Air isn't as capable as the MBPs with the Max SoC that most people think of when they think about Mac laptops for AI. Best of Reddit; Topics; Content Policy; for a GPTQ model that has a good balance between speed and quality whilst being uncensored that can fully utilize 24gb of VRAM? If you have 32gb ram you can run platypus2-70b-instruct. The P40 is definitely my bottleneck. I've messed around with splitting the threads between RAM and CUDA but with no luck, I still get like . But if you try to work with modern LLM be ready to pay for VRAM to use them. 2GB of vram usage (with a bunch of stuff open in Well, lets start with maximum resolution, with double the VRAM you could render double the number of pixels, thats a sheer doubling that no other card, even a 4080 could meet, because its purely down to VRAM. 10GB VRAM (RTX3080) Resource need. I'm in the process of setting up a new rig with 24GB RAM & i5 Processor to power my LLM. The 48GB of vram vs 24 for one card allots the ability for 70B parameter models at That said, I was wondering: I would tend to proceed with the purchase of a NVIDIA 24GB VRAM. Install wsl Run the few pre-reqs needed for ooba (conda / apt install build-essentials) etc What is the highest performing self-hostable LLM that can be ran on a 24 GB VRAM GPU? This field is evolving so fast right now I haven't been able to keep up with all the models. 37it/s. Minimal comfortable vram for xl lora is 10 and preferable 16gb. It has 32k base context, though I mostly use it in 16k because I don't yet trust that it's coherent through the whole 32k. Best uncensored LLM for 12gb VRAM which doesn't need to be told anything at the start like you need to in dolphin-mixtral. I beg to differ. I know a recent post was made about a 3060 gpu, but since I have double that, I was wondering what models might be good for writing stories? The rtx3060 is probably a better choice, but the best value atm is a used eBay 3090 if you can stretch a little. I think the majority of "bleeding-edge" stuff is done on linux, and most applications target linux first, windows second. According to open leaderboard on HF, Vicuna 7B 1. if you're into local LLM( large language model) then 24gb vram is minimum, in this case a secondhand 3090 is the best value. cpp (which it seems to be configured on) loads, but is excruciatingly slow (like 0. The GGUF quantizations are a close second. I'm Hi! I've successfully fine tuned Llama3-8B using Unsloth locally, but when trying to fine tune Llama3-70B it gives me errors as it doesn't fit in 1 GPU. The latter being much faster since it fits completely in my 24GB VRAM while taking up about half the storage space. If I had that 2GB margin to put more context in, it would be much easier to finetune 34B/33B code models at 6-8k ctx with 24gb of VRAM, right now it kind of doesn't make sense as 2k ctx is too little for coding models where you go back and forward with the loading LoRAs (vs just merging then quantizing) takes up a bit more vram. Then, pick a quant of a model accordingly. I'm personally trying out the 4x7b and 2x7b models. 21 votes, 30 comments. It's just barely small enough to fit entirely into 24GB of VRAM, so performance is quite good. As far as I understand LLaMA 30b with the int4 quantization is the best model that can fit into 24 GB VRAM. Build a platform around the GPU(s) By platform I mean motherboard+CPU+RAM as these are pretty tightly coupled. Although second gpu is pretty useless for SD bigger vram can be useful - if you interested in training your own models you might need up to 24gb (for finetuning sdxl). From what I see you could run up to 33b parameter on 12GB of VRAM (if the listed size also means VRAM usage). Env: Intel 13900K, RTX 4090FE 24GB, DDR5 64GB 6000MTs Performance: 10~25 tokens/s Reason: Fits neatly in a 4090 and is great to chat with. Likes — The number of "likes" given to the model by users. 4090 is much more expensive than 3090 but it wouldn’t give you that more benefit when it comes to LLMs (at least regarding inference. Spent many hours trying to get Nous Hermes 13B to run well but it's still painfully slow and runs out of memory (just with trying to inference). 13b llama2 isnt very good, 20b is a lil better but has quirks. I'd say this combination is about the best you can do until you start getting into the server card market. It worked but I don’t have a frame of reference to know how well it worked compared to a single GPU with 24GB VRAM would work. Can send prompt/sampler settings that I use later if you want Reply reply Q5KM 11b models will fit into 16Gb VRAM with 8k context. LoRAs only work with the base model they are matched to, and they work best if they use the same instruct syntax. Although, instead of my medium model recommendation, it is probably better to use my small model recommendation, but at FP16, or with the full 128k context, or both if you have the vRAM! In that last case though, you probably have enough vRAM to run my large model recommendation at a decent quant, which does perform better (but slower). If I go 6bpw and have OOBE "manage V-ram" as per that option, it will often work with image generation as well, but it Please, help me find models that will happily use this amount of VRAM on my Quadro RTX 6000. And you will also have a nice gaming card. For short chats, though, Stheno is fine. Should I attempt llama3:70b? In fastchat I passed --load-8bit on the vicuna 13B v1. The issue is that bigger than 24GB means you have to go A6000 which costs as much as 4 3090s. I use LM Studio myself, so I can't help with exactly how to set that up yourself with your front end, but I imagine it would be similar. NVIDIA is currently WAAAAY ahead of everyone else in the software-support department. Cheapest Macbook M1/2/3 I see on Amazon with the equivalent of 24GB VRAM is about 2K, Mac Mini looks to be about a hundred less. Worth setting up if you have a dataset :) However, now that Nvidia TensorRT-LLM has been released with even more optimizations on Ada (RTX 4xxx) it’s likely to handily surpass even these numbers. I posted my latest LLM Comparison/Test just yesterday, but here's another (shorter) comparison/benchmark I did while working on that - testing different formats and quantization levels. But I'd be surprised if anything below a 24G 3090TI is going to do the job. 1 T/S - LLaMA2-13B-Tiefighter and MythoMax-L2-13b for when you need some VRAM for other stuff. A Curated List of the Large and Small Language Models (Open-Source LLMs and SLMs). 48GB VRAM on a single card won't go out of style anytime soon and the Threadripper can handle you slotting in more cards as needed. I've got a 32gb system with a 24gb 3090 and can run the q5 quant of Miqu with 36/80 layers running on VRAM and the rest in RAM with 6k context. I wanted to do this benchmark before configuring Arch Linux. Hope this helps Right now, 24GB VRAM would suffice for my needs, so one 4090 is decent, but since I cannot just buy another 4090 and train a "larger" LLM that needs 48GB, what would my future options be? You can either get a GPU with lot of VRAM, and/or 3090s/A6000 and use NVLink (48GB for 3090 since I think it supports just 2-way SLI), or multiple A6000 (I Takes about ~6-8GB RAM depending on context length. For coding overall, your best bet would be using base deepseek-coder 33b or DeepSeek-Coder-instruct 33b. M-series chips obviously don't have VRAM, they just have normal RAM. What should I be doing with my 24GB VRAM? I LOVE midnight-miqu-70b-v1. Having said that: sometimes it will remind you is a 7B model, with random mistakes, although easy to edit. 1 and it loaded on a 4090 using 13776MiB / 24564MiB of vram. For example, my card only has 20GB of VRAM, so any usable quantization of a 70B model will be at least half in system RAM, and half (or less) in VRAM. - another threshold is 12GB VRAM for 13B LLM (but 16GB VRAM for 13B with extended context is also noteworthy), and - 8GB for 7B. All bin or safetensor models I tried consume 3-4 times param size's VRAM, e. 24GB is the most vRAM you'll get on a single consumer GPU, so the P40 matches that, and presumably at a fraction of the cost of a 3090 or 4090, but there are still a number of open source models that won't fit there unless you shrink them considerably. Hi everyone, I'm relatively new to the world of LLMs and am seeking some advice on what might be the best fit for my setup. 10 is released deployment should be relatively straightforward (yet still much more complex than just about GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection - Meta AI 2024 - Allows pre-training a 7B model on consumer GPUs with 24GB memory (e. You are going to need all the 24gb of vram to handle the 30b training. I was hoping with more vram I could run jobs in parallel but web automatic1111 at least doesn't seem to let you do that and instead just queues them up. I am considering two budget graphics cards. Not Brainstorming ideas, but writing better dialogues and descriptions for fictional stories. 5 32b was unfortunately pretty middling, despite how much I wanted to like it. LLM Comparison/Test: Mixtral-8x7B, Mistral, DeciLM, Synthia-MoE Winner: Mixtral-8x7B-Instruct-v0. It takes vram even to run mozilla or the smallest window manager xfce. 7B models cannot fit in RTX 4090 VRAM (24GB) Minstral 7B works fine on inference on 24GB RAM (on my NVIDIA rtx3090). In addition some data needs to be stored on all cards increasing memory usage. these models all have their strengths and weaknesses, and falcon is no slouch if you find a fine tuning task that suits it. I've been really interested in fine tuning a language model, but I have a 3060Ti (8GB). This card can be found on ebay for less than $250. 3090 2nd hand should be sub $800 and for llm specific use I'd rather have 2x3090s@48gb vram vs 24gb vram with more cuda power with 4090s. I'd gladly fork over stupid money for a 4090 just with more ram at a more appropriate price. Otherwise 3060 is fine for smaller types of model, avoid 8gb cards, 4060ti 16gb is a great card despite being overpriced imo. Reply reply More replies If you have that much VRAM you should probably be thinking about running exllamav2 instead of llama. Miqu is the best. It surprised me how great this model works. I have a dual 3090 setup and can run an EXL2 Command R+ quant totally on VRAM and get 15 tokens a second. I recently removed the GPU because some other software I use is written in a way that it was choosing the 3060 for rendering and was causing memory errors when the 4070ti is the primary GPU. Members Online. Speed. LoneStriker has started uploading a few 70B exl2 quants using this new quant method to Hugging Face if you want to try it out for yourself. Prompt is a simple: "What is the meaning of life?" Did you check if you maybe suffer from the VRAM swapping some recent nvidia-drivers introduced? New drivers start to swap VRAM if it gets too The fact is, as hyped up as we may get about these small (but noteworthy) local LLM news here, most people won't be bothering to pay for expensive GPUs just to toy around with a virtual goldfish++ running on their PCs. If you take the most of the key specs of the 4090 notebook and then find the same components for a desktop build (remembering to add psu, case and monitor if your boss really doesn't have one spare) then you'll likely find it's The AI landscape has been moving fast and at this point, I can barely keep track of all the various models, so I figured I'd ask. 8x7B: Nous Hermes 2 Mixtral 8x7B DPO. Kobold is probably the best open-source means for a newcomer to get into AI. It's a pretty strong choice, almost as good as the R+ version, but I can fit it in 24GB of VRAM, and it doesn't typically do stuff like start a story with "Once upon a time" or other cliches I see a lot in other models. Interested in this because I want to know if I'm stuck with 7B model unless I use GGUF. As title says, i'm planning to build a server build for localLLM. If you are using it for programming it could surprise you how much better it becomes. What I managed so far: Found instructions to make 70B run on VRAM only with a 2. I split models between a 24GB P40, a 12GB 3080ti, and a Xeon Gold 6148 (96GB system ram). As far as i can tell it would be able to run the biggest open source models currently available. I also saw some having luck on 30B compressed on 24GB vram. And you will have plenty of VRAM left for extras like Stable Diffusion, talkinghead (animated characters), vector database (long-term memory), etc. I am not sure a 70b would be a good experience on 24GB VRAM, but starting on 32GB and over 3bpw becomes OK. Question | Help Hi, new here Will that fit on 24gb vram? Reply reply More replies. It was pre-trained on around 1. 8 trillion tokens, which i guess is somewhere along the lines of 200 billion lines of code. Once the capabilities of the best new/upcoming 65B models are trickled down into the applications that can perfectly make do with <=6 GB VRAM cards/SoCs, You give it some extra context (16K), and with it, it will easily fill 20-22 GB of VRAM. Is it equivalent anyway? Would a 32gb RAM Macbook Pro be able to properly run a 4b-quantised 70b model seeing as 24gb VRAM 4090s are able to? 27 votes, 56 comments. My not so technical steps assuming your on windows. The LLM climate is changing so quickly but I'm looking for suggestions for RP quality LLMs for 24GB VRAM: Large Language Models (Open-Source LLMs) Fit in 24GB VRAM with Dynamic Sorting and Filtering. They also support NVLink (some cards don't so check before you buy), so you could bridge them to use all 48GB as one compute node for training. Some people swear by them for writing and roleplay but I don't see it. - 128GB RAM (@4800) - single 3090 with 24GB VRAM. /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. I wanted to ask which is the best open source LLM which I can run on my PC? Is it better to run a Q3 quantized mistral 8X7B model (20Gb) or is it better to use mistral-7B model(16gb) which is LLM hardware acceleration—on a Raspberry Pi (Top-end AMD GPU using a low cost Pi as it's base computer) I have recently built a full new PC with 64GB Ram, 24GB VRAM, and R9-7900xd3 CPU. r/LocalLLaMA. Subreddit to I need a Local LLM for creative writing. Mistral 7B is running at about 30-40 t/s I have a few questions regarding the best hardware choices and would appreciate any comments: GPU: From what I've read, VRAM is the most important. A desktop build with a real 4090 24GB vram, higher CUDA cores and actually adequate cooling would be much better. In my testing, so far, both are good for code, but 34b models are better in describing the code and understanding lonf form instructions. Right now it seems we are once again on the cusp of another round of LLM size upgrades. On theory, 10x 1080 ti should net me 35,840 CUDA and 110 GB VRAM while 1x 4090 sits at 16,000+ CUDA and 24GB VRAM. I've tried the model from there and they're on point: it's the best model I've used so far. But is worthy for how quick it works on a 24GB card, and how polished it is. It's really a weird time where the best releases tend to be 70B and too large for 24GB VRAM or 7B and can run on anything. Llama3-8b is good but often mixes up with multiple tool calls. I can fit 32,768 context in about 48 GB of memory, and scale it up or down depending on the situation. Thanks for answer tho, didn't expect P40 to be so weak against 3060. Say your system has 24gb VRAM and 32gb ram you could even very slowly run 70B. 5bpw, and even a 3. 0. ( eg: Converting bullet points into story passages). So, regarding VRAM and quant models - 24GB VRAM is an important threshold since it opens up 33B 4bit quant models to run in VRAM. I'd probably try renting an A100 VM, running some experiments, and measuring VRAM and RAM usage. Btw, try running 8b at 16bits using transformers. The P40 offers slightly more VRAM (24gb vs 16gb), but is GDDR5 vs HBM2 in the P100, meaning it has far lower bandwidth, which I believe is important for inferencing. GGML, if you split it between VRAM and RAM, you could definitely run a 13B model, but Id expect it to be quite slow. 2. The RTX 4090 mobile GPU is currently Nvidia's highest tier mobile GPU, with 16 GB VRAM, based off the 16 GB VRAM RTX 4080 desktop card. You can already purchase an AMD card, Intel card, or Apple SOC with Metal support and inference LLM's today on them. cpp? I tried running this on my machine (which, admittedly has a 12700K and 3080 Ti) with 10 layers offloaded and only 2 threads to try and get something similar-ish to your setup, and it peaked at 4. But in order to want to fine tune the un quantized model how much Gpu memory will I need? 48gb or 72gb or 96gb? does anyone have a code or a YouTube video tutorial to I'm considering purchasing a more powerful machine to work with LLMs locally. Running on a 3090 and this model hammers hardware, eating up nearly Here is my benchmark of various models on following setup: - i7 13700KF. Their performance is not great. And is surprisingly powerful. 4: You can do most things on both Linux and Windows, although yes I believe Linux can be preferable. I am working on a little personal model that creates smart summaries of text and I consistently get dramatically more coherent and hallucination-free output from falcon-7B than I get from LLaMA-7B, MPT-7B, or StableLM-7B. The only thing I setup is "use 8bit cache" because I test it on my desktop and some VRAM is used by the desktop. VRAM of a GPU cannot be upgraded. Context length: 4k Nothing else changed. 07t/sec). I haven't tried 8x7b yet, since I don't want to run anything on cpu because it's too slow for my taste, but 4x7b and 2x7b seem pretty nice to me. now I use For Local LLM use, what is the current best 20b and 70b EXL2 model for a single 24GB (4090) Windows system using ooba/ST for RPG / character interaction purposes, as leat that you have found so far? That seems to work fine. But it's for RP, Mythalion-13B is better at staying in character. ggmlv3. But it's the best 70b you'll ever use; the difference between Miqu 70b and Llama2 70b is like the difference between Mistral 7b and Llama 7b. They're both solid 13b models that still perform well and are really fast. Best MDM for Apple upvotes You don't need to pass data between the cards and you actually get more VRAM. Works on my server PCs and my primary PC (16GB RAM, 4GB VRAM). I was wondering if it would be good for my purpose, and eventually which one to choose between this one and this one (mainly from Amazon, but if anyone, especially Italian fellow, knows of any cheaper and safe website, obviously it is welcome. You can't utilize all the VRAM due to memory fragmentation and having your VRAM split across two cards exacerbates this. However, most of models I found seem to target less then 12gb of Vram, but I have an RTX The 24GB version of this card is without question the absolute best choice for local LLM inference and LoRA training if you only have the money to spare. The 3090 may actually be faster on certain workloads due to having ~20% higher memory bandwidth. But since you'll be working with a 40GB model with a 3bit or lower quant, you'll be 75% on the CPU RAM, which will likely be really slow. LLM E X PLORER. And for some reason the whole LLM community has never agreed on a instruct syntax, and many trainers just make up their own For as much as VRAM is king is true. If you can fit the EXL2 quantizations into VRAM, they provide the best overall performance in terms of both speed and quality. I have a 4090, it rips in inference, but is heavily limited by having only 24 GB of VRAM, you cant even run the 33B model at 16k context, let alone 70B. g. I'm running the 4. To compare I have a measly 8GB VRAM and using the smaller 7B wizardlm model I fly along at 20 tokens per second as it’s all on the card. As for what exact models it you could use any coder model with python in name so like Phind-CodeLlama or Best LLM(s) For RP . You'd ideally want more VRAM. q5_k_s but have found it's very slow on my system. LLM E X PLORER For those working with advanced models and high-precision data, 24GB VRAM cards like the RX 7900 XTX are the best bet, and with the right setup and enough money, you I’m considering the RTX 3060 12 GB (around 290€) and the Tesla M40/K80 (24 GB, priced around 220€), though I know the Tesla cards lack tensor cores, making FP16 After using GPT4 for quite some time, I recently started to run LLM locally to see what's new. With my 24gb of VRAM and DDR4 RAM, I get about 0. I have Nvidia 3090 (24gb vRAM) on my PC and I want to implement function calling with ollama as building applications with ollama is easier when using Langchain. I accept no other answer lol. And then a seperate question about the 24GB card. There’s a bit of “it depends” in the answer, but as of a few days ago, I’m using gpt-x-llama-30b for most thjngs. I appreciate multilingual model and uncensored. VRAM is a limit of model quality you can run, not speed. With GGUF models you can load layers onto CPU RAM and VRAM both. If you get interested in LLM's you can run twice as many parameters, i can get about 7 billion params in 12GB, you could get 14 billion If you run through oobabooga it will most likely automatically work with gguf models. The Tesla P40 and P100 are both within my prince range. nous-capybara-34b I haven't been able to use that with my 3090Ti yet. A reddit dedicated to the profession of Computer System Administration. That was on a 4090, and I believe (at the time at least) 24GB VRAM was basically a requirement. It could fit into 24gb of vram and there's even way to fit it to 12gb apparently, but I don't know how accurate they are at lower quants. In the RP use community you are likely going to run into alot more people with consumer 3090/4090 cards (also 24GB) than the professional grade A I'm currently running a 3B LLM models on my laptop with 12GB RAM & an i5 processor using Kobolt, but it's painfully slow. (default is the LLM Explorer Score). and remembers things, which is, always so damn satisfying to see, ha ha. Probably best to stick to 4k context on these. Best you can do is 16 GB VRAM and for most high end RTX 4090 175W laptops, you can upgrade the ram to 64 GB yourself after buying the laptop. Qwen 1. I run Local LLM on a laptop with 24GB RAM & no GPU. However, the 1080Tis only have about 11GBPS of memory As a bonus, Linux by itself easily gives you something like 10-30% performance boost for LLMs, and on top of that, running headless Linux completely frees up the entire VRAM so you can have it all for your LLM in its entirety, which is impossible in Windows because Windows itself reserves part of the VRAM just to render the desktop. Quality With 24GB you could run 33B models which are bigger and smarter. I haven’t gotten around to trying it yet but once Triton Inference Server 2023. uipukb hwjxf jspc zagwmh fpgw mih qng otesdfi xhlbwj dshg