Best llm for 24gb vram reddit. No LLM model is particularly good at fiction.
Best llm for 24gb vram reddit 4-mixtral-instruct-8x7b-zloss. But at this point in time, I mainly use it for correcting repeats, as the other models are just better at RP/Chat/NSFW. 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. Yes, and you can get 24GB in the 40XX Series as well. They did this to generate buzz. And then a seperate question about the 24GB card. Posted by u/yupignome - 1 vote and no comments 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. What models would be doable with this hardware?: CPU: AMD Ryzen 7 3700X 8-Core, 3600 MhzRAM: 32 GB GPUs: NVIDIA GeForce RTX 2070 8GB VRAM NVIDIA Tesla M40 24GB VRAM Although, a little note here — I read on Reddit that any Nous-Capy models work best with recalling context to up to 43k and it seems to be the case for this merge too. I am taking a break at this point, although I might fire up the engines again when the new WizardLM 70B model releases. A high temp of like 1. I run Local LLM on a laptop with 24GB RAM & no GPU. Faster than Apple, fewer headaches than Apple. . They're both solid 13b models that still perform well and are really fast. As far as I understand LLaMA 30b with the int4 quantization is the best model that can fit into 24 GB VRAM. Context length: 4k Nothing else changed. 72 -c 2048 --top_k 0 --top_p 0. 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). I'm r/LocalLlaMa, r/Oobabooga, and r/KoboldAI would be good starter points. My primary use case, in very simplified form, is to take in large amounts of web-based text (>10 7 pages at a time) as input, have the LLM "read" these documents, and then (1) index these based on word vectors and (2) condense each document So far I've not felt limited by the Thunderbolt transfer rate, at least if the full models fits in VRAM I guess. NVIDIA is currently WAAAAY ahead of everyone else in the software-support department. Anything lower and there is simply no point, you'll still struggle. I know the 3090 is the best VRAM for buck you can get, but I'd rather stick to Ada for a number of different reasons. For some people, the extra VRAM of the 3090 might be worth the $300 increase for a new 3090 over the 4070. So you could have an RTX 4090 and a 3060. 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 Remember GPU VRAM is king, and unless you have a very good cpu, threadripper or MAC system and good, fast ram, cpu inference is very slow. A Curated List of the Large and Small Language Models (Open-Source LLMs and SLMs). I'm planning to build a server focused on machine learning, inferencing, and LLM chatbot experiments. I have a dual 3090 setup and can run an EXL2 Command R+ quant totally on VRAM and get 15 tokens a second. Is there any other model that comes close to that model in terms of quality whilst also being able to fit on 24GB VRAM? 322 votes, 124 comments. A cheaper, but still top tier card is the 3090 for $900. I wouldn't go for a 24GB vram card just yet. But go over that, to 30B models, they don't fit in nvidia s VRAM, so apple Max series takes the lead. $6k for 140 Gb VRAM is the best value on the market no question. Please get something with atleast 6gb of vram to run 7b models quantized. The various 70B models probably have higher quality output, but are painfully slow due to spilling into main memory. 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. 1. A reddit dedicated to the profession of Computer System Administration. 1 and it loaded on a 4090 using 13776MiB / 24564MiB of vram. L3 based 8B models can be really good, I'd recommend Stheno. Dolphin is a very good llm but it's also pretty heavy. Right now it seems we are once again on the cusp of another round of LLM size upgrades. 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 <--- Best LLM to run locally . 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? For the VRAM question specifically, try to get a card with 16 GB of VRAM for 1440p gaming. , that highly depends on what you exactly do and how complex the task is. 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 Best uncensored LLM for 12gb VRAM which doesn't need to be told anything at the start like you need to in dolphin-mixtral. They're more descriptive sure, but somehow they're even worse for writing. We are Reddit's primary hub for all things modding, 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. Good (NSFW) RPG model for RTX 3090 24gb VRAM I've been using noromaid-v0. For Local LLM use, what is the current best 20b and 70b EXL2 model for a single 24GB If you want to run 70B on 24GB VRAM I'd strongly suggest using GGUF & partial offloading instead of trying to full offload a really low quant. You can't utilize all the VRAM due to memory fragmentation and having your VRAM split across two cards exacerbates this. Discussion The tradition must IceCoffeeRP, or RPStew for a bigger model (200K context possible, but 24GB of VRAM means I'm around 40K without going over). 5 bpw that run fast but the perplexity was unbearable. Subreddit to 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. Should I attempt llama3:70b? 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. Those claiming otherwise have low expectations. During my research, I came across the RTX 4500 ADA, priced at approximately £2,519, featuring 24GB of vRAM and 7680 CUDA cores. I've tried the model from there and they're on point: it's the best model I've used so far. 24GB VRAM is plenty for games for years to come but it's already quite limiting for LLM's. 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. Outperforming ultra large models like Gopher (280B) or GPT-3 (175B) there is hope for working with < 70B parameters without needing a super computer. 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. The 3090 may actually be faster on certain workloads due to having ~20% higher memory bandwidth. 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. Q3_K_M but it forgets what happened 20 messages ago. 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. That’s by far the main bottleneck. For short chats, though This subreddit has been temporarily closed in protest of Reddit's attempt to kill third-party apps through abusive API changes 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. The M3 Pro maxes out at 36 gb of RAM, and that extra 4 gb may end up significant if you want to use it for running LLMs. If you could get a 2nd card or replace that card with something with 16 or 24GB of VRAM that'd be even better. If you're looking to go all-out, a pair of 4090s will give you the same VRAM plus best-in-class compute power while still costing less than a single used A6000 with the equivalent memory. The GGUF quantizations are a close second. In my testing, so far, both are good for code, but 34b models are better in describing the code and understanding lonf form instructions. Then Anthropic put draconian "wrong think" filters on it while using the tired old trope of "We're protecting you from the evil AI!" As such, those filters and lowered resources caused Claude2 and Claude3 to write as poorly as ChatGPT. The best GPU for Stable Diffusion would depend on your budget. I'm using the ASUS TUF 4090 which is considerably more bulky compared to a100. 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. The unquantized Llama 3 8B model performed well for its size, making With a Windows machine, the go-to is to run the models in VRAM - so the GPU is pretty much everything. If you find you’re still a little tight in VRAM, that same HF account has a 3. Best MDM for Apple upvotes 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. Having said that: sometimes it will remind you is a 7B model, with random mistakes, although easy to edit. 20token/s is a good speed and 40tokens/sec is I don’t know, since my 2x24GB vram are not enough to run the q5. It's about having a private 100% local system that can run powerful LLMs. 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. I've messed around with splitting the threads between RAM and CUDA but with no luck, I still get like . What is your best guide to train LLM from your customised dataset? upvotes r/LocalLLaMA. 1 -n 500) but of course, ymmv. Note that this doesn't include processing, and it So, regarding VRAM and quant models - 24GB VRAM is an important threshold since it opens up 33B 4bit quant models to run in VRAM. now I use 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. Welcome to /r/AMD — the subreddit for all things AMD; come talk about Ryzen, Radeon For instance, if you are using an llm to write fiction, quantize on your two favorite books. 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 The good: Famous said they run 6B on one card. Seems GPT-J and GPT-Neo are out of reach for me because of RAM / VRAM requirements. Please, help me find models that will happily use this amount of VRAM on my Quadro RTX 6000. It fully goes into VRAM on Ooba with default settings and gets me around 10TPS. Quality 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're paging because a 13B in f16 will not fit in 24GB of VRAM. With a ton of System RAM you can use a lot of that for big GGUF files, but that VRAM is really holding you back. You might not need it now but you will in the future. Worth setting up if you have a dataset :) A Lenovo Legion 7i, with RTX 4090 (16GB VRAM), 32GB RAM. It's just barely small enough to fit entirely into 24GB of VRAM, so performance is quite good. So why the M40? It's $6 per GB of VRAM. 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. If I upscale to 4k resolution without some kind of sd upscaler or other tile method, I'm looking at 36-40 gigs of vram used (where it starts using regular ram too). If you are generating python, quantize on a bunch of python. But I can say that the iq2xs is surprisingly good and so far the best llm I can run with 24gb. The LLM climate is changing so quickly but I'm looking for suggestions for RP quality The VRAM calculations are estimates based on best known values, VRAM usage can change depending on Quant Size, Batch Size, KV Cache, BPW and other hardware specific metrics. It seems that most people are saying you really don't need that much vram and it doesn't always equal to higher performance. Though I personally prefer lzlv, here are some other good models: hi all, what is the best model for writing? I have a 4090 with 24gb ram and 64gb ram. And before you say 3090, I don't wanna deal with buying used, or the power consumption and transient spikes of Ampere, and I've noticed lackluster performance on some of these other AI architectures with its older core as well. 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, With 24GB you could run 33B models which are bigger and smarter. Maybe suggest some 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 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 Your personal setups: What laptops or desktops are you using for coding, testing, and general LLM work? Have you found any particular hardware configurations (CPU, RAM, 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 LLMs for 24GB VRAM: Large Language Models (Open-Source LLMs) Fit in 24GB VRAM with Dynamic Sorting and Filtering. I have heard that KoboldCPP and some other interfaces can allow two GPUs to pool their VRAM. 13b llama2 isnt very good, 20b is a lil better but has quirks. Yes, it's two generations old, but it's discounted. 12GB vram is a great start so the new 4070 RTX is great. 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. However, it does enable you to load half the weights on one card and the rest on the other. Since fits all in vram is quite fast. LLM List LLM Hosting LLM Leaderboards Blog Newsfeed Advertise. These are only estimates and come with no warranty or guarantees. Compared to q4xs it doesn’t feel like iq2 is dumber but more like „playful“ or something? Q4 feels more professional and straightforward, but also more gptsim. However, it seems games are using higher and higher vram nowadays, so would 10gb be sufficient for future games in the coming 4-5 years? How feasible is it to use an Apple Silicon M2 Max, which has about 96 GB unified memory for "large model" deep learning? I'm inspired by the the Chinchilla paper that shows a lot of promise at 70B parameters. Q4_K_M. As for VRAM impact on 3D rendering etc. I've been using Open Hermes 2. 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 This VRAM calculator helps you figure out the required memory to run an LLM, given . Just a heads up though, make sure you’re getting the original Mixtral. That is changing fast. what about for 24gb vram That's your Noromaid-0. It's tricky. To maintain good speeds, I always try to keep chats context max around 2000 context to get sweet spot of High memory as well as maintaining good speeds Even with just 2K content, my 13B chatbot is able to remember pretty much everything, thanks to vector database, it's kinda fuzzy but I applied all kinds of crazy tricks to make it work every You don't need to pass data between the cards and you actually get more VRAM. Has "8 bit cache" option which allows you to save some VRAM, recently added "4 bit cahce" so we need to wait until this would be in ooba Exl2 recomendations: VRAM is a limit of model quality you can run, not speed. In addition some data needs to be stored on all cards increasing memory usage. Not for finetuning or anything else like that though, you want CUDA. Top Picks for 24GB VRAM I've been testing WizardLM-2 8x22B via openrouter and found the output to be incredible, even with little to no adjustments. 8M subscribers in the Amd community. 5 as my general purpose AI since it dropped and I've been very happy with it. The caveats: Getting a lot of slow gpus with high vram will probably be slower. 27 votes, 56 comments. You can already purchase an AMD card, Intel card, or Apple SOC with Metal support and inference LLM's today on them. Also, with cards like "The Desk", it's good at maintaining formatting and following your lead. No they don't stack as NVLINK is not the same as it was in the past (at least it doesn't show up as one massive 48GB card for my 2x3090's). It was still great for gaming, but the 24GB VRAM was a nice addition for those who wanted to get into stuff like Blender or semi-pros in general, as Nvidia seems to be the de facto standard. I'm personally trying out the 4x7b and 2x7b models. LLM E X PLORER I have recently built a full new PC with 64GB Ram, 24GB VRAM, and R9-7900xd3 CPU. EXL2 (GPU only, much faster than the above): download 2. It can offer amazing generation speed even up to around ~30-50 t/s 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'd recommend atleast 12gb for 13b models. No LLM model is particularly good at fiction. Some people swear by them for writing and roleplay but I don't see it. The only thing I setup is "use 8bit cache" because I test it on my desktop and some VRAM is used by the desktop. But if you try to work with modern LLM be ready to pay for VRAM to use them. Likes — The number of "likes" given to the model by users. I've been having good luck with Nous-Capybara-limarpv3-34B using the Q4_K_M quantization in KoboldCPP. M-series chips obviously don't have VRAM, they just have normal RAM. 3090 is For LLMs, you absolutely need as much VRAM as possible to run/train/do basically everything with models. Just make sure to turn off CUDA System Fallback policy in your drivers, so you'll know when the model is too big for your GPU. 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. 70Bpw set of weights to help people squeeze every drop out of their VRAM. Best non-chatgpt experience. 1 GPTQ 4bit runs well and fast, but some GGML models with 13B 4bit/5bit quantization are also good. 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. I use LM Studio myself, so I can't help with exactly how to set that up yourself with your The Mac Studio has embedded RAM which can act as VRAM; the M1 Ultra has up to 128GB (97GB of which can be used as VRAM) and the M2 Ultra has up to 192GB (147GB of which can be used as VRAM). q5_k_s but have found it's very slow on my system. 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. I also saw some having luck on 30B compressed on 24GB vram. GGUF [ "continue the story/episode" was good but not as good as Wizard 13b] 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. Build a platform around the GPU(s) By platform I mean motherboard+CPU+RAM as these are pretty tightly coupled. 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. LLM was barely coherent. 0. Mistral 7B is running at about 30-40 t/s LLMs for 24GB VRAM: Large Language Models (Open-Source LLMs) Fit in 24GB VRAM with Dynamic Sorting and Filtering. 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? 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. I think the majority of "bleeding-edge" stuff is done on linux, and most applications target linux first, windows second. A used RTX 3090 with 24GB VRAM is usually recommended, since it's much cheaper than a 4090 and offers the same VRAM. I started with r/LocalLLaMA. I have a 3090 with 24GB VRAM and 64GB RAM on the system. 6BPW. Hope this helps I need a Local LLM for creative writing. It takes vram even to run mozilla or the smallest window manager xfce. I just bought a new 3090Ti with 24GB VRAM for 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). As for what exact models it you could use any coder model with python in name so like Phind-CodeLlama or WizardCoder-Python For 7B/13B models 12GB VRAM nvidia GPU is your best bet. 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. I wonder how well does 7940hs seeing as LPDDR5 versions should have 100GB/s bandwidth or more and compete well against Apple m1/m2/m3. Or, at the very least, match the chat syntax to some of the quantization data. As far as i can tell it would be able to run the biggest open source models currently available. ( eg: Converting bullet points into story passages). 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'd probably build an AM5 based system and get a used 3090 because they are For as much as VRAM is king is true. 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. Otherwise 3060 is fine for smaller types of model, avoid 8gb cards, 4060ti 16gb is a great card despite being overpriced imo. It's very good in that role. Now your running 66B models. That was on a 4090, and I believe (at the time at least) 24GB VRAM was basically a requirement. Minstral 7B works fine on inference on 24GB RAM (on my NVIDIA rtx3090). This reddit is going to be more of a finetuning your settings after you can get the model up and running. I was really tempted to get the 3090 instead of the 3080 because of the 24gb vram. You'd ideally want more VRAM. If u dont mind to wait for the responses (split the model in gpu and cpu) u can also try 8x7b models. The human one, when written by a skilled author, feels like the characters are alive and has them do stuff that feels to the reader, unpredictable yet inevitable once you've read the story. 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. Best way to phrase the question would be to ask one question about the 48GB card. Also, since you have so much VRAM, you can try 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. 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. LLM Comparison/Test: Mixtral-8x7B, Mistral, DeciLM, Synthia-MoE Winner: Mixtral-8x7B-Instruct-v0. It's about as fast as my natural reading speed, so way faster than 1T/s. . Q5KM 11b models will fit into 16Gb VRAM with 8k context. Mac Studios with maxed out RAM offer the best bang for buck if you want to run locally. - LLaMA2-13B-Tiefighter and MythoMax-L2-13b for when you need some VRAM for other stuff. MacBook Pro M1 at steep discount, with 64GB Unified memory. I definitely think pruning and quantizing can get it to something that runs on 48GB VRAM. 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. Find a "slow" part of your RP, plug in beagle, correct the repeating behavior, and you're good to go. I can run the 65b 4-bit quantized model of LLaMA right now but Loras / open chat models are limited. Fimbulvetr-v2 or Kaiju are my usual recommendations at that size, but there are other good ones. 4-Mixtral-8x7B-ZLoss, although BagelMisteryTour 8x7B is pretty good too. I appreciate multilingual model and uncensored. With that if you want SDXL as well, you would easily be needing over 100GB VRAM for best use. 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 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). 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. I have tried llama3-8b and phi3-3. Yeah it's pretty good, for LLM inference - if you're just doing inference it's hard to beat considering what you're getting for the money (screen, mobility, great battery life). 70b+: Llama-3 70b, and it's not close. 5, but none of them managed to get there, and at this point I feel like I won't get there without leveraging some new ingredients. Their performance is not great. It would be quite fast and the quality would be the best for that small model. Dark Theme . GGUF models - can use both RAM and VRAM, lower generation speed Exl2 models - very fast generation speed(if the model fits), longer context window for the same hardware, GPU only. Renting power can be not that private but it's still better than handing out the entire prompt to OpenAI. In fastchat I passed --load-8bit on the vicuna 13B v1. - another threshold is 12GB VRAM for 13B LLM (but 16GB VRAM for 13B with extended context is also noteworthy), and - 8GB for 7B. It can take some time. Speculative decoding will possibly easily boost 3x CPU inference speeds with a good combo of small draft model + big model. Noromaid its a good one. VRAM of a GPU cannot be upgraded. Just compare a good human written story with the LLM output. Question | Help Hi, new here Will that fit on 24gb vram? Reply reply More replies. Probably best to stick to 4k context on these. 1 7B, WizardLM 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. I'm looking for something more subtantial. Punches way above it's weight so even bigger local models are no better. It's a frontend similar to chatgpt, but with the possibility to download several models (some of them are extremely heavy other are more light). But it's for RP, Mythalion-13B is better at staying in character. 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 You give it some extra context (16K), and with it, it will easily fill 20-22 GB of VRAM. I mean for those prices you can upgrade every generation and still have money leftover. You are going to need all the 24gb of vram to handle the 30b training. Speed. Llama3-8b is good but often mixes up with multiple tool calls. 10GB VRAM (RTX3080) Resource need. Hello, I wanted to weigh in here cause I see a number of prompts for good models for 8GB VRAM etc. 5bpw, and even a 3. I've been exploring locally run LLMs recently (as a completely non-technical novice) and I'm looking for ways to expand VRAM capacity to load larger models without the need to substantially reconfigure my existing set up (4090 + 7950x3d + 64gb RAM). If you want something good for gaming and other uses, a pair of 3090s will give you the same capability for an extra grand. Running on a 3090 and this model hammers hardware, eating up nearly My own goal is to use LLM for what they are best for - a task of an editor, not the writer. 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. I've got a top of the line (smacks top of car) 4090 setup and even 24 gigs gets chewed through almost instantly. I am building a PC for deep learning. Wonder which model of it they are running and how it'd compare to an exl2 for 24GB 3090. Not Brainstorming ideas, but writing better dialogues and descriptions for fictional stories. And if it turns out the 12GB is insufficient, well 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). 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. 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 I'm considering purchasing a more powerful machine to work with LLMs locally. Yeah ngl, this model's subject matter is within striking range of 90% of the conversations I have with AI anyways. 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? Hopefully this quick guide can help people figure out what's good now because of how damn fast local llms move, and finetuners figure what models might be good to try training on. I thought it was a generally accepted concession that the 24GB VRAM was absolutely overkill on the 3090 when it came to gaming. If you want to try some local llm, you can try to host a docker of Serge (you can find it on GitHub). And you will have plenty of VRAM left for extras like Stable Diffusion, talkinghead (animated characters), vector database (long-term memory), etc. 3B Models work fast, 7B Models are slow but doable. LLM can fix sentences, rewrite, fix grammar. Same amount of vram and ram. I would like to train/fine-tune ASR, LLM, TTS, stable diffusion, etc deep learning models. 4GB VRAM, Core i7) - what is best for each? Reply reply What’s the best wow-your-boss Local LLM use case demo you’ve ever presented? upvotes With GGUF models you can load layers onto CPU RAM and VRAM both. All these tools should run on the same machine, competing for resources, so you can't just have the LLM run at 5token/sec for example and THEN feed it to another tool and Then another tool etc. With a Windows machine, the go-to is to run the models in VRAM - so the GPU is pretty much everything. Just get an upgrade that's good. I want to run a 70B LLM locally with more than 1 T/s. for the price of running 6B on the 40 series (1600 ish bucks) You should be able to purchase 11 M40's thats 264 GB of VRAM. I am still trying out prompts to make it more consistent. It’s a simple matter of typing what vram split you want into an option field in the webui. (default is the LLM Explorer Score). Hi, I am trying to build a machine to run a self-hosted copy of LLaMA 2 70B for a web search / indexing project I'm working on. Qwen2 came out recently but it's still not as good. If you have the budget, get something with more VRAM. (I'm somewhat of a LLM noob, so maybe it's not feasible) Suggestions for a > 24GB VRAM build? LLM Recommendations: Given the need for a smooth operation within my VRAM limits, which LLMs are best suited for creative content generation on my hardware? 4-bit Quantization Challenges: What are the main challenges I might face using 4-bit quantization for an LLM, particularly regarding performance or model tuning? Minstral 7B works fine on inference on 24GB RAM (on my NVIDIA rtx3090). The Tesla P40 and P100 are both within my prince range. Like a 1 in 3 chance to get something amazing. It's probably difficult to fit a 4 slot RTX 4090 in a eGPU case, but a 2 slot 3090 works fine The GPU's built into gaming laptops might not have enough VRAM, even a 4090 built into a laptop might only have 16GB VRAM. 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 I'm really eyeing MLewd-L2 because i heard a lot of people talk it up saying its the best atm. It surprised me how great this model works. 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. As far as quality goes, a local LLM would be cool to fine tune and use for general purpose information like weather, time, reminders and similar small and easy to manage data, not for coding in Rust or GGUF (CPU + GPU): try loading the Q2 or Q3_K_S and only partially offload the layers to fit your VRAM. My goal was to find out which format and quant to focus on. VRAM capacity is such an important factor that I think it's unwise to build for the next 5 years. What I managed so far: Found instructions to make 70B run on VRAM only with a 2. The speed will be pretty decent, far faster than using the CPU. Right now, my main rotation is: U-Amethyst-20B is very good, it takes a few tries to get something unworldly good. Works pretty well. For example, LLM with 37B params or more even in 4bit quantization form don't fit in low-end card's The 3060 does not support SLI, but If you aren’t training/finetuning you can still think of it as a pool of 24GB vram. 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 Personally, I like it with the "creative" settings (ie --temp 0. If there’s on thing I’ve learned about Reddit, it’s that you can make the most uncontroversial comment of the year and still get downvoted. 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'm currently working on a MacBook Air equipped with an M3 chip, 24 GB of unified memory, and a 256 GB SSD. So I took the best 70B according to my previous tests, and re-tested that again with various formats and quants. From what I see you could run up to 33b parameter on 12GB of VRAM (if the listed size also means VRAM usage). Random people will be able to do transfer learning but they won't build a good LLM, because you need TBs of textual data to train it effectively. Good stuff are ahead of us. 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. If unlimited budget/don't care about cost effectiveness than multi 4090 is fastest for scalable consumer stuff. 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. As title says, i'm planning to build a server build for localLLM. 4 on a top_p of . 2. Im currently using llama 3 lumimaid 8b q8 at 8k context. The VRAM calculations are estimates based on best known values, VRAM usage can change depending on Quant Size, Batch Size, KV Cache, BPW and other hardware specific metrics. srry for bad english 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. Good local models for 24gb VRAM and 32gb of RAM? 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. 73 --repeat_last_n 256 --repeat_penalty 1. 2-1. Best LLM(s) For RP . 4bpw exl2 files using Oobabooga then load the entire model into your VRAM. Running with offload_per_layer = 6 It used 10GB VRAM + 2GB shared VRAM and 20GB RAM all within WSL2 Ubuntu on Windows 11. That is why I lowered my This is by far the most impressive LLM and configuration setup I've ever 23. Hope this helps if you're into local LLM( large language model) then 24gb vram is minimum, in this case a secondhand 3090 is the best value. Minimal comfortable vram for xl lora is 10 and preferable 16gb. But is worthy for how quick it works on a 24GB card, and how polished it is. 9 generally got me great summaries of an article while 90% of the time following the specified template given in its character and system prompt. 2 VRAM on 4bpw seems good, almost out of memory for a 24GB VRAM😅. This is hearsay, so use a good deal of salt. Ooba supports multi GPU without using an SLI bridge, just through the PCIe bus. Kinda sorta. So on an M1 Ultra with 128GB, you could fit then entire Phind-CodeLlama-34b q8 with 100,000 tokens of context. I think you could run the unquantized version at 8k context totally on the gpu. Members Online. 37it/s. Claude3 WAS good the first ~week it was released to the public. These are the models I've used and really like: GPT4-X-Alpaca pi3141 GGML Wizard 13b Uncensored GGML [ "continue the story/episode" was really good] dolphin-2. What should I be doing with my 24GB VRAM? I LOVE midnight-miqu-70b-v1. However, the 1080Tis only have about 11GBPS of memory That said, I was wondering: I would tend to proceed with the purchase of a NVIDIA 24GB VRAM. Do you have a link for westlake and is it a good general purpose llm like mistral or it's just good for role play? If you're experimenting, trying things out. 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. According to open leaderboard on HF, Vicuna 7B 1. 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. 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 Hopefully this quick guide can help people figure out what's good now because of how damn fast local llms move, and finetuners figure what models might be good to try training on. LLM E X PLORER. 8b for using function calling. Cpp just came out and I assume a lot has changed/Improved I hear models like Mistral can even change the landscape, what is currently best roleplay and storytelling LLM that can run on my PC with 32 GB Ram and 8gb Vram card (Or both since I also heard about Good LLMs don't even fit in 4090 24GB as they are approx 50-70GBs. A 4080 13B accelerated 70B CPU model might run faster than a 3090 + CPU split or a 3090-13B accelerated 70B cpu model. 4: You can do most things on both Linux and Windows, although yes I believe Linux can be preferable. And is surprisingly powerful. Claude makes a ton of mistakes. 6-mistral-7b. Ok, I know this may be asked here a lot, but the last time I checked this sub was around the time that LLaMa. If you're set on fitting a good share of the model in your GPU or otherwise achieving lightning-fast generation, I would suggest any 7B model -- mainly, vicuna 1. If you can fit the EXL2 quantizations into VRAM, they provide the best overall performance in terms of both speed and quality. r/LocalLLaMA. Both will fit in 24GB with a Q3 quant. I tried many different approaches to produce a Midnight Miqu v2. We should have to see if the base model is good, or wait for the finetunes. Bang for buck 2x3090s is the best setup. Moving to 525 drivers will just OOM kill it. This seems like a solid deal, one of the best gaming laptops around for the price, if I'm going to go that route. Oh by the way, Gemma 2 27b just came out, so maybe that model will be good for your setup. 1 Updated LLM Comparison/Test with new RP model: Rogue Rose 103B Get the Reddit app Scan this QR code to download the app now. With exl2 and 24GB of VRAM and 4k context you can squeeze up about 2. 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 The combination of top_p top_k and temperature matter for this task. Older drivers don't have GPU paging and do allow slightly more total VRAM to be allocated but it won't solve your issue, which is that you need to run a quantized model if you want to run a 13B at reasonable speed. With your 24GB, the tokens per second shouldn't be too slow. Mac architecture isn’t such that using an external SSD as VRAM will assist you that much in this sort of endeavor, because (I believe) that VRAM will only be accessible to the CPU, not the GPU. 0 that felt better than v1. At the high end is the 4090 with a price $1600. 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. buzet uanpueo cns mgqg qnrafa zeem gfamt janvb mpkgep zyncd