70b llm gpu reddit gaming 2 64-bit CPU 64GB 256-bit LPDDR5 275TOPS, 200gb/s memory bandwidth wich isn't the fastest today (around 2x a modern cpu?) But enough space to run a 70b q6 for only 2000 USD 🤷♂️ (at around 60w btw) It's looking like 2x 4060 Ti 16gb is roughly the cheapest way to get 32gb of modern Nvidia silicon. ISO: Pre-Built Desktop with 128GB Ram + Fastest CPU (pref AMD): Performance-wise, I did a quick check using the above GPU scenario and then one with a little different kernel that did my prompt workload on the CPU only. Getting duo cards is a bad idea, since you're losing 50% performance for non-LLM tasks. Sample prompt/response and then I offer it the data from Terminal on how it performed and ask it to interpret the results. 1 T/S I saw people claiming reasonable T/s speeds. I've tried CPU inference and it's a little too slow for my use cases. 08-bit weights across various LLMs families and evaluation metrics, outperforms SOTA quantization methods of LLM by significant A tangible benefit. Just stumbled upon unlocking the clock speed from a prior comment on Reddit sub (The_Real_Jakartax) Below For example- I have a windows machine with the 4090. New comments cannot be posted. cpp to split them across your hardware, instead of just focusing on the GPU. r/buildapc. /main -m \Models\TheBloke\Llama-2-70B-Chat-GGML\llama-2-70b-chat. GGUF is surprisingly usable. can this run llama3 70B model? 2 - doing some computations (on CPU/GPU) 3 - reading / writing something from/to memory (VRAM/RAM) 4 - reading / writing something from/to external storage (NVME, SSD, HDD, whatever) The software that is doing LLM inference for you is still a software, so it follows the above idea in terms of what it can do. get 2 used 3090 and you can run 70b models too at around 10-13 t/s ZOTAC Gaming GeForce RTX™ 3090 Trinity OC 24GB GDDR6X 384-bit 19. The home for gaming on Mac machines! Here you will find resources, information, and a great community of gamers. How much memory is necessary ? So all in all got to understand that having a huge ram and better graphics card has no part in this llm situation. llmboxing Current way to run models on mixed on CPU+GPU, use GGUF, but is very slow. I notice it seems not really censured or less than others. 6B to 120B (StableLM, DiscoLM German 7B, Mixtral 2x7B, Beyonder, Laserxtral, MegaDolphin) upvotes · comments r/LocalLLaMA BiLLM achieving for the first time high-accuracy inference (e. g. 0), decent size/speed SSD, 1300+ PSU, Large case with PCIe riser, would give you usable tokens/s (7-10 range). 0. If you will be splitting the model between gpu and cpu\ram, ram frequency is the most important factor (unless severly bottlenecked by cpu). I can do 8k with a good 4bit (70b q4_K_M) model at 1. 0, which theoretically tops What GPU split should I do for RTX 4090 24GB GPU 0 and RTX A6000 48GB GPU 1 and how much context would I be able to get with Llama-2-70B-GPTQ-4bit-32g-actorder_True? a fully reproducible open source LLM matching Llama 2 70b /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app So I've had the best experiences with LLMs that are 70B and don't fit in a single GPU. Mixtral was especially upsetting with its poor performance. For training? Yes and no, multiple GPU training methods don't work really good on GPUs without NVLink. For many gaming is nonsense while using LLMs is productive. My setup is 32gb of DDR4 RAM (2x 16gb) sticks and a single 3090. I'm planning to build a GPU PC specifically for working with large language models (LLMs), not for gaming. However, it's literally crawling along at ~1. Or check it out in the app stores Popular; TOPICS. ggmlv3. Mac can run LLM's but you'll never get good speeds compared to Nvidia as almost all of the AI tools are build upon CUDA and it will always run best on these. On 16 core GPU M1 Pro with 16 GB RAM, you'll get 10 tok/s for 13b 5_K_S model. It’s been the best density per buck I’ve found since many 4U configurations that can handle 3, 4 and 8 dual slot GPUs are much more expensive. I am thinking of is running Llama 2 13b GPTQ in Microsoft Azure vs. In the repo, they claim "Finetune Llama-2 70B on Dual 24GB GPUs" and "Llama 70B 4-A100 40GB Training" is possible. I realized that a lot of the finetunings are not available on common llm api sites, i want to use nous capybara 34b for example but the only one that offered that charged 20$/million tokens which seemed quite high, considering that i see Lama 70b for around 0. It just offloads different layers to the different GPUs. 1 8B on my system and it works perfectly for the Inference you need 2x24GB cards for quantised 70B models (so 3090s or 4090s). You're gonna need two of those, that's quite a step. Budget: Around $1,500 Requirements: GPU capable of handling LLMs efficiently. My goal is to achieve decent inference speed and handle popular models like Llama3 medium and Phi3 which possibility of expansion. this kind of cut the entire possibility. It's now possible to run a 2. Gaming. Using the CPU powermetrics reports 36 watts and the wall monitor says 63 watts. gguf. Then click Download. It is lightweight and portable - you can create an LLM app on a Mac, compile to Wasm, and then run the binary app on Nvidia devices. And the P40 GPU was scoring roughly around the same level of an RX 6700 10GB. Huawei matebook d15 or Asus x515ep 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? Welcome to r/gaminglaptops, the hub for gaming laptop enthusiasts. 5 GGML split between GPU/VRAM and CPU/system RAM 1 GGML on CPU/system RAM Good question, for existing LLM service providers (inference engines) from the date the paper was published: " Second, the existing systems cannot exploit the opportunities for memory sharing. 10 is released deployment should be relatively straightforward (yet still much more complex than just about Llama 2 70B model running on old Dell T5810 (80GB RAM, Xeon E5-2660 v3, no GPU) My goal is to host my own LLM and then do some API stuff with it. I've created Distributed Llama project. I don't know why it's so much more efficient at the wall between GPU and Reddit's home for Artificial Intelligence (AI) New technique to run 70B LLM Inference on a single 4GB GPU Article ai. Actually the 8 bit quantization (Q8) is a choice available among all the . Or check it out in the app stores Tldr: Can I get away with 32(or 64)GB of system ram and 48(or 96)GB GPU VRAM for large LLM like lzlv-70b or goliath-120b? Share Add a Comment. Is there any chance of running a model with sub 10 second query over local documents? Thank you for your help. Or check it out in the app stores Best budget LLM GPU? I'm looking for a budget GPU server for running and training LLMs, preferably 70b+ I want to keep the budget around $1. I’ve added another p40 and two p4s for a total of 64gb vram. Model? VRAM/ GPU config? Tutorials? upvotes LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b I'm using a normal PC with a Ryzen 9 5900x CPU, 64 GB's of RAM and 2 x 3090 GPU's. I have my my P40's in HP-DL gen 8 and 9 which you can also pick-up cheap and then upgrade the CPUs to top tier E5 2698 or 99 for cheap. If 70B models show improvement like 7B mistral demolished other 7B models, then a 70B model would get smarter than gpt3. x1 will only make the models load slower. 98 B llm_load_print_meta: model size = 38. I was an average gamer with an average PC, I had a 2060 super and a Ryzen 5 2600 CPU, honestly I'd still use it today as I don't need maxed out graphics for gaming. I'm currently using Meta-Llama-3-70B-Instruct-Q5_K_M. the 3090. What would be the best GPU to buy, so I can run a document QA chain fast with a 70b Llama model or at least 13b model. com Open. No quantization, distillation, pruning or other model compression techniques that would result in degraded model performance are needed. I'm currently in the market of building my first PC in over a decade. So I took the best 70B according to my previous tests, and re-tested that again with various formats and quants. You can probably fit some sort of 34B ok. cpp over oobabooga UI. knowledge, and the best gaming, study, and work platform there exists. LLM services often use advanced decoding algorithms, such as parallel sampling and beam search, that generate multiple outputs per request. Renting power can be not that private but it's still better than handing out the entire prompt to OpenAI. cpp as the model loader. 5 Incase you want to train models, you could train a 13B model in Looking to buy a new GPU, split use for LLMs and gaming. Command-R+ isn't the brightest light out there but it's language and creative writing capabilities is something I want locally. I'm considering buying a new GPU for gaming, but in the meantime I'd love to have one that is able to run LLM quicker. For GPU inference, using exllama 70B + 16K context fits comfortably in 48GB A6000 or 2x3090/4090. 33 MiB llm_load_tensors: CUDA1 buffer size Assuming using the same cloud service, Is running an open sourced LLM in the cloud via GPU generally cheaper than running a closed sourced LLM? (ie. I personally use 2 x 3090 but 40 series cards are very good too. Unlikely that 2x used 3090 (USD 800 each) would cost the same as 1x used A6000 (USD 4000). Right now I'm using runpod, colab or inference APIs for GPU inference. This post also conveniently leaves out the fact that CPU and hybrid CPU/GPU inference exists, which can run Llama-2-70B much cheaper then even the affordable 2x TESLA P40 option above. I'd prefer Nvidia for the simple reason that CUDA is more widely adopted. But I don't have a GPU. LLM sharding can be pretty useful. Using the GPU, powermetrics reports 39 watts for the entire machine but my wall monitor says it's taking 79 watts from the wall. Important note is that I only use GPU to offload model *layers*, the KQV cache (context) is kept 100% in RAM (no_offload_kqv=true option). Tried running llama-70b on 126GB of memory; memory overflow. This is a forum where guitarists, from novice to experienced, can explore the world of guitar through a variety of media and discussion. Welcome to r/gaminglaptops, the hub for gaming laptop enthusiasts. Ayumi LLM benchmarks for role-playing upvote r/AIRolePlaying. CPU is nice with the easily expandable RAM and all, but you’ll lose out on a lot of speed if you don’t offload at least a couple layers to a fast gpu 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. 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 If you've got the budget, RTX 3090 without hesitation, the P40 can't display, it can only be used as a computational card (there's a trick to try it out for gaming, but Windows becomes unstable and it gives me a bsod, I don't recommend it, it ruined my PC), RTX 3090 in prompt processing, is 2 times faster and 3 times faster in token generation (347GB/S vs 900GB/S for rtx 3090). 0 x16, so I can make use of the multi-GPU. 55 bits per word 70B model barely on a 24GB card. Reasonable Graphics card for LLM AND Gaming . q4_K_S. 5 tokens per second running Llama 2 70B with a Q5 Quant. Thinking about a 4x3090 build, but that's a different beast to tame than a desktop PC with just an additional graphics card. Model tested: miqudev/miqu-1-70b. Sort by: Best. 16k I'm new to LLMs, and currently experimenting with dolphin-mixtral, which is working great on my RTX 2060 Super (8 GB). 58 GiB (4. 5 GPTQ on GPU 9. Join us in celebrating and promoting tech, knowledge, and the best gaming, study, and work platform there exists. core ultra 7 155H, 32GB LPDDR5-6400, nvidia 4060 8GB, nvme pcie 4. I looked into Bloom at release and have used other LLM models like GPT-Neo for a while and I can tell you that they do not hold a candle to the LLaMA lineage (or GPT-3, of course). I saw mentioned that a P40 would be a cheap option to get a lot of vram. 0i1-IQ2_S. At 6144 context using rope scaling, a 70b q8 takes about 2. Responses aren't instantaneous here. M2 Ultra is the smallest, prettiest, out of the box easiest, most powerful personal LLM node today. Llama 3 70b Q5_K_M GGUF on RAM + VRAM. I randomly made somehow 70B run with a variation of RAM/VRAM offloading but it run with 0. Set n-gpu-layers to max, n_ctx to 4096 and usually that should be enough. Hi guys, yet another post about a LLM self-hosted server setup (inference only). Found instructions to make 70B run on VRAM only with a 2. Power consumption is remarkably low. The build I made called for 2X P40 GPU's at $175 each, meaning I had a budget of $350 for GPU's. It's doable with blower style consumer cards, but still less than ideal - you will want to throttle the power usage. If you want something good for gaming and other uses, a pair of 3090s will give you the same capability for an extra grand. AFAIK PCIe mode does not matter. Llama 2 q4_k_s (70B) performance without GPU . Llama 2 70B is old and outdated now. You'll need RAM and GPU for LLMs. On a totally subjective speed scale of 1 to 10: 10 AWQ on GPU 9. Also, you could do 70B at 5bit with OK context size. Or check it out in the app stores Run 70B LLM on 4Gb GPU with layered inference twitter. LLM was barely coherent. offloading non I started with running quantized 70B on 6x P40 gpu's, but it's noticeable how slow the performance is. I can run 70Bs split, but I love being able to have a second GPU dedicated to running a 20-30B while leaving my other GPU free to deal with graphics or running local STT and TTS, or occasionally StableDiffusion. I would prioritize RAM, shooting for 128 Gigs or as close as you can get, then GPU aiming for Nvidia with as much VRAM as possible. I have been tasked with estimating the requirements for purchasing a server to run Llama 3 70b for around 30 users. AI, human enhancement I’m currently sat on around a £700 Amazon gift voucher that I want to spend on a gpu from llm solely. The 4060ti seems to make the most sense except the 128bit memory bus slow down vs the 192bit on the other cards. GGUF models ^. Especially when it comes to running multiple GPU's at the same time. The second card will be severely underused, and/or be a cause for instabilities and bugs. Will occupy about 53GB of RAM and 8GB of VRAM with 9 offloaded layers using llama. Sure, an average gaming pc might have like 8gb of vram, which is perfect for the 8b model, but a flagship gpu can't really run the 70b model. For a bit less than ~$1k, it seems like a decent enough bargain, if it's for a dedicated LLM host and not for gaming. 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. Most serious ML rigs will either use water cooling, or non gaming blower style cards which intentionally have lower tdps. 2t/s, suhsequent text generation is about 1. THEY CANNOT PRETRAIN A 70B LLM WITH 2x24GB GPUs. Inference speed is limited by the internal GPU memory bandwidth, the in and out of them is rarely a bottleneck. I’d love to see a llama 3:70b fine tune, but people need nice hardware to train this size. 5 t/s, with fast 38t/s GPU prompt processing. We really can’t even trust benchmarks. Even though the GPU wasn't running optimally, it was still faster than the pure CPU scenario on this system. They work, but they are slower than using NVLink or having all the VRAM in a single card (ShardedDataParalel comes to mind from accelerate library). LLAMA3:70b test: 3090 GPU w/o enough RAM: 12 minutes 13 seconds. 5 blind test. GPT-3. I am mostly thinking of adding a secondary GPU, I could get a 16GB 4060ti for about $700, or for about double I could get a second-hand 3090 (Australian prices are whack). And I have 33 layers offloaded to the GPU which results in ~23GB of VRAM being used with 1GB of VRAM left over. Even taking into account the fact that the model with Q3 quantization is located entirely in the video memory of two adapters. 80 BPW) llm_load_print_meta: general. Butwell this is all experimental. Valheim; Genshin Impact; Minecraft; Pokimane; Halo Infinite; Call of Duty: Warzone; Path of Exile; Hollow Knight: Silksong; Escape from Tarkov; Watch Dogs: Legion; Run 70B LLM Inference on a Single 4GB GPU with This 70b doesn't quite fit in 24gb vram though. Once you then want to step up to a 70B with offloading, you will do it because you really really feel the need for complexity and is willing to take the large performance hit in output. 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 It's 2 and 2 using the CPU. Then run your LLMs with something like llama. 0 Actually I hope that one day a LLM (or multiple LLMs) can manage the server, like setting up docker containers troubleshoot issues and inform users on how to use the services. Members Online. You need at least a few more, i would go with 8x80GB on same node (yet you'll still need offloading optimizer states like ZeRO) Therefore this is not a slurm issue, it simply serves as a job scheduler and resource broker. If you are buying new equipment, then don’t build a PC without a big graphics card. Tian Guan Ci Fu, & the donghua adaptation Heaven Official’s Blessing! Join the TGCF Reddit EVGA XC3 ULTRA GAMING GeForce RTX 3090 24 GB Video Card: $1299. Therefore I have been looking at hardware upgrades and opinions on reddit. But wait, that's not how I started out almost 2 years ago. What are your biggest pet-peeves A few months ago I got a 5b param LLM (one of the defaults from FastChat, iirc it had an M in the title) running on a Jetson Xavier (there's some breaking change Nvidia made between Orin and everything preceding it, I think it's related to the Ubuntu 18. How to run 70b model on 24gb gpu? Question | Help This is a subreddit to discuss all things related to VFIO and gaming on virtual machines in general. 21 votes, 53 comments. Locked post. 99 @ Amazon Custom: NVIDIA - GeForce - RTX NVLINK BRIDGE for 30-Series Products - Space Gray: $79. How much does VRAM matter? Gaming News & Discussion; Mobile Games; Other Games; Role-Playing Games; Recommendation for 7B LLM fine tuning. and the best gaming, study, and work platform there exists. Discover discussions, news, reviews, and advice on finding the perfect gaming laptop. and the best gaming, study, and work No. . if the capital constrains hits . I'm using midnight-miqu-70b-v1. If you want a good gaming GPU that is also useful for LLMs, I'd say get one RTX 3090. cpp. One of our company directors has decided we need to go 'All in on AI'. 5 Gbps PCIE 4. Increase the inference speed of LLM by using multiple Choosing the right GPU (e. Reply More posts you may like. Open source 7b parameter models are running fine on my workstation. I have a hard time finding what GPU to buy (just considering LLM usage, not gaming). /r/StableDiffusion is back open after the protest of Reddit killing open API Get the Reddit app Scan this QR code to download the app now. Please use our Discord server instead of supporting a On 70b I'm getting around 1-1. With this model I can unload 23 of 57 layers to GPU. 8M subscribers in the singularity community. I am running 70B Models on RTX 3090 and 64GB 4266Mhz Ram. Testing methodology. For inference? For LLM yeah it does, on exllama/v2 you will get absurd speeds. Or check it out in the app stores (7800x3d is only there as last resort for gaming needs instead of work hehe). You are going to need two of these cards, which makes the 70b a little awkward in sizing. 99 @ Amazon Video Card: EVGA XC3 ULTRA GAMING GeForce RTX 3090 24 GB Video Card: $1299. 2048-core NVIDIA Ampere architecture GPU with 64 Tensor cores 2x NVDLA v2. I guess you can try to offload 18 layers on GPU and keep even more spare RAM for yourself. The Personal Computer. EDIT: I am newbie to AI, want to run local LLMs, greedy to try LLama 3, but my old laptop is 8 GB RAM, I think in built Intel GPU. Use EXL2 to run on GPU, at a low qat. 1 70B and Llama 3. Is there anyway to get an external graphics card that could use Nvidia GeForce RTX 2060 Super / RX 5700 XT (8GB+ of VRAM) needed to play a game? a fully reproducible open source LLM matching Llama 2 70b As the title says there seems to be 5 types of models which can be fit on a 24GB vram GPU and i'm interested in figuring out what configuration is best: Q4 LLama 1 30B Q8 LLama 2 13B Q2 LLama 2 70B Q4 Code Llama 34B (finetuned for general usage) Q2. Reply reply psi-love • A better graphics card does make a difference, it's just not as important for a normal user as for a For the 70b LLM models I can split the workload between that and the slower P40 GPUs to avoid offloading any layers to system memory since that would be detrimental to the performance. So here's a Special Bulletin post where I quickly test and compare this new model. Don't think i'd buy off facebook marketplace, or a brand new reddit account, but would off an established ebay account 25 votes, 24 comments. py`. 2 tokens per second. After the initial load and first text generation which is extremely slow at ~0. The output of LLAMA 3 70b LLM (q3, q4) on the two specified GPUs was significantly (about 4 times) slower than running models that typically only run on CUDA (for example, cuda-based text-generation-webui with llama. 1 70Bmodel, with its staggering 70 billion parameters, represents I've seen services that you pay for GPU usage, but OpenRouter seems to rent I have deployed Llama 3. Either use Qwen 2 72B or Miqu 70B, at EXL2 2 BPW. , RTX A6000 for INT4, H100 for higher precision) is crucial for optimal performance. I mean, you are using Reddit. In the blog, they only claim that they can "train llama2-7b on the included alpaca dataset on two 24GB cards", which is the same as they claimed in their `train. Thing is, the 70B models I believe are underperforming. If you're willing to run a 4-bit quantized version of the model, you can spend even less and get a Max instead of an Ultra with 64GB of RAM. 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. Gaming Consoles & Gear; Gaming News & Discussion; Mobile Games; Other Games; Role-Playing Games; Simulation Games; I want to set up a local LLM for some testing, and I think the LLaMA 3:70B is the most capable out there. I have 4x3090's and 512GB of RAM (not really sure if ram does something for fine-tuning tbh). Again, this is just my experience. Everything seems to work well and I can finally fit a 70B model into the VRAM with 4 bit quantization. Currently it’s got 4x p100’s and 4x p40’s in it that get a lot of use for non-llm AI, so not sure I’m willing to tinker around with half the devices even if the compute cores is better. Also, Goliath-120b Q3_K_M or L GGUF on RAM + VRAM for story writing. I haven’t gotten around to trying it yet but once Triton Inference Server 2023. Edit 2: Nexesenex/alchemonaut_QuartetAnemoi-70B-iMat. bin -p "<PROMPT>" --n-gpu-layers 24 -eps 1e-5 -t 4 --verbose-prompt --mlock -n 50 -gqa 8 /r/StableDiffusion is back I have been running Llama 2 on M1 Pro chip and on RTX 2060 Super and I didn't notice any big difference. 5 GGML on GPU (cuda) 8 GGML on GPU (Rocm) 5 GGML on GPU (OpenCL) 2. 5 minutes to send me a response. q3_K_S. GGUF is even better than Senku for roleplaying. Hi, I want to run 70B Llama3, so I picked up a mining rig on ebay with 7*16GB Found out about air_llm, https://github. The 2697s seem a good compromise as prices have gone up since so many have realized what a couple E5's and a decent graphics card can do for AI, mining or gaming. In text-generation-web-ui: Under Download Model, you can enter the model repo: TheBloke/Llama-2-70B-GGUF and below it, a specific filename to download, such as: llama-2-70b. On the software side, you have the backend overhead, code efficiency, how well it groups the layers (don't want layer 1 on gpu 0 feeding data to layer 2 on gpu 1, then fed back to either layer 1 or 3 on gpu 0), data compression if any, etc. Use llama. Undervolt to the minimum value that produces fast enough responses in your workflow (I run my 3090 at 50%). To date I have various Dell Poweredge R720 and R730 with mostly dual GPU configurations. Best 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. 8k. Considering I got ~5t/s on i5-9600k with 13b in CPU mode, I wouldn't expect to get more than that with 70b in CPU mode, probably less. You might be able to squeeze a QLoRA in with a tiny sequence length on 2x24GB cards, but you really need 3x24GB cards. . But if the current Nvidia lineup, what is the ideal choice at the crossroads of VRAM, performance, and cost? I don't intend to run 70b models solely in my GPU, but certainly something more than 10GB would be preferred. Depending on your use case, inference on 70B would work fine on 2x 3090, 64GB Memory (not slow), decent cpu/mobo to give you decent PCIe speed (2x 8x PCIe 3. Generation of one paragraph with 4K context usually takes about a minute. Overall I get about 4. AirLLM optimizes inference memory usage, allowing 70B large language models to run inference on a single 4GB GPU card. I've run llama2-70b with 4-bit quantization on my M1 Max Macbook Pro with 64GB of ram. 0 70b q3K_S GPU 16 layers. When processing those layers (and it processes layers sequentially) it will go at the speed of the GPU it's on, but usually that's moderately minimal in terms of impact (it has an impact but probably not overly significant vs being able to I have several Titan RTX cards that I want to put in a machine to run my own local LLM. 4 tokens depending on context size (4k max), I'm offloading 25 layers on GPU (trying to not exceed 11gb mark of VRAM), On 34b I'm getting around 2-2. Question for buying a gaming PC With a 4090 a fully reproducible open source LLM matching Llama 2 70b It's about having a private 100% local system that can run powerful LLMs. Tesla GPU's for LLM text generation? Budget for graphics cards would be around 450$, 500 if i find decent prices on gpu power cables for the server. r/AIRolePlaying. It's all hype, no real innovation. 5 tokens depending on context size (4k max), I'm offloading 30 layers on GPU (trying to not exceed 11gb mark of VRAM), The Best Fiction/Novel Writing I've seen from an LLM to date - Midnight-Miqu-70B-v1. Anyway, for gaming, dual GPU is kind of awful. Llama cpp and exllama work out of the box for multiple GPU's. Most people here don't need RTX 4090s. You can get a higher quantitized gguf of the same model and load that on the gpu, it will be slower because of the higher quants, but it will give better results You might want to look into exllama2 and its new exl2 format. exllama scales very well with multi-gpu. This allows me to use large context and not get out-of-memory errors. 8x H100 GPU's inferencing llama 70B = 21,000+ Tokens/Sec (server environment number-- the lower number) I'm currently trying to figure out where it is the cheapest to host these models and use them. Has anyone crunched the numbers on this configuration? I'd love it if someone could share details before I start impulse-buying :D Do these work out of the box with competitive speeds to their Cuda counterparts? I know you can use openvino for stable diffusion, I haven't heard it translate super well to the LLM world. gguf with 36 layers to the GPU (RTX 4090). 73 MiB llm_load_tensors: CUDA0 buffer size = 22086. 4 German data protection trainings: I run models through 4 professional German 1. Would these cards alone be sufficient to run an unquantized version of a 70B+ LLM? My thinking is yes, but I’m looking for reasons as to why it wouldn’t work since I don’t see anyone talking about these cards anymore. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. 0 Gaming Graphics Card, IceStorm 2. do we pay a premium when running a closed sourced LLM compared to just running anything on the cloud via GPU?) One eg. For your 60 core GPU, just pair with at least 128 GB to get bigger 70b model and you'll be happy. A 3090 gpu is a 3090 gpu, you can use it in a PC or a egpu case. Make sure to not increase your context size too much as this can cause your prompt processing speeds to tank. Get the Reddit app Scan this QR code to download the app now. With 3x3090/4090 or A6000+3090/4090 you can do 32K with a bit of room to spare. This looks like we’re basically getting 70b llama I recently got hold of two RTX 3090 GPUs specifically for LLM inference and training. gguf . Llama 3:70b > llama 3: 8B fine tuned for code. Where do the "standard" model sizes come from (3b, 7b, 13b, 35b, 70b)? upvotes I can run low quant 34b models, but the deterioration is noticed vs the 70b models I used to run on an A100. 0bpw 8k Note: Reddit is dying due to terrible leadership from CEO /u/spez. But, 70B is not worth it and very low context, go for 34B models like Yi 34B. Since all the weights get loaded into the vram for inferencing and stay there as long as inference is taking place the 40Gbps limit for thunderbolt 4 should not be a bottleneck or am i wrong on this front? That said I would enjoy running command-R+ or WizardLM-2-8x22b with decent quants in exl2. Now, if you're planning on going down the rabbit hole of training your own LLM, there are some other considerations. A q4 is a little less than that. M3 Max 16 core 128 / 40 core GPU running llama-2-70b-chat. I’m building a dual 4090 setup for local genAI experiments. GGUF is more like a container for the model and inside you can put multiple versions, with different quantizations. I'm not sure what it's hitting though, the cpu processing spikes a lot when it is processing. 80GB is not enough for training 70b LLM on bf16. And that's just the hardware. The budget is ~5000$. I went from x16 to x4, and I am still planning to Beyond that, there is more to LLMs than chat. name = LLaMA v2 llm_load_print_meta: BOS If you want the best performance for your LLM then stay away from using Mac and rather build a PC with Nvidia cards. 41 perplexity on LLaMA2-70B) with only 1. gguf and it's decent in terms of quality. My hope though is that LLM's (and the tools to run them) get smaller, better and need less resources to get the same results as higher counterparts. I have a 1070. 04 EOL) at a reasonable speed. 99 @ Best Buy Custom: Super Micro Case Custom Has anyone tried using this GPU with ExLlama for 33/34b models? What's your experience? a fully reproducible open source LLM matching Llama 2 70b but the software in your heart! Join us in celebrating and promoting tech, knowledge, and the best gaming, study, and work platform there exists. I'm using OobaBooga and Tensor core box/etc are all checked. Join our passionate community to stay informed and connected with the latest trends and technologies in the gaming laptop world. With that said, my 4090 can barely run a 70b at all. Since the 155H is a laptop chip I'll include numbers with gpu. 1-Nemotron-70B-Instruct model feels same as Reflection 70B and other models. LLaMA has some miracle-level Kung Fu going on under the hood to be able to approximate GPT-3 on a desktop consumer CPU or GPU. Without GPU offloading the same is closer to about . 2t/s. Their PCIe interface is 4. The unified memory on the Mac is nearly as fast as GPU memory which is why it performs so well. Training is a different matter. However, if I go up to a 70b q3 on the 4090, it goes to crap. true. As a rule, as long as it is the same model family, for example Llama based models, Q2 70B beats Q8 34b, but for other model families, Like Minstral for 7B and Yi for 34B, are in lot of ways more comparable to the bigger Llama models (13B and 70B respectively). It’s been trained on our two recently announced custom-built 24K GPU clusters on over 15T token of data – a training dataset 7x larger than that used for Llama 2, including 4x more code. 14 seconds, context 1113 (sampling Breaking news: Mystery model miqu-1-70b, possibly a leaked MistralAI model, perhaps Mistral Medium or some older MoE experiment, is causing quite a buzz. You can run a swarm using petals and just add a gpu as needed. I use llama. For coding, deepseek-coder-33b in my opinion is not worse than llama3-70b, so for a single 24gb gpu it's an easy go-to recipe. I am wondering if it would be worth to spend Get a graphics card for gaming, not for LLM nonsense. Q5_K_M. Yes 70B would be a big upgrade. Generation Fresh install of 'TheBloke/Llama-2-70B-Chat-GGUF'. LLM Boxing - Llama 70b-chat vs GPT3. What would be system requirement to comfortably run Llama 3 with decent 20 to 30 tokes per second at least? Edit: The IQ2_X2 quant of dranger003/Senku-70B-iMat. Everything pertaining to the technological singularity and related topics, e. The topmost GPU will overheat and throttle massively. Using a dev platform for LLM apps with custom models and actions. com/lyogavin/Anima/tree/main/air_llm, Nvidia's new Llama-3. And if you go on ebay right now, I'm seeing RTX 3050's for example for like $190 to $340 just at a glance. My goal was to find out which format and quant to focus on. The Llama 3. 10572 should be good. Open comment sort options doubt about dual GPU for gaming/LLMs comments. q3_k_s. With Wizard I can fit Q4_K version in my memory. 5 Turbo. 5 t/s. And yes of course all closed models will beat even the best open models including Claude and Get the Reddit app Scan this QR code to download the app now. Get the Reddit app Scan this QR code to download the app now rope_finetuned = unknown llm_load_print_meta: model type = 70B llm_load_print_meta: model ftype = Q4_K - Medium llm_load_print_meta: model params = 68. The M2 is closer to 10-15 tokens per second on a 70b q2. Where do the "standard" model sizes come from (3b, 7b, 13b, 35b, 70b)? The perceived goal is to have many arvix papers in stored in prompt cache so we can ask many questions, summarize, and reason together with an LLM for as many sessions as needed. Now, I don't mind waiting for that, because I prefer quality more than anything, but depending on how you feel on speed you may find yourself getting halfway frustrated. Also 70B Synhthia has been my go to assistant lately. Note they're not graphics cards, they're "graphics accelerators" -- you'll need to pair them with a CPU that has integrated graphics. Take the A5000 vs. You'll also need to have a cpu with integrated graphics to boot or another gpu. 8. There are lower quality quants, all the way down to Q2, that loses a lot of performance. Mixtral 8x7B was also quite nice One or two a6000s can serve a 70b with decent tps for 20 people. I would like to run a 70b LLaMa model, hence I was thinking about buying 2x RTX 3090 (but I am open to other suggestions). And all 4 GPU's at PCIe 4. 5k *plan on buying multiple of these servers and chaining them somehow* What would be the best gpu which llms are you running 7b, 13b, 22b, 70b? and what performance are you getting out of the card for those models on the eGPU. Use Q4 quants. this might be a stupid question since any LLM not recommended to run on cpu. I have a home server contained within a Fractal Define 7 Nano and would like to cram a GPU The most cost effective way to run 70B LLMs locally at high speed is a mac I was able to load 70B GGML model offloading 42 layers onto the GPU using oobabooga. You can fit a 70b 5bpw with 32k context in that or go with a 103/120 with Firstly, you can't really utilize 2X GPU's for stable diffusion. 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. I've monitored the link before, and by the end of training a 70B model, it had 100's of GB in transfers between cards. Hybrid GPU+CPU inference is very good. 0 12-core Arm Cortex-A78AE v8. I have another laptop with 40GB of RAM and an NVIDIA 3070 GPU, but I understand that Ollama does not use the GPU You will still be able to squeeze a 33B quant into GPU, but you will miss out of options for extra large context, running a TTS and so on. 🐺🐦⬛ LLM Comparison/Test: 6 new models from 1. Kinda crazy, but it's not like gaming setups. Best high end CPU - Motherboard combo? upvote While you can run any LLM on a CPU, it will be much, much slower than if you run it on a fully supported GPU. 7$/million tokens. Try for yourself I just wanted to report that with some faffing around I was able to get a 70B 3 bit model Llama2 inferencing at ~1 token / second on Win 11. gopubby. I can load an 70b q2 and it runs REALLY well; like 15-25 tokens per second well, after Nvidia's new driver (which I only just got 2 days ago lol). I have a intel core i5 12400 cpu. I've got a Dell precision with an RTX 3500 in it and despite being rubbish for LLM's Can anyone suggest a cheap GPU for a local LLM interface for a small 7/8B model in a quantized version? Is there a calculator or website to calculate the amount of performance I would get? OpenBioLLM 70B 6. Reply reply Other than that, its a nice cost-effective llm inference box. 5 bpw that run fast but the perplexity was unbearable. 55 LLama 2 Get the Reddit app Scan this QR code to download the app now. 1,25 token\s. Vram = 7500, ram = 4800 -31. Both are based on the GA102 chip. Nothing groundbreaking this Q3/Q4 just finetuning for benchmarks. Having them work on e. Besides that, they have a modest (by today's standards) power draw of 250 watts. Welcome to r/guitar, a community devoted to the exchange of guitar related information. Or check it out in the app stores TOPICS offloading 33 repeating layers to GPU llm_load_tensors: offloaded 33/57 layers to GPU llm_load_tensors: CPU buffer size = 22166. cpp) .
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