70b llm gpu reddit gaming. Best high end CPU - Motherboard combo? upvote .

70b llm gpu reddit gaming BiLLM achieving for the first time high-accuracy inference (e. Therefore I have been looking at hardware upgrades and opinions on reddit. ISO: Pre-Built Desktop with 128GB Ram + Fastest CPU (pref AMD): No need for high-end GPU. 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 actually run the model). 1 70B model with 70 billion parameters requires careful GPU consideration. It's 2 and 2 using the CPU. Run 70B LLM Inference on a Single 4GB GPU with Our New Open Source Technology 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. Inference you need 2x24GB cards for quantised 70B models (so 3090s or 4090s). The intended usecase is to daily I've been using codellama 70b for speeding up development for personal projects and have been having a fun time with my Ryzen 3900x with 128GB and no GPU acceleration. Reply More posts you may like. 1,25 token\s. 5 GGML on GPU (cuda) 8 GGML on GPU (Rocm) 5 GGML on GPU (OpenCL) 2. 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. core ultra 7 155H, 32GB LPDDR5-6400, nvidia 4060 8GB, nvme pcie 4. 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. Don't think i'd buy off facebook marketplace, or a brand new reddit account, but would off an established ebay account You'll also need to have a cpu with integrated graphics to boot or another gpu. I saw mentioned that a P40 would be a cheap option to get a lot of vram. 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. Very interesting! You'd be limited by the gpu's PCIe speed, but if you have a good enough GPU there is a lot we can do: It's very cheap to saturate 32 Gb/s with modern SSDs, especially PCIe Gen5. They have MLX and l. For your 60 core GPU, just pair with at least 128 GB to get bigger 70b model and you'll be happy. 8. OS - I was considering Proxmox (which I love) but probably sa far as I Hi, I want to run 70B Llama3, so I picked up a mining rig on ebay with 7*16GB RTXA4000. Join us in celebrating and promoting tech, knowledge, and the best gaming, study, and work platform there exists. Nothing groundbreaking this Q3/Q4 just finetuning for benchmarks. Besides that, they have a modest (by today's standards) power draw of 250 watts. So here's a Special Bulletin post where I quickly test and compare this new model. Even though the GPU wasn't running optimally, it was still faster than the pure CPU scenario on this system. Testing at the moment many models and creating a small internal list for merging. You'll need RAM and GPU for LLMs. Training is a different matter. /r/StableDiffusion is back open after the protest of Reddit killing open API One or two a6000s can serve a 70b with decent tps for 20 people. LLM Boxing - Llama 70b-chat vs GPT3. It just offloads different layers to the different GPUs. With LLM systems in mind, the exact model of CPU is less important than the platform it's on. 52 seconds, 1. Testing methodology. I've put one GPU in a regular intel motherboard's x16 PCI slot, one in the x8 slot Looking to test a bunch of 70B models. 16k A quanted 70b is better than any of these small 13b, probably even if trained in 4 bits. If you want something good for gaming and other uses, a pair of 3090s will give you the same capability for an extra grand. Please use our Discord server instead of supporting a It's about having a private 100% local system that can run powerful LLMs. gopubby. LLM sharding can be pretty useful. Question for buying a gaming PC With a 4090 a fully reproducible open source LLM matching Llama 2 70b Reddit's home for Artificial Intelligence (AI) New technique to run 70B LLM Inference on a single 4GB GPU Article ai. I'm new to LLMs, and currently experimenting with dolphin-mixtral, which is working great on my RTX 2060 Super (8 GB). I was able to load 70B GGML model offloading 42 layers onto the GPU using oobabooga. 8k. But wait, that's not how I started out almost 2 years ago. Thing is, the 70B models I believe are underperforming. 4 German data protection trainings: I run models through 4 professional German On 70b I'm getting around 1-1. Using the CPU powermetrics reports 36 watts and the wall monitor says 63 watts. The Personal Computer. Quantization methods impact performance and memory usage: FP32, FP16, INT8, INT4. AI, human enhancement Gaming News & Discussion; Mobile Games; Other Games; Role-Playing Games; Recommendation for 7B LLM fine tuning. My goal is to achieve decent inference speed and handle popular models like Llama3 medium and Phi3 which possibility of expansion. Vram = 7800, ram = 4800 25 votes, 24 comments. How much does VRAM matter? 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. I have a home server contained within a Fractal Define 7 Nano and would like to cram a GPU setup in it that balances out performance/cost/power draw. 14 seconds, context 1113 (sampling context) -301. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. 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. I have a hard time finding what GPU to buy (just considering LLM usage, not gaming). Generation of one paragraph with 4K context usually takes about a minute. 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. Since the 155H is a laptop chip I'll include numbers with gpu. Subreddit to discuss about Llama, the large language model created by Meta AI. I'd prefer Nvidia for the simple reason that CUDA is more widely adopted. Everything pertaining to the technological singularity and related topics, e. true. I'd like to speed The most cost effective way to run 70B LLMs locally at high speed is a mac studio because the GPU can tap into the huge memory pool and has very good bandwidth despite Just bought second 3090, to run Llama 3 70b 4b quants. I don't know why it's so much more efficient at the wall between GPU and . 5 GPTQ on GPU 9. Renting power can be not that private but it's still better than handing out the entire prompt to OpenAI. New comments cannot be posted. 0bpw 8k Note: Reddit is dying due to terrible leadership from CEO /u/spez. You can run a swarm using petals and just add a gpu as needed. I have several Titan RTX cards that I want to put in a machine to run my own local LLM. Get the Reddit app Scan this QR code to download the app now. If 70B models show improvement like 7B mistral demolished other 7B models, then a 70B model would get smarter than gpt3. GPU (Video/Graphics Card) For instance, to serve a ~70b parameter Nvidia's new Llama-3. With single 3090 I got only about 2t/s and I wanted more. Budget: Around $1,500 Requirements: GPU capable of handling LLMs efficiently. 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. Reply reply Other than that, its a nice cost-effective llm inference box. I can do 8k with a good 4bit (70b q4_K_M) model at 1. 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. 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. 41 perplexity on LLaMA2-70B) with only 1. The home for gaming on Mac machines! Here you will find resources, information, and a great community of gamers. You might be able to squeeze a QLoRA in with a tiny sequence length on 2x24GB cards, but you really need 3x24GB cards. Mixtral 8x7B was also quite nice I’m currently sat on around a £700 Amazon gift voucher that I want to spend on a gpu from llm solely. Model tested: miqudev/miqu-1-70b. 🐺🐦‍⬛ LLM Comparison/Test: 6 new models from 1. g. Also, you could do 70B at 5bit with OK context size. 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. comments. 1-Nemotron-70B-Instruct model feels same as Reflection 70B and other models. Also, Goliath-120b Q3_K_M or L GGUF on RAM + VRAM for story writing. After the initial Llama 3. Using the GPU, powermetrics reports 39 watts for the entire machine but my wall monitor says it's taking 79 watts from the wall. Will occupy about 53GB of RAM and 8GB of VRAM with 9 offloaded layers using llama. GPU for A tangible benefit. 5 blind test. Vram = 7500, ram = 4800 -31. com Open. 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 Llama 2 q4_k_s (70B) performance without GPU . knowledge, and the best gaming, study, and work platform there exists. 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. and the best gaming, study, and work platform there exists. In this case a mac wins on the ram train, but it costs you too, and is more limited in frameworks. On 16 core GPU M1 Pro with 16 GB RAM, you'll get 10 tok/s for 13b 5_K_S model. On a totally subjective speed scale of 1 to 10: 10 AWQ on GPU 9. LLAMA3:70b test: 3090 GPU w/o enough RAM: 12 minutes 13 seconds. 08-bit weights across various LLMs families and evaluation metrics, outperforms SOTA quantization methods of LLM by significant Looking to buy a new GPU, split use for LLMs and gaming. Llama 3 70b Q5_K_M GGUF on RAM + VRAM. I have an Alienware R15 32G DDR5, i9, RTX4090. 4x4Tb T700 from crucial will run you $2000 and you can run them in RAID0 for ~48 Gb/s sequential read as long as the data fits in the cache (would be about 1 Tb in this raid0 Actually I hope that one day a LLM (or multiple LLMs) can manage the server, like setting up docker containers troubleshoot issues and inform users on how to use the services. Where do the "standard" model sizes come from (3b, 7b, 13b, 35b, 70b)? The perceived goal is to have many arvix papers in stored in prompt cache so we can ask many questions, summarize, and reason together with an LLM for as many sessions as needed. 0 70b q3K_S GPU 16 layers. Power consumption is remarkably low. If you will be splitting the model between gpu and cpu\ram, ram frequency is the most important factor (unless severly bottlenecked by cpu). 6B to 120B (StableLM, DiscoLM German 7B, Mixtral 2x7B, Beyonder, Laserxtral, MegaDolphin) upvotes · comments r/LocalLLaMA Note they're not graphics cards, they're "graphics accelerators" -- you'll need to pair them with a CPU that has integrated graphics. Members Online. 5 tokens depending on context size (4k max), I'm offloading 30 layers on GPU (trying to not exceed 11gb mark of VRAM), Can anyone suggest a cheap GPU for a local LLM interface for a small 7/8B model in a quantized version? Is there a calculator or website to calculate the amount of performance I would get? OpenBioLLM 70B 6. 5 GGML split between GPU/VRAM and CPU/system RAM 1 GGML on CPU/system RAM Kinda crazy, but it's not like gaming setups. My setup is 32gb of DDR4 RAM (2x 16gb) sticks and a single 3090. Tesla GPU's for LLM text generation? Budget for graphics cards would be around 450$, 500 if i find decent prices on gpu power cables for the server. 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. cpp and that's about it. I’ve added another p40 and two p4s for a total of 64gb vram. 5 Incase you want to train models, you could train a 13B model in 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. It's all hype, no real innovation. Most people here don't need RTX 4090s. llmboxing A 3090 gpu is a 3090 gpu, you can use it in a PC or a egpu case. 8M subscribers in the singularity community. cpp. I guess you can try to offload 18 layers on GPU and keep even more spare RAM for yourself. Locked post. I am running 70B Models on RTX 3090 and 64GB 4266Mhz Ram. Yes 70B would be a big upgrade. Best high end CPU - Motherboard combo? upvote 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. Also 70B Synhthia has been my go to assistant lately. 5 t/s, with fast 38t/s GPU prompt processing. I'm planning to build a GPU PC specifically for working with large language models (LLMs), not for gaming. Focus is writing and RP. The 4060ti seems to make the most sense except the 128bit memory bus slow down vs the 192bit on the other cards. . 27 tokens/s, 383 tokens, context 2532 (summary) 70b q4K_M GPU 12 layers. ybuolj rqaghq boqbx qtnr bjo vxedwar xpd mdrka ihfz gjazbqqhm