Local LLMs are no longer a myth. Enthusiasts are squeezing usable assistants from 12GB GPUs and eyeing trillion-parameter dreams via smarter setups [5].
- Run LLMs Locally - A practical guide to the local LLM journey, laying out hardware and software essentials so you can start without a data-center. [1]
- LlamaBarn - On Macs, this auto-config tool tunes models to your hardware, cutting guesswork and trial-and-error. [2]
- The 12GB reality isn’t all doom and gloom. In discussions around small-GPU life, gemma 3 12b shines in VRAM-limited runs, while Qwen3VL-30B-A3B (and similar 14B-class options) are cited as competitive local choices when quantized or configured wisely. [3]
- Local Setup - Real-world rigs mix GPUs like 3090, 4090, 5090, and RTX 6000 Pro; teams report 70M–120M tokens processed daily. Cooling, 240V power, and keeping vllm running smoothly are ongoing concerns; some boards push up to seven GPUs with careful hardware choices such as ASUS Pro WS WRX80E-SAGE SE and Threadripper CPUs. [4]
- MoE kernels - A bold path toward trillion-parameter work: custom MoE kernels promise cloud-portable models, with projects like Perplexity AI’s pplx-garden pushing the ecosystem forward. The mantra remains: “No local, no care” isn’t the goal, but cloud-friendly local tooling helps bridge the gap. [5]
The bottom line: the local-LLaMA vibe is evolving—from tight VRAM hacks to modular, small-batch MoE approaches that flirt with 1T-scale ambitions. Watch how LlamaBarn and local-guides keep expanding what fits on a desk, not just a data center.
References
Run LLMs Locally
Guide to running LLMs locally, covering hardware, software, performance, and setup considerations
View sourceLlamaBarn – automatically configure models based on your Mac's hardware
GitHub project LlamaBarn automatically configures LLMs to match Mac hardware, streamlining model selection and setup
View sourceBest AI models to run on a 12 GB vram gpu?
User discusses LLMs suited for 12GB VRAM, comparing Gemma, Qwen, MOE, and quantization with RAM and speed considerations.
View sourceLocal Setup
Self-hosted LLM inference rig with GPUs; compares costs to cloud, uses vLLM/Llama models, discusses hardware, power, cooling, and ROI experiments.
View sourceCustom Mixture-of-Experts (MoE) kernels that make trillion-parameter models available with cloud platform portability
Discusses MoE kernels enabling trillion-parameter models; debates local runnable models versus cloud portability; privacy concerns and forum naming criticisms online.
View source