Running Your First Local LLM on a 6–8 GB GPU: A Scientific Guide to Small Models
Running Your First Local LLM on a 6–8 GB GPU: A Scientific Guide to Small Models Synopsis: This guide describes practical, reproducible steps to run a compact language model (2B–7B parameter class) on a consumer GPU with ~6–8 GB VRAM. It focuses on minimal dependencies, quantization for memory reduction, and objective benchmarking so you get useful output while preserving reproducibility and safety. Why this approach works Large models (tens to hundreds of billions of parameters) require large memory and specialized hardware. Smaller models (2B–7B) combined with quantization (4-bit or 8-bit) and device mapping permit reasonable latency and task utility on 6–8 GB GPUs. The underlying scientific principles are: Model scaling law tradeoffs: smaller models have less representational capacity but are still effective for many tasks when used with retrieval or fine-tuned heads. Quantization: reduces the memory footprint by representing weights wit...