A practical setup guide for running local LLMs on GPUs with approximately 10GB of usable VRAM (RTX 4070 or RTX 4060 Ti 16GB). Covers how to calculate effective VRAM budget after OS and KV cache overhead, model selection (Llama 3.1 8B, DeepSeek Coder 6.7B, Mistral 7B), GGUF quantization levels (Q4_K_M through Q8_0) and their trade-offs, Ollama installation and configuration via custom Modelfiles, a Python benchmarking script targeting 30–45 tokens/sec, IDE integration with the Continue extension, RAG setup with ChromaDB, OpenAI-compatible API usage, and troubleshooting for OOM errors, slow performance, and degraded output quality.
Table of contents
How to Set Up a Local LLM on 10GB VRAMTable of ContentsWhy 10GB VRAM Is the New Sweet Spot for Local LLMsHardware Reality Check: What 10GB VRAM Actually MeansModel Selection: Choosing the Right LLM for Your VRAMInstalling Ollama and Pulling Your First ModelPerformance Optimization: Hitting 30 to 45 Tokens/SecPractical Use Cases: Putting Your Local LLM to WorkTroubleshooting Common 10GB VRAM IssuesImplementation Checklist and Next StepsSort: