GGUF Quantization Demystified: Practical Strategies for Deploying LLMs on Consumer GPUs

Mastering GGUF quantization is essential for running large language models on consumer-grade GPUs, as the right precision level determines whether models fit in memory and perform effectively. This guide replaces trial-and-error deployment with concrete formulas and practical decision tables, enabling developers to maximize model quality within real hardware constraints. By understanding quantization tradeoffs, organizations can deploy state-of-the-art models locally rather than relying on cloud services, dramatically reducing inference costs and latency.