Quantization (LLM)

Quantization is the process of reducing the numerical precision used to store a large language model's weights, shrinking the model so it fits in less memory and runs faster. A model trained at 16-bit precision can be quantized to 8-bit, 4-bit, or lower, cutting its memory footprint by half or more. This is the single most important technique for running capable local models on consumer hardware instead of renting cloud GPUs.

The precision-versus-quality trade-off

Lower precision means each weight is stored with fewer bits, which reduces VRAM use and increases speed, but it can slightly degrade output quality. Modern quantization formats — such as GGUF quant levels (for example Q4_K_M or Q5_K_M) and methods like GPTQ and AWQ — are designed to minimize that loss. In practice, 4-bit and 5-bit quants of a larger model often outperform an unquantized smaller model that uses the same memory, so quantization frequently improves real-world results on a fixed hardware budget.

Why it matters for sovereign AI

Quantization is what makes running an LLM locally practical: a quantized model can run entirely on a self-owned GPU with no data leaving your premises. For Bitcoiners extending self-custody to their AI tooling, it is the difference between depending on a cloud API and owning the full stack.

Check which models fit your card on D-Central's GPU and LLM compatibility guide.