LLMTraining & AlignmentUpdated 2026.04.29

Quantization

Also known as모델 양자화INT8INT4GGUF

In one line

Quantization compresses model weights to lower precision (say, 16-bit down to 4-bit) so the same model fits on smaller GPUs and runs more cheaply.

Going deeper

Quantization shrinks an LLM's numerical weights into fewer bits — say, 16-bit floats down to 8-bit or 4-bit integers. Memory and compute drop sharply while answer quality usually holds. With 4-bit quantization, a 70B model fits on a single 48GB workstation GPU, or runs on a 24GB consumer card with offloading.

Marketers will not configure it directly, but quantization sits underneath two trends you do feel: on-device AI (assistants running on a phone) and low-cost private deployments (in-house LLMs on modest servers). Both are quantization stories at heart.

Push quantization too far and quality slips, especially on reasoning- or code-heavy tasks. If a deployment feels mysteriously dumber than the same model elsewhere, the quant level is worth checking before anything else.

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