Does quantization always make it worse?

No. Around 4 bits the loss is small. But extreme compression below 2 bits can make quality drop sharply.

It is the quantized-model file format used by llama.cpp, designed to run efficiently on CPUs and unified memory.

Can Korean models be quantized?

Yes, but compression vulnerability varies by language; Korean can be more sensitive to aggressive quantization, so choosing the bit width matters.

VIDRAFT / Insights / LLM engineering

LLM engineering

What is model quantization — is a 4-bit LLM still smart?

Quantization lowers the numeric precision of a model's weights to cut size and compute. At around 4 bits the quality loss is small, which is the key to running large LLMs on a phone or laptop with no GPU — the core of on-device AI.

Published 2026-07-24About 2min readby VIDRAFT

Quick answer

Quantization lowers a model's weights from 16-bit toward about 4-bit, sharply cutting size and compute. Done well, the quality loss is small — which is what makes it possible to run large LLMs on a phone or laptop with no GPU. VIDRAFT POCKET runs on top of this.

What exactly is quantization?

It lowers the numeric precision used to store and compute a model's many weights. Representing a 16-bit float as a 4-bit integer, for example, shrinks the model about 4x and lowers memory-bandwidth demand, so it runs larger and faster on the same hardware.

A language model's weights mostly cluster in a narrow range, so lowering precision loses less information than you might expect. GGUF (llama.cpp) and MLX are the common formats that run such quantized models efficiently on CPUs and unified memory.

Won't 4-bit make it dumb?

Done well, the loss is small. It is widely confirmed that quality degradation around 4 bits is limited. But pushing too far (below 2 bits) makes loss rise sharply, and vulnerability differs by language.

The key is how you quantize. Techniques that keep important weights at higher precision, or calibration, preserve much of the quality at the same bit width. So even at the same 4 bits, results differ by method.

Why does it matter for on-device AI?

Without quantization, large models demand GPUs and large memory. At 4 bits, a 26-35B-class model shrinks to 10-20GB and fits in the unified memory of a GPU-less laptop or a phone. Quantization is the key that makes on-device AI real.

VIDRAFT POCKET ships quantized open models as GGUF and MLX and measured 2.69x faster CPU decoding than a leading on-device model at matched quality — showing quantization makes models not just smaller but faster.

What are the limits?

Extreme quantization collapses quality, and vulnerability varies by language and task. Languages with lower token efficiency, like Korean, can be more sensitive to aggressive compression, so the bit width must be chosen carefully between target device and quality.

In practice you measure where quality drops off a cliff and pick just before it. Past that knee, the model shrinks but becomes useless, so compression must always go together with measurement.

Frequently asked questions

Does quantization always make it worse?: No. Around 4 bits the loss is small. But extreme compression below 2 bits can make quality drop sharply.
What is GGUF?: It is the quantized-model file format used by llama.cpp, designed to run efficiently on CPUs and unified memory.
Can Korean models be quantized?: Yes, but compression vulnerability varies by language; Korean can be more sensitive to aggressive quantization, so choosing the bit width matters.

Sources

On-device AI

Can you run a large LLM without a GPU?

LLM engineering

Can you make an AI model smarter without training? Model merging

Physical AI

Can you teach a robot Korean without touching its firmware?

↖ Home — vidraft.net

This article is based on VIDRAFT's public, measured data and external sources. Performance figures are measurements under the stated conditions and may vary by environment.