Quantization

The maturation of quantization compression (INT4/INT8), sparsification, and efficient small-parameter architectures has made models in the 1B–10B range practically useful.

The appeal of running large models locally is that data never leaves the machine, it can be used offline, there is no pay-per-use billing, and it can be freely fine-tuned; the cost is that only models with smaller parameter counts or 4-bit quantized models can be run, and both inference speed and context length are limited.

Because the built-in compute power of the screwdriver is limited, vision models often need quantization and compression or rely on a smartphone for coordinated inference, which directly affects recognition speed and accuracy.

A common industry approach to running local models in the browser is to use quantization to compress model size and leverage browser graphics interfaces such as WebGPU to accelerate matrix operations.

Locally run coding models must account for engineering constraints such as quantization compression, inference speed, and memory footprint, so "laptop-sized" means the model's parameter scale is usually small (hundreds of millions to a few billion).

The core problems that inference frameworks solve include efficiently loading model weights into VRAM, scheduling concurrent requests, KV Cache management, and quantization compression to reduce VRAM usage.

The technical drivers of capability compression include knowledge distillation, quantization compression, and task-specific fine-tuning.

AI photo-retouching products supporting millions of DAUs need to control per-inference costs through model distillation, quantization compression, and edge-cloud collaboration.

Technologies such as quantization compression (INT4/INT8 quantization), speculative decoding, and KV Cache optimization have enabled some vendors to reduce inference costs by more than 80% compared to two years ago.