Why 2-Bit LLM Quantization Fails at the Hardware Boundary
by Vivek Kumar
The jump from 4-bit to 2-bit weight quantization looks small on a slide. In an implementation, it changes the problem. A naive weight-only 2-bit scheme gives each scaling group just four weight codes. The margin for an unlucky scale, an outlier or a slightly different rounding path nearly disappears.
That is why a model can look acceptable in a server-side simulator and still fail on a custom edge accelerator. The issue is not simply the compression ratio. At W2, the representation, training procedure, compiler and device runtime become one numerical system. If those parts are tuned independently, the model may preserve aggregate accuracy while losing the exact behaviors that matter in production.
The 4-Bit Playbook Breaks at 2 Bits
Most post-training quantization workflows assume that a useful local approximation produces an acceptable global model. Calibrate representative data, choose per-channel or per-group scales, round the weights and verify...
Copyright of this story solely belongs to hackernoon.com. To see the full text click HERE
