# W4A4 and W4A8: The New Quantization Toolkit for Squeezing More Out of a Checkpoint

- Published: 18 September 2026
- Updated: 18 September 2026
- Author: RenderBob team
- Category: Technical
- Canonical: https://renderbob.io/blog/w4a4-w4a8-quantization-toolkit

ComfyUI's Quantization Toolkit, formerly the INT8 Toolkit, adds native W4A4, W4A8 and W8A8 quantisation, including LoRA support. Here is what the notation means and how to pick a mode.

ComfyUI's Quantization Toolkit, formerly published as the INT8 Toolkit, brings native support for quantising diffusion models to W4A4, W4A8 or W8A8, loading pre-quantised checkpoints, applying quantisation-aware LoRAs, and tuning low-VRAM inference. It is a more systematic, mode-based update to the quantisation stack this blog has covered piecemeal with FP8 and NVFP4.

## Decoding the notation

W4A4 means 4-bit weights and 4-bit activations. W8A8 means 8-bit weights and 8-bit activations. W4A8 is an asymmetric middle ground: 4-bit weights paired with 8-bit activations. Weights are the model's learned parameters, static and quantised once. Activations are the intermediate values flowing through the network during a forward pass, quantised on the fly. Lower bit-widths save more VRAM and run faster, and they push harder on quality. Activations are typically more sensitive to aggressive quantisation than weights, which is why W4A8 exists as a deliberate middle option.

## How the toolkit tells you to choose

Start with int8, the simplest, most broadly compatible W8A8 path, for general use. Move to int4_mixed, an architecture-aware mix of W4A4 and W8A8 that applies the more aggressive format only where the model can tolerate it, when memory pressure justifies it. Treat w4a8 as experimental: it is a distinct kernel format (asymmetric 4-bit weights with ConvRot INT8 activations, falling back to W8A8 on incompatible shapes), not a preset inside int4_mixed. Test it deliberately rather than assuming it slots in as a drop-in upgrade.

LoRA compatibility is the studio-critical detail. The toolkit supports applying LoRAs to an already-quantised model, with standard, stochastic-requantised, and dynamic-runtime modes for how that interaction is handled. A studio that has invested in character or brand LoRAs needs those adapters to keep working when the base model is running quantised for VRAM reasons. A toolkit that treats this as a first-class path removes a real risk from adopting aggressive quantisation on production checkpoints.

Quantisation mode is a quality-versus-VRAM trade-off that should be made once, benchmarked against real production shots, and applied consistently. Pin the tested mode in the studio toolkit rather than rediscovering it on every new project.