---
license: apache-2.0
pipeline_tag: image-text-to-text
base_model_relation: quantized
tags:
- nvfp4
- comfyui
- text-encoder
- blackwell
base_model:
- Qwen/Qwen3-4B-Instruct-2507
language:
- en
- zh
---

# Qwen3-4B-Instruct-NVFP4

## Overview

This repository provides an **NVFP4 (FP4 E2M1) mixed-precision** quantized build of
[Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
in [ComfyUI](https://github.com/comfyanonymous/ComfyUI) `comfy_quant` format,
primarily intended for use as a **text encoder** (e.g. for Z-Image).

Many thanks to [SergiusFlavius](https://huggingface.co/SergiusFlavius), who helped me generate this quantization.

## Quantization Details

- **Backend:** ComfyUI `convert_to_quant` v1.2.6 (`comfy_kitchen` CUDA NVFP4 kernels)
- **Format:** `comfy_quant` mixed precision
  - **NVFP4** (FP4 E2M1, 16-element blocks, learned rounding) — text transformer
    blocks 2–33 projections only
  - **FP8** (`float8_e4m3fn`, tensorwise, learned rounding) — text blocks 1 & 34
  - **FP16** (`bfloat16`) — `embed_tokens`, `model.norm`, all norms/biases,
    `pos_embed`, `patch_embed`, text blocks 0 & 35, **and the entire vision encoder**
    (blocks, merger, `deepstack_merger_list`)
- **No `input_scale`:** ComfyUI quantizes activations **dynamically** at runtime
  (per-tensor amax via the NVFP4 layout), so a baked-in activation scale is not
  required. This matches the stock Qwen3-VL NVFP4 baseline.
- **Hardware target:** NVIDIA Blackwell (RTX 50 series, SM 12.0) — **required** for
  native NVFP4 tensor-core execution (see Runtime below).
- **Size:** 4.03 GB / 1413 tensors (vs 8.88 GB FP16 source — **~55% smaller**).

### Layer breakdown

| Tier | Layers | Count |
|------|--------|-------|
| FP16 (bf16) | `embed_tokens`, `model.norm`, all norms/biases, `pos_embed`, `patch_embed`, text blocks 0 & 35, **entire vision encoder** | kept lossless |
| FP8 (e4m3fn, tensorwise) | text blocks 1 & 34 | 14 weights |
| NVFP4 (E2M1, block=16) | text blocks 2–33 projections | 224 weights |

## Runtime / hardware

How the layers execute depends on the GPU (ComfyUI `pick_operations` gates each
format on the device capability):

| GPU (SM) | NVFP4 layers | FP8 layers |
|----------|--------------|------------|
| RTX 5090 / Blackwell (12.0) | **native FP4 tensor cores** (fast) | native FP8 |
| RTX 4090 / Ada (8.9) | dequantized to bf16 (size only, no speedup) | native FP8 |

On Blackwell, NVFP4 weights stay packed and run through `comfy_kitchen`'s
`TensorCoreNVFP4Layout` GEMM (FP4 weight × dynamically-quantized FP8 activation).
On non-Blackwell GPUs the format is emulated via dequantization, so you get the
smaller footprint but not the FP4 speedup.

## Usage (ComfyUI)

Place `qwen3_vl_4b_instruct_nvfp4.safetensors` in `ComfyUI/models/text_encoders/`.

- **As Z-Image text encoder:** load with a `CLIPLoader` node, type `lumina2`.

ComfyUI auto-detects the quantization metadata (`Found quantization metadata
version 1`) and selects `MixedPrecisionOps` for the text encoder.


## License

Apache-2.0 (inherited from the base model).