
Base Model	Qwen3-VL-30B-A3B-Thinking (MoE: 31B total, ~3B active)
Quantization	NVFP4 (W4A4) — weights and activations
VRAM	~16GB (vs ~62GB BF16, ~31GB FP8)
What's quantized	Text/language backbone only
What's NOT quantized	Vision encoder, multi-modal projector, LM head, MoE gates (all remain BF16)

Usage with vLLM

Serving

bash

vllm serve OptimizeLLM/Qwen3-VL-30B-A3B-Thinking-NVFP4 \
    --tensor-parallel-size 1 \
    --max-model-len 8192 \
    --reasoning-parser-plugin qwen3_vl_reasoning_parser.py \
    --reasoning-parser qwen3_vl \
    --kv-cache-dtype fp8

Reasoning Parser

The included qwen3_vl_reasoning_parser.py is a custom vLLM reasoning parser plugin for this model. It extends vLLM's DeepSeekR1ReasoningParser and fixes an edge case where the model's answer can end up in reasoning_content instead of content when thinking is disabled.

Download it alongside the model and pass it via --reasoning-parser-plugin. Without it, you may see intermittent responses where content is null and the answer is in reasoning_content.

Python (OpenAI-compatible API)

python

from openai import OpenAI
import base64

client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")

with open("photo.jpg", "rb") as f:
    img_b64 = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="OptimizeLLM/Qwen3-VL-30B-A3B-Thinking-NVFP4",
    messages=[{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"}},
            {"type": "text", "text": "What do you see in this image?"},
        ],
    }],
    max_tokens=2048,
)
print(response.choices[0].message.content)

Thinking Control

The Thinking variant supports chain-of-thought reasoning. Control it per-request by appending to the text portion of your message:

/think — Enable detailed reasoning (complex visual analysis, comparisons)
/no_think — Disable thinking (fast perception, OCR, simple descriptions)

Example: "Describe this image. /no_think"

Quantization

Quantized with llm-compressor v0.9.0 using 512 multimodal calibration samples from neuralmagic/calibration (VLM split — image+text pairs, not text-only).

The full quantization script is included as quantize_vl.py. Notable details:

VLM calibration data — uses the VLM split (image+text pairs) instead of the LLM split (text-only). The text decoder sees different activation distributions when processing image-conditioned inputs, so text-only calibration is suboptimal for VL models.
glibc malloc trim fix — without mallopt(M_TRIM_THRESHOLD, 0), glibc arena bloat accumulates about 1.4GB per subgraph during calibration (~68GB phantom RSS across 49 subgraphs), enough to OOM a 128GB machine. The script includes this fix. Zero impact on quantization quality.
Minimum system RAM: 96GB recommended (62GB model + headroom). Quantization was performed on a 128GB system.

Hardware

GPU	VRAM	Notes
RTX PRO 6000 (96GB)	~16GB model + headroom	Recommended — full Blackwell NVFP4 acceleration
RTX 5090 / 4090 (24GB)	~16GB model, ~8GB headroom	Fits, but limited context/batch
A100 / H100 (80GB)	~16GB model + headroom	Works, but NVFP4 acceleration requires SM100+

Note: Full NVFP4 acceleration (W4A4 compute) requires Blackwell architecture (SM100+). On pre-Blackwell GPUs, vLLM uses weight-only quantization — still a memory savings, but without the activation quantization speedup.

Acknowledgments

Qwen Team for the base model
vLLM Project for llm-compressor and inference runtime
Neural Magic for the VLM calibration dataset