This model has been quantized using llm-compressor v0.10.1.dev31+geb49917e (just after Qwen3.5 support was merged) and transformers v5.3.0. It is based on the official example with a few modifications (see next section). The sequence length has been increased from 4096 to 8192 and the number of samples from 256 to 1024. The 1024 samples come from 4 differents datasets: * 256 general conversation samples (UltraChat) * 256 math reasoning samples (GSM8K) * 256 code samples (CodeAlpaca) * 256 multilingual samples (Aya)

Visual QA

Input Text · Image→ Output Text

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Qwen3-Coder-30B-A3B-Instruct-NVFP4

ig1

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation

Input Text→ Output Text

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Qwen3-VL-30B-A3B-Instruct-NVFP4

ig1

NVFP4 quantization using llm-compressor v0.8.2.dev28+g0f346cf7 (and transformers v4.57.1) based the officiel NVFP4 example script for Qwen3-VL-235B-A22B-Instruct. As of v0.13.0, special execution configuration is not needed anymore. You can simply launch it this way: The v0.13.0 is fixed because it has been tested on our side, feel free to try with newer version of vLLM when they came out.

Visual QA

Input Text · Image→ Output Text

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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