Qwen3 235B A22B NVFP4

Name: Qwen3 235B A22B NVFP4
Author: NVIDIA

by NVIDIAQwen 3

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released July 202541K context≈ 30,720 words235B params

A large mixture-of-experts model that activates 22 billion parameters out of 235 billion total, keeping inference costs lower while preserving the reasoning depth of a much larger system. It handles complex text tasks with strong multilingual coverage, a hallmark of the Qwen 3 family. The NVFP4 quantization from NVIDIA trades a small amount of precision for significantly faster throughput on compatible hardware.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multilingual

Moderate

Instruction Following

Qwen3 235B A22B NVFP4

by NVIDIAQwen 3

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released July 202541K context≈ 30,720 words235B params

A large mixture-of-experts model that activates 22 billion parameters out of 235 billion total, keeping inference costs lower while preserving the reasoning depth of a much larger system. It handles complex text tasks with strong multilingual coverage, a hallmark of the Qwen 3 family. The NVFP4 quantization from NVIDIA trades a small amount of precision for significantly faster throughput on compatible hardware.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multilingual

Moderate

Instruction Following

Glossary

CoverageThe extent to which training data represents all relevant aspects or regions of a document or domain.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.MultilingualA model trained to understand and generate text in multiple languages, not just English.Multilingual CoverageThe ability of a model to understand and generate text in multiple languages, typically because it was trained on data from many different languages.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning DepthA model's ability to perform complex multi-step logical thinking and problem-solving; typically increases with model size.ThroughputThe number of tokens a model can generate per second, measuring its processing speed.

Capabilities

Capabilities

Use Case Fit

Glossary