Qwen3 Coder Next NVFP4

Qwen3

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

Released February 2026262K context≈ 196,608 words

A code-focused model from the Qwen3 family, quantized to NVFP4 precision by GadflyII for efficient inference on NVIDIA hardware. The reduced precision keeps memory footprint small and throughput high, though very fine numerical details may occasionally lose fidelity compared to full-precision variants. It handles code generation, debugging, and technical reasoning with the fluency characteristic of the Qwen3 Coder lineage.

Capabilities

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

Coding

Exceptional

Tool Use

Qwen3 Coder Next NVFP4

Qwen3

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

Released February 2026262K context≈ 196,608 words

A code-focused model from the Qwen3 family, quantized to NVFP4 precision by GadflyII for efficient inference on NVIDIA hardware. The reduced precision keeps memory footprint small and throughput high, though very fine numerical details may occasionally lose fidelity compared to full-precision variants. It handles code generation, debugging, and technical reasoning with the fluency characteristic of the Qwen3 Coder lineage.

Capabilities

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

Coding

Exceptional

Tool Use

Glossary

Code GenerationThe ability of a model to write, complete, or suggest programming code based on prompts or partial code input.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.NVFP4 PrecisionA low-precision numerical format optimized by NVIDIA that uses fewer bits per number than standard formats, enabling efficient inference on NVIDIA GPUs while maintaining reasonable accuracy.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Technical ReasoningThe capacity to work through complex logical problems, debug issues, and apply domain-specific knowledge systematically.ThroughputThe number of tokens a model can generate per second, measuring its processing speed.

Capabilities

Capabilities

Use Case Fit

Glossary