Qwen3.5 9B MXFP8 FP8KV FP8Attn CT AutoRound

Qwen 3

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

Released September 2026context N/A

A quantized variant of Qwen3.5 9B that trades a small amount of numerical precision for reduced memory footprint and faster inference. The MXFP8 weight quantization combined with FP8 key-value cache and FP8 attention means nearly every compute-heavy operation runs in lower precision, calibrated via AutoRound. Expect behavior very close to the base model, though edge cases involving subtle numerical distinctions may occasionally differ.

Capabilities

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

Instruction Following

Strong

Reasoning & Logic

Strong

Factual Knowledge

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Qwen3.5 9B MXFP8 FP8KV FP8Attn CT AutoRound

Qwen 3

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

Released September 2026context N/A

A quantized variant of Qwen3.5 9B that trades a small amount of numerical precision for reduced memory footprint and faster inference. The MXFP8 weight quantization combined with FP8 key-value cache and FP8 attention means nearly every compute-heavy operation runs in lower precision, calibrated via AutoRound. Expect behavior very close to the base model, though edge cases involving subtle numerical distinctions may occasionally differ.

Capabilities

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

Instruction Following

Strong

Reasoning & Logic

Strong

Factual Knowledge

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

AttentionA mechanism that lets the model focus on relevant parts of the input when generating each output token.AutoRoundAn automated quantization method that intelligently rounds weights to lower precision while minimizing the loss in model performance.AutoRoundAn advanced quantization method that automatically optimizes how model weights are rounded to lower precision, preserving accuracy better than simpler quantization approaches.Base ModelA pretrained model that completes text patterns but hasn't been trained to follow instructions, serving as a starting point for customization through fine-tuning.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Key-Value CacheA memory optimization technique that stores pre-computed key and value matrices during text generation to avoid recalculating them for each new token.MXFP8A specialized 8-bit floating-point format designed for efficient neural network computation while maintaining reasonable accuracy.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.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.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.Weight QuantizationA specific type of quantization that compresses only the model's learned parameters (weights) while keeping other calculations at higher precision.