Qwen3.5 35B A3B FP8

Qwen 3

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

Released February 2026262K context≈ 196,608 words35B params

A mid-sized mixture-of-experts model from Qwen that punches above its active parameter count by activating only 3B parameters per forward pass despite having 35B total. It handles both text and images, making it multimodal out of the box. The FP8 quantization keeps memory footprint lean while preserving most of the full-precision capability.

Capabilities

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

Multimodal

Strong

Reasoning & Logic

Qwen3.5 35B A3B FP8

Qwen 3

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

Released February 2026262K context≈ 196,608 words35B params

A mid-sized mixture-of-experts model from Qwen that punches above its active parameter count by activating only 3B parameters per forward pass despite having 35B total. It handles both text and images, making it multimodal out of the box. The FP8 quantization keeps memory footprint lean while preserving most of the full-precision capability.

Capabilities

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

Multimodal

Strong

Reasoning & Logic

Glossary

Active Parameter CountThe number of model parameters that are actually used during inference for a given input, as opposed to the total parameters available.FP8 QuantizationA compression technique that reduces model size by representing weights using 8-bit floating-point numbers instead of higher precision, making it faster and more memory-efficient.Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.MultimodalA model that can process and understand multiple types of input, such as both text and images.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.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.

Capabilities

Capabilities

Use Case Fit

Glossary