Qwen3.6 35B A3B MLX 4bit

Qwen3.6

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

Released April 2026context N/A35B params

A mid-sized multimodal model that handles both text and image inputs, running efficiently in a 4-bit quantized MLX format suited for local Apple Silicon deployment. The quantization trades some precision for significantly reduced memory footprint, making it accessible on consumer hardware. It inherits Qwen3.6's architecture with a sparse mixture-of-experts design, activating around 3 billion parameters per forward pass despite a 35 billion total parameter count.

Capabilities

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

Factual Knowledge

Strong

Multilingual

Use Case Fit

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

Qwen3.6 35B A3B MLX 4bit

Qwen3.6

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

Released April 2026context N/A35B params

A mid-sized multimodal model that handles both text and image inputs, running efficiently in a 4-bit quantized MLX format suited for local Apple Silicon deployment. The quantization trades some precision for significantly reduced memory footprint, making it accessible on consumer hardware. It inherits Qwen3.6's architecture with a sparse mixture-of-experts design, activating around 3 billion parameters per forward pass despite a 35 billion total parameter count.

Capabilities

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

Factual Knowledge

Strong

Multilingual

Use Case Fit

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

Glossary

Apple SiliconApple's custom-designed processors (like M1, M2, M3) optimized for running machine learning models on Mac computers.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.MLXA machine learning framework optimized for running models efficiently on Apple Silicon chips.MLX FormatA model format designed specifically for efficient inference on Apple Silicon devices, optimized for the MLX machine learning framework.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.Multimodal ModelAn AI model that can process and understand multiple types of input data, such as video, images, and text together.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.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.