Qwen3.6 35B A3B 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, quantized to 4-bit precision for efficient local deployment via Apple's MLX framework. The 4-bit quantization reduces memory footprint significantly, making it practical to run on consumer hardware, though with some trade-off in raw precision compared to full-weight variants.

Qwen3.6 35B A3B 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, quantized to 4-bit precision for efficient local deployment via Apple's MLX framework. The 4-bit quantization reduces memory footprint significantly, making it practical to run on consumer hardware, though with some trade-off in raw precision compared to full-weight variants.

Glossary

4-bit PrecisionA quantization level where model weights are stored using only 4 bits per value, significantly reducing model size at the cost of some accuracy.4-bit QuantizationA specific type of quantization that represents model weights using only 4 bits instead of the original 32 bits, enabling very efficient inference on consumer hardware.Bit PrecisionThe number of bits used to represent each number in a model; lower bit precision (like 3-bit) means smaller file size but potentially less accurate calculations.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.MLXA machine learning framework optimized for running models efficiently on Apple Silicon chips.MLX FrameworkA machine learning framework specifically designed for running AI models efficiently on Apple Silicon hardware.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.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.