Qwen3.5 27B 4bit

Qwen 3

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

Released February 2026262K context≈ 196,608 words27B params

A mid-sized multimodal reasoner that processes both text and images, quantized to 4-bit precision for efficient local deployment via MLX. The compression keeps memory footprint lean while preserving much of the original model's capability, though fine details may soften compared to full-precision runs. It handles visual and textual inputs in a single pass, making it practical for on-device workflows where resource constraints matter.

Capabilities

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

Factual Knowledge

Strong

Multilingual

Qwen3.5 27B 4bit

Qwen 3

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

Released February 2026262K context≈ 196,608 words27B params

A mid-sized multimodal reasoner that processes both text and images, quantized to 4-bit precision for efficient local deployment via MLX. The compression keeps memory footprint lean while preserving much of the original model's capability, though fine details may soften compared to full-precision runs. It handles visual and textual inputs in a single pass, making it practical for on-device workflows where resource constraints matter.

Capabilities

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

Factual Knowledge

Strong

Multilingual

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.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.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.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.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.On-DeviceA model designed to run directly on a user's device (phone, laptop, etc.) rather than requiring a remote server.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.

Capabilities

Capabilities

Use Case Fit

Glossary