Docker offers the quickest path to setting up this model locally.
Use the instructions provided below to complete the setup.
Completing the installation grants you full access to everything you hoped to achieve with this deployment.
The Qwen3.5-9B-MLX-4bit model delivers strong performance while maintaining a compact footprint thanks to its 9B parameters and 4-bit quantization. Its integration with the MLX framework enables optimized memory usage and accelerated inference on consumerāgrade hardware. The model supports an 8K token context window, allowing it to handle longer dialogues and complex reasoning tasks. Benchmarks show it achieves competitive perplexity scores compared to larger models, making it ideal for deployment in resourceāconstrained environments. Additionally, the MLX optimizations reduce latency, providing smooth realātime responses even on laptops and edge devices.
| Parameter | Value |
|---|---|
| Model Name | Qwen3.5-9B-MLX-4bit |
| Parameters | 9B |
| Quantization | 4ābit |
| Framework | MLX |
| Context Length | 8K tokens |
| Inference Speed | >100 tokens/s (GPU) |
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