The most efficient approach for a local installation is leveraging Docker containers.
Follow the step-by-step instructions below.
Be patient as the system self-retrieves massive model weights dynamically.
The installer diagnoses your environment to deploy the most compatible profile.
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 consumer‑grade 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 resource‑constrained environments. Additionally, the MLX optimizations reduce latency, providing smooth real‑time responses even on laptops and edge devices.
| Parameter | Value |
|---|---|
| Model Name | Qwen3.5-9B-MLX-4bit |
| Parameters | 9B |
| Quantization | 4‑bit |
| Framework | MLX |
| Context Length | 8K tokens |
| Inference Speed | >100 tokens/s (GPU) |
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