The fastest method for installing this model locally is by using Docker.
Follow the guidelines below to continue.
The installer will automatically analyze your hardware and select the optimal configuration for your system.
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鈥慻rade 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鈥慶onstrained environments. Additionally, the MLX optimizations reduce latency, providing smooth real鈥憈ime responses even on laptops and edge devices.
| Parameter | Value |
|---|---|
| Model Name | Qwen3.5-9B-MLX-4bit |
| Parameters | 9B |
| Quantization | 4鈥慴it |
| Framework | MLX |
| Context Length | 8K tokens |
| Inference Speed | >100 tokens/s (GPU) |
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