Running this model locally is fastest when deployed through Docker.

Use the instructions provided below to complete the setup.

The setup auto-streams the model assets (expect a multi-GB download).

During setup, the script automatically determines and applies the best settings tailored to your machine.

🔧 Digest: e2ffe4a2de9a12fd6ba0946d7109adc2 • 🕒 Updated: 2026-06-28 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: high memory bandwidth GPU for next-gen local AI pipeline

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.

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