Run Qwen3.5-9B-MLX-4bit

Deploying this model locally is quickest when done via Docker.

Refer to the instructions below to proceed.

Completing this setup means you now possess absolutely everything you wanted to obtain from the platform.

🗂 Hash: b53ca57cb76621c79f7bd22e63c8c14c • Last Updated: 2026-06-26 Verify Processor: 6-core 3.5 GHz minimum required RAM: 64 GB to avoid OOM crashes on large contexts Storage:100 GB free space for HuggingFace cache folder Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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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