Qwen3.5-9B-MLX-4bit For Low VRAM (6GB/8GB)

The most rapid route to a local installation of this model is through Docker.

Follow the guidelines below to continue.

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.

📦 Hash-sum → f69525b3e148e2497cdbf726ddd3cd11 | 📌 Updated on 2026-06-27
  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk Space: 100 GB for multi-modal model vision components
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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