Run Qwen3.5-9B-GGUF with 1M Context

Run Qwen3.5-9B-GGUF with 1M Context

If you want the fastest local installation for this model, use Docker.

Please follow the instructions listed below to get started.

The setup auto-downloads all needed files (several GBs).

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

📘 Build Hash: d9f964c35c545313085dfc2ca26da350 • 🗓 2026-06-26
  • Processor: next-gen chip for heavy context processing
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

The Qwen3.5-9B-GGUF model represents a significant advancement in open‑source language models, offering a balanced blend of performance and efficiency for both research and commercial applications. Built on the Qwen3.5 architecture, it leverages grouped‑query attention and rotary positional embeddings to achieve faster inference while maintaining high accuracy on benchmarks. With 9 billion parameters quantized into GGUF format, the model reduces memory footprint and enables deployment on consumer‑grade hardware without sacrificing response quality. The model supports up to 8K token context windows, allowing it to handle longer dialogues and complex reasoning tasks with minimal truncation. Its integration with the GGUF format further simplifies deployment across diverse platforms, making advanced AI capabilities accessible to a broader community.

Context Length 8K tokens
Training Tokens 2 trillion
Benchmark (MMLU) 84.3%
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