Qwen3.5-9B-W4A16-AutoRound-AWQ

Vishva007/Qwen3.5-9B-W4A16-AutoRound-AWQ

This is a W4A16 (4-bit weight, 16-bit activation) AWQ-format quantized version of Qwen/Qwen3.5-9B, produced using AutoRound — Intel's sign gradient descent based quantization method designed for production-grade accuracy retention.

Quantization Details

Key Notes

• AWQ format — Exported in the standard AWQ format, optimized for efficient inference with activation-aware weight clipping.
• High accuracy configuration — 800 iterations with 512 calibration samples targets production-grade quality with minimal degradation from the base model.
• W4A16 — Weights are quantized to 4-bit integers; activations remain in FP16 for inference stability.
• ~50% memory reduction compared to the FP16 base model, enabling deployment on consumer and mid-range GPUs.

Usage

This model is compatible with transformers, AutoAWQ, vLLM, and SGLang — any backend supporting AWQ-format weights works out of the box. For full model details, architecture, and capabilities, refer to the base model page.

🚀 Deploy on RunPod

One-click launch environments pre-configured with PyTorch, CUDA, and dependencies for fine-tuning or quantization.

🎁 Need GPU compute? Sign up via RunPod and get $5–$500 in free credits when you add your first $10.

PyTorch 2.14

PyTorch 2.13

PyTorch 2.12

---