vllm-qwen35-optimization

Document PR-backed optimizations for Qwen3.5 in vLLM deployments.

721|65|Updated Apr 1, 2026

Search Tags:#optimization #quantization #vllm #moe #pr-diff-dossier #eagle3 #qwen3_5

Ready to run

npx skills add https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS --skill vllm-qwen35-optimization

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vllm-qwen35-optimization
Source: https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS/tree/main/skills/model-optimization/vllm/vllm-qwen35-optimization
Command: npx skills add https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS --skill vllm-qwen35-optimization

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides a structured, PR-backed approach to optimizing Qwen3.5 in vLLM, covering dense and MoE variants, GDN fusion, FP8/NVFP4 quantization, LoRA, Eagle3, and associated runtime changes, so teams can track improvements and reproduce optimizations.

Core Features & Use Cases

PR-dossier driven optimization guides for Qwen3.5 in vLLM (dense, MoE, and Eagle3 paths).
Porting, validating, and documenting Qwen3.5 configs across vLLM deployments with quantization and LoRA adjustments.
Reproducible evidence workflow using PR histories, landed PRs, and runtime surfaces to validate changes.

Quick Start

Use the included guidelines to audit PR diffs and apply Qwen3.5 optimizations to your vLLM setup.

Frequently Asked Questions about vllm-qwen35-optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I optimize Qwen3.5 in vLLM using PR-backed changes?▼

To optimize Qwen3.5 in vLLM, audit PR diffs and apply documented configurations for dense and MoE variants, GDN fusion, FP8/NVFP4 quantization, LoRA, and Eagle3 to ensure reproducible runtime improvements.

Does vLLM support FP8 and NVFP4 quantization for Qwen3.5 MoE models?▼

Yes, vLLM supports FP8 and NVFP4 quantization for Qwen3.5 dense and MoE variants. You can apply these quantization adjustments alongside GDN fusion and LoRA configurations tracked through landed PR histories.

What is the best way to track reproducible vLLM optimizations for Qwen3.5?▼

The best way to track reproducible vLLM optimizations is by enforcing diff-audit discipline using PR histories and landed changes. This dossier-driven approach validates runtime modifications and ensures auditable improvements across deployments.

Can I use Eagle3 and LoRA adjustments together when configuring Qwen3.5 in vLLM?▼

Yes, you can apply Eagle3 runtime changes and LoRA adjustments together when configuring Qwen3.5 in vLLM. The optimization workflow validates these combined paths against mainline deployments to ensure stability.

How does GDN fusion improve Qwen3.5 performance in vLLM deployments?▼

GDN fusion improves Qwen3.5 performance in vLLM deployments by optimizing runtime execution paths. Documenting these fusion changes through PR-dossier workflows enables auditable and reproducible inference improvements.

What are the limitations of applying unverified PR diffs to vLLM Qwen3.5 setups?▼

Applying unverified PR diffs to vLLM Qwen3.5 setups risks runtime instability. Enforcing diff-audit discipline and validating changes against mainline vLLM deployments mitigates risk and ensures production-grade requirements are met.