Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models

TL;DR:

• Qwen3-8B is one of the most exciting recent releases—a model with native agentic capabilities, making it a natural fit for the AIPC.

• With OpenVINO.GenAI, we’ve been able to accelerate generation by ~1.3× using speculative decoding with a lightweight Qwen3-0.6B draft.

• By using speculative decoding and applying a simple pruning process to the draft, we pushed the speedup even further to ~1.4×

• We wrapped this up by showing how these improvements can be used to run a fast, local AI Agent with 🤗 smolagents

Qwen3

Qwen3-8B is part of the latest Qwen family, trained with explicit agentic behaviors. It supports tool invocation, multi-step reasoning, and long-context handling capabilities, that make it well-suited for complex agent workflows. When integrated with frameworks like Hugging Face 🤗smolagents, QwenAgent, or AutoGen, it enables a wide range of agentic applications built around tool use and reasoning. Unlike single-turn chatbots, agentic applications rely on reasoning models that produce “thinking aloud” traces, intermediate steps that expand token usage, making inference speed critical to responsiveness. The combination of optimized inference and built-in agentic intelligence makes Qwen3-8B a compelling foundation for next-gen AI agents.

Accelerating Qwen3-8B on Intel® Core™ Ultra with Speculative Decoding

We started by benchmarking the 4-bit optimized OpenVINO version of Qwen3-8B on an Intel Lunar Lake integrated GPU, establishing this as our baseline for further acceleration

Speculative decoding is a method to speed up auto-regressive generation. It works by using a smaller, faster model as a draft to propose multiple tokens in a single forward pass, which are then validated by the larger target model in one forward pass. In our setup, Qwen3-8B served as the target model while Qwen3-0.6B was used as the draft. This approach delivered an average of 1.3× speedup over the baseline.