Opening article
Hugging Face demonstrated accelerating the Qwen3-8B agent on Intel Core Ultra using OpenVINO.GenAI and speculative decoding. By pairing the target model with a depth-pruned Qwen3-0.6B draft model, inference speedups reached up to 1.4x. The optimized setup enables faster local AI agent workflows using frameworks like smolagents.
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×
To showcase the real-world potential, we deployed our optimized setup with the 🤗smolagents library. With this integration, developers can plug in Qwen3-8B (paired with our pruned draft) to build agents that call APIs and external tools, write and execute code, handle long-context reasoning and run efficiently on Intel® Core™ Ultra. The benefits aren’t limited to Hugging Face, this model pairing can also be used seamlessly with frameworks like AutoGen or QwenAgent, further strengthening the agentic ecosystem.
Here’s how it worked: 1. The agent used a web search tool to gather up-to-date information. 2. It then switched to the Python interpreter to generate slides with the python-pptx library. This simple workflow highlights just a fraction of the possibilities unlocked when accelerated Qwen3 models meet frameworks like 🤗smolagents, bringing practical, efficient AI agents to life on AI PC. Try it here 🚀
We wrapped this up by showing how these improvements can be used to run a fast, local AI Agent with 🤗 smolagents
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.
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
Improving the inference speed of agentic models like Qwen3-8B on consumer hardware makes local, responsive AI agents more practical for developers and end users.