Intel and Hugging Face demonstrated that running agents based on large models on a personal PC is no longer just theory: combining speculative decoding with a depth-pruned draft model speeds up Qwen3-8B on Intel Core Ultra processors, reducing latency and making local agents more viable. (huggingface.co)

What they did exactly

The team took Qwen3-8B as the target model and used Qwen3-0.6B as a draft to apply speculative decoding: the draft proposes several tokens in one pass and the target model validates those proposals in a single pass. In their base setup this yielded about a 1.3× speedup versus the baseline on an Intel integrated GPU. (huggingface.co)

Sound abstract? Think of the draft as someone jotting quick ideas and the final author reviewing them in bulk. If the draft is much faster, the whole process becomes faster too.

How they pushed the improvement further

The researchers noticed that model depth (layers) has a big impact on latency. They depth-pruned the Qwen3-0.6B draft, removing 6 of its 28 layers, then fine-tuned that pruned draft with synthetic data generated by Qwen3-8B (using prompts from a large dataset). The pruned draft added another boost: roughly a 1.4× total speedup relative to the baseline. (huggingface.co)

This illustrates a powerful idea: you don't always need a new chip to get faster results; sometimes adjusting the small model that helps "write" the output speeds up the whole system.

Code and implementation

The integration was built on OpenVINO.GenAI and the demo shows how to instantiate the pipeline with a draft:

from openvino_genai import LLMPipeline, draft_model

model = LLMPipeline(target_path, device, draft_model=draft_model(draft_path, device))

Before running, both models must be converted to OpenVINO; the article includes instructions and a reproducible notebook to follow step by step. (huggingface.co)

Real case: local agents with smolagents

To show practical value, they built an agent with the smolagents library that: 1) searched the web for information, 2) switched to the Python interpreter, and 3) generated slides with python-pptx. In other words, an agent flow that adapts, executes, and produces useful artifacts — all on a machine with Intel Core Ultra. This shows the move from faster models to practical agents. (huggingface.co)

Can you imagine running an assistant that writes code and presents results on your laptop without sending anything to the cloud? That's exactly the goal they're enabling.

Limitations and practical warnings

The reported results depend on the exact configuration: OpenVINO 2025.2, an Intel Core Ultra 7 268V with integrated Arc 140V GPU and 32 GB of RAM. Performance can vary with hardware, drivers, and inference settings. This isn't a universal guarantee; it's a reproducible guide and a starting point. (huggingface.co)

Also: reducing layers and using weaker drafts involves a trade-off between speed and quality that you should evaluate for your own tasks.

What can you try now?

If you want to experiment, the article links a notebook and the pruned draft model to reproduce the results. Good practice: follow the notebook, measure on your own machine, and adjust the speculation window and draft size.

For developers and founders this opens real opportunities: faster local agents, less cloud dependency, and prototypes that work on modern laptops.