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Hugging Face
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Local Computer Use Agents

5 posts · Mar 2026 – Sep 2026

What this trail covers

Hugging Face now supports local computer use agents, enabling agents to run directly on a user's machine for faster and more private interactions. This capability focuses on bringing AI agent functionality to the edge, reducing reliance on cloud infrastructure and improving responsiveness. The Holo3.1 collection showcases this by offering a curated set of agents designed for local execution. Holotron-12B, a multimodal computer-use model, demonstrates high-throughput inference with a hybrid SSM architecture. This post details the creation of smaller, specialized models for local execution, such as the Qwen-Image 2.1-PE-T2I-Pocket-0.8B for prompt rewriting and the Qwen-Image 2.1-viewpoint-orbit-LoRA for camera-angle control, demonstrating a workflow for generating custom models tailored for specific tasks and devices.

All Hugging Face trails
  1. Holo4: powering generalist computer-use agentsIntroduces Holo4-27B, a large image-text-to-text model that powers generalist computer-use agents. This model enables agents to understand and interact with visual information and perform a wide range of tasks on a local machine, building upon the existing capability for local computer use agents.
  2. The model that didn't exist, so you made it yourselfThis post details a workflow for creating custom, smaller AI models for local execution using an AI assistant (ML Intern). It showcases the creation of a 0.8B parameter model for prompt rewriting (Qwen-Image 2.1-PE-T2I-Pocket-0.8B) and a LoRA for camera-angle control (Qwen-Image 2.1-viewpoint-orbit-LoRA). The process involves defining requirements, generating training data (e.g., 8,797 requests for the pocket rewriter, 24,722 images for camera-angle LoRA), fine-tuning on Hugging Face hardware, and evaluating the results. The post highlights cost-effectiveness (e.g., USD 16 for the camera-angle LoRA project) and the ability to create specialized models that fit on consumer devices.
  3. Holo3.1: Fast & Local Computer Use AgentsThis post introduces the Holo3.1 collection, which showcases local computer use agents. It highlights the Holotron-12B model, a multimodal computer-use model capable of high-throughput inference with a hybrid SSM architecture. The post also mentions the integration of GGML and llama.cpp to enhance the local AI inference stack, streamlining the process of shipping models from the Transformers library to llama.cpp for local execution and improving packaging and user experience for GGML-based software.
  4. AutoSynthData: Generating Training Data for Enterprise AgentsThis post introduces AutoSynthData, a system for generating synthetic training data for enterprise agents. It details the process of identifying agent capability gaps through model failures and teacher successes, then generating and validating new tasks. The system uses a `(system specification, user prompt, verifier)` abstraction for tasks and emphasizes feasibility, realism, and difficulty for training signal. The implementation is illustrated with EnterpriseOps Gym experiments, including hybrid and ITSM scenarios, and highlights the importance of sample-level verification and batch-level review for high-quality synthetic data.
  5. Reachy Mini goes fully localThis post details the practical implementation of running a full speech-to-speech AI agent pipeline locally, integrating with the Reachy Mini robot. It provides specific commands and configurations for locally serving an LLM using llama.cpp (Gemma 4-E4B-it-GGUF) with optimized flags, and running the `speech-to-speech` library with recommended VAD (Silero), STT (Parakeet-TDT), and TTS (Qwen3-TTS) components. It also outlines how to connect the Reachy Mini's conversation app to this local backend, demonstrating a fully offline AI agent system.
  7. Holotron-12B - High Throughput Computer Use AgentThis post introduces Holotron-12B, a multimodal computer-use model built on NVIDIA's Nemotron architecture. It highlights the model's hybrid State-Space Model (SSM) and attention mechanism, which optimizes for high-throughput serving and long-context inference by avoiding the quadratic cost of full attention. The post details the model's training process, its performance improvements on agent benchmarks (WebVoyager, OS-World-G, GroundUI, WebClick) compared to the Nemotron base model and Holo2-8B, and its inference efficiency gains (over 2x higher throughput on WebVoyager with vLLM optimizations). It also discusses the model's ability to scale efficiently with increasing concurrency, achieving 8.9k tokens/s at 100 concurrent workers.