Holo3.1: Fast & Local Computer Use Agents

2026-06-02 · Source: Hugging Face - Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cloud Computing & IT Infrastructure · Depth: Advanced, short

Summary

The Holo3.1 family of computer-use models has been released, building on the Qwen family to enhance robustness across diverse environments, agent frameworks, and deployment targets. This update introduces quantized checkpoints, including FP8, Q4 GGUF, and NVFP4, specifically optimized for local inference. Holo3.1 significantly improves mobile automation, with the 35B-A3B model achieving 79.3% on AndroidWorld (up from 67%) and 4B/9B variants reaching 72% (from 58%). It also adds native function-calling support for cross-harness performance, showing over a 25% improvement in the Holotab product harness. New smaller models (0.8B, 4B, 9B) are available for cost-effective, private deployments. Quantized 35B-A3B checkpoints enable fast local inference, with NVFP4 W4A16 delivering 1.41x the throughput of FP8 and 1.74x of BF16 on DGX Spark, and a ~2x end-to-end speedup cutting average step time from 6.8s to 3.3s.

Key takeaway

For MLOps Engineers deploying computer-use agents, Holo3.1 offers critical advancements for robust, local, and cross-environment operations. If you struggle with performance across mobile, desktop, or varied agent frameworks, Holo3.1's quantized FP8, Q4 GGUF, and NVFP4 checkpoints provide efficient on-device inference. This allows you to expand agent capabilities to consumer hardware and private networks, reducing latency and deployment costs while maintaining performance.

Key insights

Holo3.1 enables robust, universal computer-use agents with efficient local inference across diverse environments.

Principles

• Agent performance varies significantly across deployment environments.
• Quantization techniques like FP8 and NVFP4 enable fast local inference.
• Robustness across GUI environments and agent harnesses is critical.

Method

Quantized checkpoints (FP8, Q4 GGUF, NVFP4) are released, with NVFP4 W4A16 using NVIDIA's Model Optimizer, combined with agent harness optimizations for speed.

In practice

• Deploy Holo3.1-0.8B for ultra-lightweight local agents.
• Utilize Q4 GGUF checkpoints for local inference on consumer hardware.
• Implement native function-calling for third-party agent stack integration.

Topics

• Holo3.1
• Computer Use Agents
• Local Inference
• Model Quantization
• Mobile Automation
• Agent Frameworks

Best for: AI Architect, AI Engineer, Machine Learning Engineer, MLOps Engineer

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• The Infrastructure Behind Making Local LLM Agents Actually Useful — Local LLM agents require deliberate infrastructure for performance and long-session stability beyond basic model serving.
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• Are Your AI Agents Flying Blind? The Truth About AgentOps — AgentOps provides a structured framework for managing, monitoring, and optimizing AI agents in production environments.
• Towards Scalable Lightweight GUI Agents via Multi-role Orchestration — LAMO enables lightweight MLLMs to perform scalable GUI automation via multi-role orchestration and a two-stage training recipe.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Hugging Face - Blog.