Holo3.1: Fast & Local Computer Use Agents

Have you ever imagined letting an AI operate your computer—opening browsers, filling forms, or even controlling mobile apps? That's the promise of Computer Use Agents. Over the past year, we've seen plenty of impressive demos, but very few solutions that work reliably across diverse environments. The release of Holo3.1 might finally change that.

From Holo3 to Holo3.1: Users weren't complaining about capability, but about deployability.

Back in March, Hcompany's Holo3 was hailed as a state-of-the-art computer-use model, topping benchmarks like OSWorld. But the team quickly realized that developers' real pain points weren't a few percentage points of task success—it was that the model only shone in narrow conditions: strong on one desktop browser but fragile when switching frameworks, clueless on mobile, and impossible to deploy privately.

In other words, an agent that only excels in a contrived setting is still a lab toy. Holo3.1 directly targets these three 'usability' gaps: environment diversity, framework compatibility, and deployment flexibility.

A three-dimensional upgrade: the makings of a production-grade agent.

First, environment agnosticism—mobile is no longer a second-class citizen. Holo3.1's score on AndroidWorld jumped from 67% to 79.3%, with its 35B-A3B variant approaching human-level performance. This comes from extensive mobile interaction data and fine modeling of touch and swipe gestures. With so much business now conducted on phones, an agent that can't handle mobile can hardly be called universal.

Second, framework agnosticism—works with whatever agent harness you use. Many teams building custom agents use scaffolding like LangChain or AutoGPT, but the underlying model often doesn't support the required interaction protocol. Holo3.1 natively supports function calling and structured output, achieving near-parity performance across mainstream frameworks and custom systems, eliminating the need to force-fit square pegs into round holes.

Third, deployment agnosticism—runs on a single consumer GPU. This is perhaps the most exciting part. Holo3.1 is the first to release quantized checkpoints in FP8, Q4 GGUF, and NVFP4 formats. Thanks to its MoE architecture, the 35B-A3B model has an active parameter count of only 3B; 4-bit quantization makes it small enough to fit on a 24GB GPU like an RTX 3090/4090. Benchmarks show a 3-4x relative speedup on mobile agent tasks, bringing inference latency into real-time territory. This means you can run a capable computer-use agent entirely offline, with data never leaving your machine.

Trend insight: the last-mile problem of general agents is being dismantled.

The upgrade logic of Holo3.1 reveals a significant shift: we are moving from 'can it do the task?' to 'can it do the task reliably, anywhere?' This isn't just about model capability; it's about engineering maturity.

Before, the prevailing assumption was that computer-use agents required massive cloud models and complex remote environments. But quantization, MoE architecture, and focused GUI fine-tuning now make lightweight local deployment feasible. The large mobile improvements hint that future agent operating systems might be built directly into devices, becoming as fundamental as multi-touch. Moreover, full embrace of open source (HuggingFace, TGI, vLLM) means the ecosystem can evolve collaboratively, avoiding vendor lock-in.

How can you use it? A few directions already emerging.

For developers, an immediate application is mobile app automation testing. Traditional script maintenance is costly; an agent that understands interfaces and plans actions can drastically reduce the barrier to create automated tests. Enterprise workflow automation is another natural fit: contract reviews, data entry, cross-system operations. Holo3.1's local quantization opens the door for data-sensitive industries like finance and healthcare.

If you already use an agent framework, consider plugging Holo3.1 in as the execution layer. Its function-calling interface integrates naturally into existing toolchains. The cost of a consumer GPU pales in comparison to continuous API fees, and latency is far lower.

A surprising but sound insight: quantization doesn't always mean sacrificing intelligence.

Many people fear quantization, assuming lower bit-widths make models dumber. Yet Holo3.1's results show that on domain-specific fine-tuned MoE models for GUI tasks, 4-bit performance can equal full precision. This suggests there's more redundancy in the model than we think, and targeted training with low-rank structure can effectively compensate for quantization loss. This should encourage developers to experiment more boldly with local large models instead of relying solely on cloud APIs.

Holo3.1 may not be the first computer-use agent, but it's the first that bakes practicality into its DNA: runs everywhere, integrates with any framework, and fits on a normal GPU. This might just be the push that brings general-purpose agents into our daily work.