Holo3.1: H Company Ships Quantized Computer-Use Agents for Local Deployment in 2026
TL;DR
- H Company releases Holo3.1, a family of computer-use agents optimized for local deployment with quantized checkpoints (FP8, Q4 GGUF, NVFP4)
- 79.3% accuracy on AndroidWorld with the 35B-A3B model, up from 67% in Holo3, plus native mobile automation support
- 2× end-to-end speedup on NVIDIA DGX Spark using NVFP4 quantization, cutting average step time from 6.8s to 3.3s
- Four model sizes (0.8B to 35B-A3B) enable deployment from consumer laptops to cloud infrastructure, all running entirely on-premises
What Happened
H Company shipped Holo3.1, the first production-ready computer-use agent family designed for local execution. The release addresses the gap between cloud-based AI demos and what enterprises actually need: agents that run on consumer hardware without sending data to external servers.
The core advancement is quantization. H Company released optimized checkpoints in FP8, Q4 GGUF (for llama.cpp), and NVIDIA’s NVFP4 format. These compressed models maintain near-identical performance while enabling deployment on devices that couldn’t previously run 35B-parameter models. The NVFP4 checkpoint scores within 2 points of the full BF16 version on OSWorld while delivering 1.74× token throughput.
Holo3.1 also expands beyond desktop automation. The model now handles mobile environments natively, jumping from 67% to 79.3% on AndroidWorld for the flagship 35B-A3B variant. Smaller models (4B, 9B) saw similar gains, climbing from 58% to 72%. H Company added function-calling protocols to work with third-party agent frameworks, not just their proprietary stack.
Why It Matters
Computer-use agents have been stuck in a deployment paradox. The models capable of reliably controlling browsers, desktop apps, and mobile interfaces were too large to run locally. Cloud inference works for demos but fails for enterprise workflows involving sensitive data, offline environments, or regulatory constraints.
Holo3.1 breaks this constraint. The Q4 GGUF checkpoint runs on Apple Silicon laptops and consumer-grade GPUs. Developers can now build agents that automate internal tools, handle PII-heavy workflows, or operate in air-gapped networks—all without data leaving the premises.
The mobile expansion matters because desktop-only automation is incomplete. Business workflows increasingly span devices. An agent that can’t handle mobile apps forces users back into manual handoffs. H Company’s 72-79% AndroidWorld performance puts mobile automation within production viability for the first time.
Key Details
Model Family:
| Model | Parameters | Use Case | Deployment Target |
|---|---|---|---|
| Holo3.1-0.8B | 800M | Embedded agents | Mobile/edge devices |
| Holo3.1-4B | 4B | Cost-efficient production | Consumer GPUs |
| Holo3.1-9B | 9B | Balanced workflows | Mid-range hardware |
| Holo3.1-35B-A3B | 35B | SOTA performance | High-end GPUs/Spark |
Quantization Formats:
- FP8: Standard 8-bit floating point, widely supported
- Q4 GGUF: 4-bit quantization for llama.cpp, targets consumer hardware
- NVFP4 W4A16: NVIDIA-optimized 4-bit weights, 16-bit activations
Performance:
- AndroidWorld: 79.3% (35B-A3B), 72% (4B/9B) — up from 67%/58% in Holo3
- OSWorld: ~2-point degradation from BF16 to NVFP4, minimal quality loss
- Throughput: 1.74× tokens/sec (NVFP4 vs BF16), 1.41× (NVFP4 vs FP8)
- Agent step time: 3.3s average (NVFP4 on Spark) vs 6.8s (FP8 baseline)
Availability:
- Released on Hugging Face and via H Company’s Holo Models API
- All four model sizes available now
- Quantized checkpoints initially for 35B-A3B, smaller sizes coming
Implications
This release signals a shift from cloud-first to deployment-agnostic AI agents. The industry has focused on scaling model size and cloud throughput. Holo3.1 inverts that: compress the model, optimize for local hardware, maintain quality.
Enterprise adoption accelerates when agents don’t require vendor lock-in. Companies won’t deploy automation that sends every screenshot and workflow step to an external API. Local inference removes that friction. Expect RPA vendors, IT automation platforms, and internal developer tools to integrate Holo3.1 quickly.
The mobile gains are underappreciated. AndroidWorld scores above 70% mean agents can now handle multi-step mobile workflows—app navigation, form filling, cross-app data transfer—without constant human correction. Combined with desktop control, this enables true cross-platform automation for the first time.
Our Take
H Company made the right call prioritizing deployment over benchmark chasing. The AI industry has shipped too many models that only work in controlled cloud environments. Holo3.1’s quantization strategy—multiple formats, minimal degradation, substantial speedups—shows maturity.
The 0.8B model is the sleeper hit here. Ultra-lightweight agents that run on mobile devices or embedded systems unlock use cases cloud models can’t touch: offline field service apps, on-device customer support, personal automation that never leaves your phone. If the 0.8B model hits 60%+ on core benchmarks, it’ll spawn an entirely new category of applications.
What to watch: whether H Company open-sources the quantization recipes and agent harness optimizations. The 2× speedup on Spark came from “agent harness optimizations” developed with NVIDIA—those details matter. If they release that code, expect rapid ecosystem development. If they keep it proprietary, adoption stays gated by their API.
The bigger question is whether local agents actually deliver on the privacy promise. Running the model locally is one thing. Building agents that don’t leak data through logging, telemetry, or third-party integrations is another. H Company needs to ship reference architectures and security guidelines, not just model weights.
Bottom line: Holo3.1 is the first computer-use agent family designed for real-world deployment constraints. If the small models deliver, this is the release that moves agents from research curiosity to production infrastructure.