Holo 3.1 Deep Dive: The Open-Source Watershed for Local Agents
1. Introduction
On June 2, French AI company H Company released the Holo 3.1 series. This was not an ordinary model iteration.
Two months ago, Holo 3 still felt like a demo—usable, but fragile when environments changed. Holo 3.1's positioning has changed: production-ready. Across devices, across frameworks, across deployment targets, robustness comes first.
The more pivotal move: quantized weights released for the first time. FP8, Q4 GGUF, and NVFP4 formats shipped simultaneously. Consumer-grade GPUs now have a ticket to run agent models.
---
2. H Company: The French Dark Horse with a $220M Seed Round
H Company, formerly Holistic AI, emerged from stealth in May 2024 holding a $220 million seed round—led by Accel, with participation from Amazon, Samsung, UiPath, and other industry giants. Headquartered in Paris, building teams globally.
The company positions itself around "Action-Oriented AI" rather than chatbots. Its founding team comes from DeepMind and other institutions, with one core thesis: models should not only talk, but act.
The evolution from Holo 3 and Holo 3.1 to their own product Holotab validates this path—within the Holotab product framework, Holo 3.1 improves over its predecessor by more than 25%.
---
3. Model Lineup: Four Sizes, None Missing
| Model | Params | Positioning | |---|---|---| | 0.8B | Dense | Edge-first, runs even on CPU | | 4B | Dense | Consumer entry level | | 9B | Dense | Best performance/latency balance | | 35B-A3B | MoE | Flagship, 3B active parameters |
The 35B-A3B MoE architecture deserves a note: 35B total parameters, but only 3B activated per inference. Memory-hungry but computationally light—a trade-off designed specifically for high-throughput agent scenarios. H Company puts it plainly: the model can be big, but it cannot be slow.
The base is built on the Qwen series, with the incremental work all in GUI/Computer Use-specific training. Crucially: native support for structured JSON output and function-calling protocols. No intermediate translation layer needed—the model can directly issue commands, call APIs, click buttons.
---
4. Benchmarks: Real Runs, Not Score-Chasing
4.1 AndroidWorld (Mobile)
| Model | Holo 3 | Holo 3.1 | Gain | |---|---|---|---| | 4B / 9B | 58% | 72% | +14pp | | 35B-A3B | 67% | 79.3% | +12.3pp |
Mobile GUI operation is the most striking improvement. A 14-percentage-point jump for small models shows H Company invested serious effort in cross-device training.
4.2 Quantization Without Meaningful Loss
On OSWorld, FP8 and NVFP4 score only about 2 points below full-precision BF16. The quantized versions can be treated as nearly lossless—this is a prerequisite for deployment, not a bonus.
4.3 Inference Speed
Measured on DGX Spark, NVFP4 W4A16 token throughput is 1.74x that of BF16. Combined with the agent framework H Company co-optimized with NVIDIA, end-to-end step time drops from 6.8 seconds to 3.3 seconds—cut in half.
---
5. Deployment: Four Routes, Pick Your Need
| Option | Speed | Barrier | Scenario | |---|---|---|---| | llama.cpp + GGUF | ★★★★★ | Medium | First choice for agents | | LM Studio | ★★★★ | Low | Daily use | | Ollama | ★★★★ | Low | API calls | | SGLang | ★★★★★★ | High | Maximum performance |
Real-world recommendation: llama.cpp. The reasoning is simple—fastest, most configurable, and GGUF offers the best compatibility with agent frameworks (OpenClaw). Ollama and LM Studio win on out-of-box convenience, but in agent scenarios, controllability beats clickability.
Consumer Hardware Mapping
| GPU | Recommended Model | |---|---| | RTX 4090/3090 24GB | 35B-A3B Q4_K_M | | RTX 5070Ti/4060Ti 16GB | 9B | | 12GB | 4B | | 8GB | 0.8B |
Apple Silicon Macs can run the 9B GGUF directly. The 0.8B version even supports pure CPU inference—a phone-grade local agent is theoretically feasible.
---
6. Agent Ecosystem: From "Can Click" to "Survives Environment Changes"
Holo 3.1's biggest engineering gain is not the model itself, but robustness.
What real environments throw at agents:
- Web layouts change
- System popups differ
- Mobile and desktop controls are distributed differently
- Switching agent frameworks breaks output formats
- Enterprises require data to stay on-premise
- Cloud brought efficiency and lower maintenance barriers
- But core data, permission systems, and regulatory requirements eventually move back on-premise
- Training methodology: only "based on Qwen"—fine-tuning strategy, data composition, kernel optimizations all blank
- License: how open is it? Any commercial-use restrictions? No clear answer in current materials
- Real cross-framework validation: compatibility beyond OpenClaw lacks independent third-party evaluation
Holo 3.1's answer stands on three legs: cross-framework function-calling protocol + structured JSON output + quantized local deployment options. Adaptation happens at the model level, reducing translation loss at the framework level.
Industry observers summarize GUI agent competition as four questions:
> Cross-device task success rate? Real-world framework adaptation cost? Local hardware threshold? Private network stability? > > If these fail, no benchmark score matters—it's just a showroom model.
Holo 3.1 delivered an answer on all four fronts.
---
7. Local Deployment: Not Sentiment, But a Boundary
Running the model locally is not about saving API fees.
Cloud agents don't operate on chat logs—they operate on internal systems, financial pages, customer records, permission entry points. Product managers see efficiency; compliance teams see a risk inventory. Keeping data inside the intranet is not just a technical option—it is becoming a hard compliance constraint.
Analogy to enterprise cloud adoption:
The closer technology gets to core processes, the less deployment location remains a purely technical choice. Holo 3.1's quantized weight release hands ammunition to this contest.
---
8. Open Questions and Conclusion
What H Company did not disclose is equally worth noting:
Holo 3.1 won't rewrite the industry overnight, but it draws the watershed clearly. GUI agent competition is shifting from "does the demo run" to "does it run on real devices, real frameworks, and under real privacy constraints." Local deployment options are no longer a bonus—they are becoming the ticket to entry.
> *This article is based on H Company official release materials, the HuggingFace technical blog, freedidi hands-on tutorials, and industry analysis. Data as of June 13, 2026.*