Holo3.1: Vision-Language Models for Local Automation Agents

Hcompany, a France-based company, recently announced the release of Holo3.1, a new family of Vision-Language Models (VLM) designed for computer automation and interaction. This suite of models aims to empower software agents, enabling them to understand and operate in complex digital environments, from web to desktop, and even mobile ecosystems. The introduction of Holo3.1 represents a significant step forward for organizations seeking robust and controllable AI solutions.

The distinctive feature of Holo3.1 lies in its ability to support local deployment. This is made possible through the use of optimized and quantized checkpoints, which reduce hardware resource requirements and facilitate the execution of models directly on the user's infrastructure. This approach is particularly relevant for companies that prioritize data sovereignty, security, and reduced latency, all crucial elements for sensitive AI workloads.

Architecture and Deployment Options

The Holo3.1 family is built upon the Qwen 3.5 base models and offers a range of sizes, from 0.8 billion to 35 billion parameters (35B-A3B). This scalability allows organizations to choose the most suitable model for their needs, balancing performance with computational requirements. Smaller models can run on hardware with limited resources, while larger versions provide advanced capabilities for more complex tasks.

For the Holo3.1-35B-A3B model, Hcompany provides various Quantization options, including BF16, FP8, NVFP4, and Q4 GGUF. Quantization is a fundamental technique for optimizing AI models, as it reduces the precision of model weights (e.g., from 16-bit to 8-bit or 4-bit), thereby decreasing VRAM usage and improving Throughput during Inference. While it may involve a slight trade-off in precision, Quantization is essential for making Large Language Models (LLM) and VLM usable on on-premise hardware, which often has GPU memory constraints.

Benefits for On-Premise Infrastructure and Data Sovereignty

Holo3.1's approach, emphasizing local deployment and cost efficiency, aligns perfectly with the needs of organizations considering self-hosted alternatives to cloud solutions. The ability to run these VLMs directly on their own servers offers complete control over data and processes, a critical aspect for regulated industries or companies with stringent compliance and security requirements. The Apache 2.0 License, under which the models are distributed, also ensures flexibility for integration and customization.

For CTOs, DevOps leads, and infrastructure architects, the Holo3.1 family presents an opportunity to implement AI automation agents without relying entirely on external cloud services. This can translate into a more favorable Total Cost of Ownership (TCO) in the long term, balancing initial hardware investment with reduced recurring operational costs and greater autonomy. The capability to operate in air-gapped environments or with limited connectivity is an additional advantage for specific scenarios.

Application Scenarios and Strategic Considerations

Holo3.1 is designed to excel in a variety of contexts, from computer use automation to UI grounding, mobile automation, and business workflows. Its native function-calling support simplifies integration with existing agent Frameworks, allowing developers to create more sophisticated and reactive applications. This means that agents can not only "see" and "understand" the interface but also actively interact with it by executing specific actions.

Choosing among the different Holo3.1 models and their respective Quantization options will depend on the specific requirements of each deployment. A 0.8B parameter quantized model might be ideal for edge scenarios or mobile devices with limited resources, while the 35B-A3B version in BF16 might require high-end GPUs, such as NVIDIA A100 or H100, to ensure optimal performance. For those evaluating on-premise deployment of LLMs and VLMs, AI-RADAR offers analytical frameworks on /llm-onpremise to assess the trade-offs between performance, costs, and infrastructure requirements, helping to make informed decisions.