Holotron-12B - High Throughput Computer Use Agent

We're thrilled to release Holotron-12B, a multimodal computer-use model from H Company. Post-trained from the open NVIDIA Nemotron-Nano-2 VL model on H Company’s proprietary data mixture, Holotron-12B is the result of a close collaboration between our research labs to engineer a new type of model optimized primarily for scale and performance in production.

H Company is part of the NVIDIA Inception Program.

The model is now available on Hugging Face.

Why We Built Holotron-12B

Most multimodal models today optimize primarily for static vision or following instructions. Holotron-12B, just like our Holo2 model, however, has a different goal: serving as a policy model for computer-use agents that must perceive, decide, and act efficiently in interactive environments.

With Holotron-12B, we wanted to create a model that could efficiently and effectively scale in production while handling long contexts with multiple images, and still perform well on agent benchmarks. The NVIDIA Nemotron model offered a strong foundation on the inference side, and by developing Holotron-12B we've demonstrated how much more the model can accomplish with further training.

High Throughput Inference with a Hybrid SSM Architecture

When evaluated on the WebVoyager Benchmark, the model excels using a real-world multimodal agentic workload featuring long context, multiple high-resolution images, and a high request concurrency of 100 benchmark workers. Running on a single H100 GPU and using vLLM with the latest SSM optimizations (v0.14.1), Holotron-12B achieved an over 2x higher throughput compared to Holo2-8B. This makes Holotron-12B an attractive choice for throughput-bound workloads, such as data generation, annotation, and online reinforcement learning.

Training and Evaluating Holotron-12B

The final checkpoint was trained on approximately 14 billion tokens.