Cosmos Nemotron VLM

Cosmos Nemotron VLM combines a Cosmos vision backbone (spatiotemporal perception and “physical common sense”) with a Nemotron language decoder for clear, grounded answers. It ingests single images, multi-image sets, or video clips plus text prompts, tracks objects over time, and explains scenes with chain-of-thought style reasoning. The model is instruction-tuned for practical tasks—document OCR and layout understanding, chart/table reading, UI/screenshot analysis, video Q&A, and robotics/agent planning—and returns structured outputs (Markdown/JSON) suitable for pipelines and agents.

For production use it supports function/tool calling, streaming tokens, and retrieval grounding; deployment is optimized on NVIDIA GPUs with TensorRT-LLM and packaged as a NIM microservice for autoscaling and low latency. Quantization (8/4-bit) and multi-GPU parallelism help balance cost and throughput. Typical uses include vision copilots, video analytics and monitoring, shop-floor/robot guidance, technical document extraction, and UI automation—any workflow that needs reliable visual understanding with strong language reasoning.