Descriptions:
NVIDIA has released Nemotron Ultra, its largest open model to date at 550 billion total parameters—with only 55 billion active at inference time thanks to a mixture-of-experts architecture. The model supports a 1 million token context window and is fully open, with weights, training data, and recipes available on Hugging Face. Its architecture combines Mamba 2 layers with sparse attention and multi-token prediction heads for native speculative decoding, and it was trained via multi-tier on-policy distillation using over 10 specialized teacher models across domains including software engineering, terminal use, search, and safety.
In this hands-on walkthrough, AI practitioner Fahd Mirza deploys Nemotron Ultra through NVIDIA’s free API endpoint and connects it to the Hermes coding agent on an Ubuntu system. The model is given a single agentic goal: autonomously research a FastAPI performance optimization, implement it locally, benchmark before and after using curl, and confirm measurable improvement—with no additional prompting. Without any guidance on approach, the model checks the environment, installs orjson and uvloop, writes three production files (baseline app, optimized app, benchmark script), starts dual servers on separate ports, runs benchmarks, reads results, kills both servers, and delivers a full summary. The optimization yielded up to 12% latency improvement and nearly 14% throughput improvement on larger payloads, with the goal confirmed achieved in just one of a 20-turn budget.
The video also explains how the co-evolution of student and teacher models across two full distillation iterations produces a model that generalizes broadly across agentic tasks rather than excelling in only one domain.
📺 Source: Fahd Mirza · Published June 04, 2026
🏷️ Format: Hands On Build
