Llama 3.1 Nemotron Ultra 253B

Llama-3.1-Nemotron-Ultra-253B-v1 is a dense 253-billion parameter reasoning model built as a derivative of Meta's Llama-3.1-405B-Instruct. Nvidia applied Neural Architecture Search to create a version that achieves near-top benchmark performance while consuming significantly less memory and operating at lower inference latency than its parent model. The architecture delivers what Nvidia describes as an excellent accuracy-to-efficiency tradeoff, fitting on a single 8xH100 node and supporting a context window of up to 128K tokens. This design positions the model for scenarios requiring complex reasoning, tool integration, and retrieval-augmented generation where both raw capability and operational efficiency matter.

The model underwent targeted post-training focused on reasoning, human chat preferences, and task-specific skills like RAG and tool calling. Its development lineage reflects Nvidia's push into optimized open-weight releases, with model code, weights, and post-training data made publicly available on Hugging Face. On comparative benchmarks, it has shown strong results against larger competitors—outperforming DeepSeek R1 on reasoning tasks at roughly half the parameter count, and excelling in GPQA and LiveCodeBench evaluations. The focus on advanced reasoning, function calling, and instruction following makes it well-suited for AI assistant workflows and agentic applications where precise, multi-step problem solving is essential.