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A NVIDIA Developer post describes applying full‑stack NIM optimizations to production serving for the Nemotron 3 Ultra large language model, reporting up to a 2.5× increase in concurrent users served. The piece emphasizes that deployment is only the first step and that system‑level changes across the stack are needed to maximize real‑world throughput and scalability.
NVIDIA Developer published guidance on running BioNeMo NIM microservices for protein structure prediction inside Claude Science, illustrating how NVIDIA’s model microservices can be invoked within an agentic research workflow. The article frames this integration in the context of agentic AI that can read papers, propose hypotheses, call models, and prioritize experiments.
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