MLOps – NVIDIA Technical Blog
…8 MIN READ May 08, 2026 Improving Bash Generation in Small Language Models with Grammar-Constrained Decoding Bash is one of the most flexible and powerful interfaces exposed to AI agents. In…
SLMs are well-positioned for the agentic era because they use a narrow slice of LLM functionality for any single language model errand. LLMs are built to be powerful generalists, but most agents use only a very narrow subset of their capabilities. They typically parse commands, generate structured outputs such as JSON for tool calls, or produce summaries and answer contextualized questions. These tasks are repetitive (up to the differences in prompt payloads), predictable, and highly specialized—well within the scope of specialized SLMs. An LLM trained to handle open-domain conversations is o
How Small Language Models Are Key to Scalable Agentic AI | NVIDIA Technical Blog…8 MIN READ May 08, 2026 Improving Bash Generation in Small Language Models with Grammar-Constrained Decoding Bash is one of the most flexible and powerful interfaces exposed to AI agents. In…
…8 MIN READ May 08, 2026 Improving Bash Generation in Small Language Models with Grammar-Constrained Decoding Bash is one of the most flexible and powerful interfaces exposed to AI agents. In…
…agent, the better it gets. While the integration points are specific to this use case (Slack, Outlook, and GitHub), the pattern of safely mixing public and private data in a self-improving…
…Uses priority and latency agentic hints to control queue ordering so user-facing turns run before background work. It can take in expected output sequence length (OSL) to improve load-balancing accuracy…
…be leveraged in an agentic workflow to automate quantum processor bring-up and retune calibration workflows. Quantum error correction decoders need to be low latency while improving the logical error rate (LER…