{"id":2277,"date":"2026-05-16T19:43:29","date_gmt":"2026-05-16T19:43:29","guid":{"rendered":"https:\/\/ai-box.eu\/?p=2277"},"modified":"2026-05-16T19:50:27","modified_gmt":"2026-05-16T19:50:27","slug":"nemo-agent-toolkit-auf-der-rtx-a6000-ada-vom-inferenz-layer-zum-orchestrator-layer","status":"publish","type":"post","link":"https:\/\/ai-box.eu\/en\/large-language-models-en\/nemo-agent-toolkit-auf-der-rtx-a6000-ada-vom-inferenz-layer-zum-orchestrator-layer\/2277\/","title":{"rendered":"NeMo Agent Toolkit on the RTX A6000 Ada – From Inference Layer to Orchestrator Layer"},"content":{"rendered":"

In my four-part TensorRT-LLM series<\/a> I showed how I optimize inference performance on the RTX A6000 Ada \u2014 251 tokens\/sec with Qwen-2.5-7B in FP8, deployable .engine<\/code> files, all cleanly reproducible. But in doing so, I had only built one part of the stack: the inference layer<\/strong>.<\/p>\n

Inspired by the ever-present GenAI agent publications, it became clear to me: a production AI stack consists of multiple layers. Inference is only one of them. Above it sits the orchestrator<\/strong> \u2014 the layer that decides which tool gets called when, that does multi-step reasoning, that produces the actual agent behavior.<\/p>\n

NVIDIA has released the NeMo Agent Toolkit (NAT)<\/strong> as an open-source library for exactly this. In this post I’ll show you how I installed NAT on my Ubuntu 24.04 server \u2014 cleanly isolated in a Python venv, with my existing Ollama setup as the backend \u2014 and how I got my first ReAct agent up and running. Including two non-trivial pitfalls that I’d rather spare you.<\/p>\n

Here’s the link to the NeMo Agent Toolkit: https:\/\/github.com\/NVIDIA\/NeMo-Agent-Toolkit<\/a><\/p>\n

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