Artificial Intelligence1 min read

Run AI Models for Edge Application Development with Just a Few Commands

By · Published by Everything Blog

In short

The environment utilizes the lightweight TensorRT Edge-LLM C++ runtime alongside a dedicated command-line interface to build and cache optimized inference engines. Teams can interact with the text-only Nemotron-3.5 Lightning model via a terminal chat or an OpenAI-compatible API endpoint. To further minimize generation latency, the setup supports speculative decoding by using a draft model to propose tokens for verification.

Key points

  • The setup targets physical AI implementations operating under strict power, memory, and l…: The setup targets physical AI implementations operating under strict power, memory, and latency limits such as robotics and driver monitoring.
  • Initial configuration requires downloading an approximately 21GB model, which is cached l…: Initial configuration requires downloading an approximately 21GB model, which is cached locally as a compiled engine for rapid subsequent launches.
  • Token generation speeds can be accelerated within the runtime by enabling DSpark speculat…: Token generation speeds can be accelerated within the runtime by enabling DSpark speculative decoding.

Sources checked by our analysis pipeline: 1

AI is expanding beyond the data center as developers build a new generation of agents to act at the edge: monitoring driver attention and cabin conditions, helping robots understand their surroundings and plan tasks, and coordinating fleets of machines on production lines. These physical AI applications rely on models that perceive, reason, and act within tight memory, power, and latency budgets. But getting a model ready to run can involve substantial setup.

NVIDIA DGX Spark, now available in a 64GB configuration, gives embedded developers a platform to prototype, fine-tune, and test models and agents. NVIDIA TensorRT Edge-LLM provides a lightweight C++ inference runtime designed for embedded workloads, bringing the same inference framework to development on Spark and deployment on platforms such as NVIDIA Jetson Thor, NVIDIA DRIVE AGX Thor, and NVIDIA IGX Thor.

You can get started in just a few commands with the trtedge command-line interface (CLI). It downloads your selected model and builds TensorRT engines optimized for inference on Spark.

In this tutorial, you’ll run NVIDIA Nemotron-3.5 Lightning, a text-only model, on Spark using TensorRT Edge-LLM. You’ll test…

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