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COMPANY · ENTITY #3447

TensorRT Edge-LLM

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COMPANIES · 1 SOURCE · NVIDIA Developer

NVIDIA demonstrates TensorRT Edge-LLM workflow to run Nemotron-3.5 Lightning on DGX Spark 64GB

NVIDIA published a tutorial showing how to use TensorRT Edge-LLM (examples tested with Edge-LLM 0.11.1 and TensorRT 10.16.1.11) on a DGX Spark 64GB to prototype, fine-tune and test embedded AI models. The trtedge CLI downloads models and builds optimized TensorRT engines, supports serving via a local OpenAI-compatible API, and can enable DSpark speculative decoding (reported to raise mean generation from 73.4 to 126.9 tokens/s on HumanEval prompts while keeping peak memory at 27.9GB).

7.0

MODELS · 1 SOURCE · NVIDIA Developer

TensorRT Edge-LLM runs Qwen3.6-27B on Jetson AGX Thor, completes MLPerf Edge Agentic 6.4× faster

NVIDIA's TensorRT Edge-LLM ran Qwen3.6-27B on a single Jetson AGX Thor Developer Kit and achieved 52.33 tokens/sec in the MLPerf Inference v6.1 Edge Agentic performance workload, completing all 1,007 turns in 24 minutes 36 seconds — 6.4× faster than the llama.cpp Jetson reference (2h37m). The submission used NVFP4 quantization for weights/activations, FP8 for the KV cache, tree-based multi-token prediction, and KV-cache/recurrent-state reuse to accelerate long-context agent decoding.

7.0