# Jetson AGX Thor Developer Kit

A NVIDIA developer kit for developing and testing software for Jetson-based AI and robotics products.

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## TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor

DevFeed: [TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor](<https://devfeed.tech/articles/tensorrt-edge-llm-completes-the-mlperf-edge-agentic-benchmark-6-4x-faster-on-jetson-agx-thor-31485.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/tensorrt-edge-llm-completes-the-mlperf-edge-agentic-benchmark-6-4x-faster-on-jetson-agx-thor/>)

Author: Elizabeth Goodman

Published: 2026-09-16T20:37:07Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Jetson AGX Thor Developer Kit](<https://devfeed.tech/topics/jetson-agx-thor-developer-kit.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [TensorRT](<https://devfeed.tech/topics/tensorrt.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [jetson](<https://devfeed.tech/tags/jetson.md>), [jetson-agx-thor-developer-kit](<https://devfeed.tech/tags/jetson-agx-thor-developer-kit.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-benchmarking](<https://devfeed.tech/tags/llm-benchmarking.md>), [mlperf](<https://devfeed.tech/tags/mlperf.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [tensorrt](<https://devfeed.tech/tags/tensorrt.md>), [tensorrt-llm](<https://devfeed.tech/tags/tensorrt-llm.md>), [thor](<https://devfeed.tech/tags/thor.md>)

### AI overview

This article reports that NVIDIA TensorRT Edge-LLM ran Qwen3.6-27B on a single NVIDIA Jetson AGX Thor Developer Kit for the MLPerf Inference v6.1 Edge Agentic benchmark. Using NVFP4 quantization, tree-based multi-token prediction, and KV cache reuse, it achieved 52.33 tokens per second and completed 1,007 turns in 24 minutes and 36 seconds, 6.4 times faster than the llama.cpp reference submission.

### Source excerpt

AI agents are moving from cloud data centers to vehicles, robots, and other edge devices. Unlike a chatbot that answers a single prompt, an agent works through...