# MLPerf

Published articles for MLPerf.

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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...

## NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut

DevFeed: [NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut](<https://devfeed.tech/articles/nvidia-vera-rubin-nvl72-delivers-leading-performance-in-mlperf-inference-v6-1-debut-31524.md>)

Original publisher: [Read original article](<https://blogs.nvidia.com/blog/vera-rubin-nvl72-mlperf-inference/>)

Author: Zhihan Jiang

Published: 2026-09-16T15:00:48Z

Content type: article

Language: en

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

Topics: [NVIDIA Vera Rubin](<https://devfeed.tech/topics/nvidia-vera-rubin.md>), [Vera Rubin NVL72](<https://devfeed.tech/topics/vera-rubin-nvl72.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Dynamo](<https://devfeed.tech/topics/dynamo.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [TensorRT-LLM](<https://devfeed.tech/topics/tensorrt-llm.md>)

Tags: [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [dynamo](<https://devfeed.tech/tags/dynamo.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [mlperf](<https://devfeed.tech/tags/mlperf.md>), [networking](<https://devfeed.tech/tags/networking.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera-rubin](<https://devfeed.tech/tags/nvidia-vera-rubin.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [software](<https://devfeed.tech/tags/software.md>), [tensorrt](<https://devfeed.tech/tags/tensorrt.md>), [tensorrt-llm](<https://devfeed.tech/tags/tensorrt-llm.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

NVIDIA reports MLPerf Inference v6.1 preview results for Vera Rubin NVL72 and GB300 NVL72 systems. Vera Rubin NVL72 delivered up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and up to 2.5x higher throughput on DeepSeek-R1, while a four-rack GB300 NVL72 submission achieved 99% scaling efficiency. The results used vLLM, NVIDIA Dynamo, and TensorRT-LLM.

### Source excerpt

System performance, efficient infrastructure scaling and continuous software optimization are key levers that determine AI inference economics. Higher system performance means more tokens generated, resulting in higher revenue. Efficient scaling means throughput grows proportionally as hardware gets added, requiring fewer resources to serve users at scale. Continuous optimization means generating more value from infrastructure investments. [...]