#Jetson agx orin benchmark software#
#Jetson agx orin benchmark series#

The Jetson AGX Orin series includes the Jetson AGX Orin 64GB and the Jetson AGX Orin 32GB modules. Jetson Xavier and Jetson Orin modules AI TOPS performance comparison Jetson Orin capability and price comparisonįigure 2. Compared to Jetson Xavier modules, Jetson Orin brings even higher performance, power efficiency, and inference capabilities to modern AI applications. With up to 275 tera operations per second (TOPS) of performance, Jetson Orin modules can run server class AI models at the edge with end-to-end application pipeline acceleration. Jetson AGX Orin module Jetson AGX Orin and Jetson Orin NX seriesĪt GTC Spring 2022, we announced that four Jetson Orin modules will be available in 2022.

#Jetson agx orin benchmark software#

They run the comprehensive NVIDIA AI software stack to power the next generation of demanding edge AI applications.įigure 1. These modules deliver tremendous performance with class-leading energy efficiency. NVIDIA Jetson AGX Orin modules are the highest-performing and newest members of the NVIDIA Jetson family. These autonomous robots must concurrently perform 3D perception, natural language understanding, path planning, obstacle avoidance, pose estimation, and many more actions that require both significant computational performance and highly accurate trained neural models for each application. Human-robot interactions are increasing in retail spaces, food delivery, hospitals, warehouses, factory floors, and other commercial applications. The next generation of applications require large increases in AI compute performance to handle multimodal AI applications running concurrently in real time. Nvidia's Jetson AGX Orin for the Edge is also up to 63 percent more energy efficient and up to 81 percent more powerful than last year thanks to numerous improvements.The pace for development and deployment of AI-powered robots and other autonomous machines continues to grow rapidly. Nvidia's L4 is significantly faster than the predecessor T4. The card is already available from some cloud providers and delivers 2.2 to 3.1 times the inference performance of its predecessor, the T4, in the benchmarks. New to the benchmark is Nvidia's L4 Tensor GPU, which the company recently unveiled at GTC. New L4 card up to 3 times faster than predecessor

| Image: NvidiaĬompared to an A100 GPU, the H100 GPU is also significantly stronger at inferencing transformer models, such as BERT 99.9, thanks to the Transformer engine, where the H100 delivers more than four times the performance.Īs a result, the card promises to deliver big performance gains for many generative AI models, such as those that generate text, images, or 3D models. A comparison with some accelerators that participated in MLPerf 3.0. Nvidia is the only company to present results for all tasks in MLPerf Inference 3.0. In presenting the results, Nvidia emphasized that they see themselves as the clear leader in performance, but also as the equally important leader in the versatility of their architecture. This jump is seen in RetinaNet inference, with other models such as 99% accurate BERT running 12% faster, ResNet-50 running 13% faster, and 3D U-Net used in medical applications running 31% faster. Nvidia Hopper makes significant year-over-year gainsĪccording to Nvidia, the H100 Tensor Core GPUs in the DGX H100 systems have up to 54 percent more inference performance than last year due to software optimizations. The test is designed to more accurately reflect how data enters the AI accelerator and is output in the real world, thus revealing bottlenecks in the network. A new feature is a network environment that tests the AI performance of different systems under more realistic conditions: Data is streamed to an inference server. Today MLPerf released new results of the MLPerf Inference 3.0 benchmark. The test is hosted by MLCommons and aims to transparently compare different chip architectures and system variants.

In the MLPerf benchmark, hardware vendors and service providers compete with their AI systems. New data shows performance leaps with Hopper and new hardware.

Nvidia leads this year's MLPerf inference benchmark.