Nvidia is touting another win on the latest set of MLPerf benchmarks released Wednesday. The GPU maker said it posted the fastest results on new MLPerf inference benchmarks, which measured the performance of AI inference workloads in data centers and at the edge. MLPerf's five inference benchmarks, applied across four inferencing scenarios, covered AI applications such as image classification, object detection and translation. Nvidia topped all five benchmarks for both data center-focused scenarios (server and offline), with its Turing GPUs. Meanwhile, the Xavier SoC turned in the highest performance among commercially available edge and mobile SoCs that submitted for MLPerf under edge-focused scenarios of single-stream and multi-stream.
Today, NVIDIA posted the fastest results on new MLPerf benchmarks measuring the performance of AI inference workloads in data centers and at the edge. The new results come on the heels of the company's equally strong results in the MLPerf benchmarks posted earlier this year. MLPerf's five inference benchmarks -- applied across a range of form factors and four inferencing scenarios -- cover such established AI applications as image classification, object detection and translation. NVIDIA topped all five benchmarks for both data center-focused scenarios (server and offline), with Turing GPUs providing the highest performance per processor among commercially available entries. Xavier provided the highest performance among commercially available edge and mobile SoCs under both edge-focused scenarios (single-stream and multistream).
When you want to see whether one CPU is faster than another, you have PassMark. But what do you do when you need to figure out how fast your machine-learning platform is--or how fast a machine-learning platform you're thinking of investing in is? Machine-learning expert David Kanter, along with scientists and engineers from organizations such as Google, Intel, and Microsoft, aims to answer that question with MLPerf, a machine-learning benchmark suite. Measuring the speed of machine-learning platforms is a problem that becomes more complex the longer you examine it, since both problem sets and architectures vary widely across the field of machine learning--and in addition to performance, the inference side of MLPerf must also measure accuracy. If you don't work with machine learning directly, it's easy to get confused about the terms. The first thing you must understand is that neural networks aren't really programmed at all: they're given a (hopefully) large set of related data and turned loose upon it to find patterns.
Nvidia has released a new version of TensorRT, a runtime system for serving inferences using deep learning models through Nvidia's own GPUs. Inferences, or predictions made from a trained model, can be served from either CPUs or GPUs. Serving inferences from GPUs is part of Nvidia's strategy to get greater adoption of its processors, countering what AMD is doing to break Nvidia's stranglehold on the machine learning GPU market. Nvidia claims the GPU-based TensorRT is better across the board for inferencing than CPU-only approaches. One of Nvidia's proffered benchmarks, the AlexNet image classification test under the Caffe framework, claims TensorRT to be 42 times faster than a CPU-only version of the same test -- 16,041 images per second vs. 374--when run on Nvidia's Tesla P40 processor.