For lack of rich competition, some of Nvidia's most significant results in the latest MLPerf were against itself, comparing its newest GPU, H100 "Hopper," to its existing product, the A100. Although chip giant Nvidia tends to cast a long shadow over the world of artificial intelligence, its ability to simply drive competition out of the market may be increasing, if the latest benchmark test results are any indication. Did you miss out on Black Friday 2022? No problem: Cyber Monday deals are here, with internet retailers offering their lowest prices of the year. ZDNET is surfacing the latest and best sales online in real time for you to check out now.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Nvidia released results today against new MLPerf industry-standard artificial intelligence (AI) benchmarks for its AI-targeted processors. While the results looked impressive, it is important to note that some of the comparisons they make with other systems are really not apples-to-apples. For instance, the Qualcomm systems are running at a much smaller power footprint than the H100, and are targeted at market segments similar to the A100, where the test comparisons are much more equitable.

TEL AVIV, Israel, Sept. 8, 2022 -- Deci, the deep learning company harnessing Artificial Intelligence (AI) to build better AI, announced results for its Natural Language Processing (NLP) inference model submitted to the MLPerf Inference v2.1 benchmark suite under the open submission track. Generated by Deci's Automated Neural Architecture Construction (AutoNAC) technology, the NLP model, dubbed DeciBERT-Large, ran on Dell-PowerEdge-R7525-2 hardware using the AMD EPYCTM 7773X processor. The resulting model outperformed both the throughput performance of the BERT-Large model by 6.46x and achieved a 1% boost in accuracy. The model was submitted under the offline scenario in MLPerf's open division in the BERT 99.9 category. The goal was to maximize throughput while keeping the accuracy within a 0.1% margin of error from the baseline, which is 90.874

Even though they have been around for years, the phrase "MLPerf benchmarks" holds little meaning to most people outside of the AI developer community. However, this community-driven benchmark suite, which measures performance of a broad range of machine learning (ML) tasks, is quickly becoming the gold standard for the fair and unbiased assessment of accelerated computing solutions for machine learning training, inference, and high performance computing (HPC). The era of MLPerf is here, and everyone should be paying attention. Organizations across every industry are racing to take advantage of AI and machine learning to improve their businesses. According to Karl Freund, founder and principal analyst at Cambrian AI Research, businesses should expect that customer demand for AI-accelerated outcomes will continue to grow.

By now it's evident that artificial intelligence (AI) is the singular most definitive technology of this generation, and it's powering broad industrial transformation across critical use cases. Ronald van Loon is a NVIDIA partner and had the opportunity to apply his expertise as an industry analyst to explore the implications of MLPerf benchmarking results on the next generation of AI. Enterprises are facing an unprecedented moment as they strive to leverage AI for competitive advantage. This means optimizing training and inferencing for AI models to gain differentiating benefits, like significantly improved productivity for their data science teams and achieving faster time to market for new products and services. However, AI is advancing incredibly quickly and AI model size is dramatically increasing in such areas as Natural Language Processing (NLP), which has grown 275 times every two years using the Transformer neural network architecture.

Deep Learning-based (DL) applications are becoming increasingly popular and advancing at an unprecedented pace. While many research works are being undertaken to enhance Deep Neural Networks (DNN) -- the centerpiece of DL applications -- practical deployment challenges of these applications in the Cloud and Edge systems, and their impact on the usability of the applications have not been sufficiently investigated. In particular, the impact of deploying different virtualization platforms, offered by the Cloud and Edge, on the usability of DL applications (in terms of the End-to-End (E2E) inference time) has remained an open question. Importantly, resource elasticity (by means of scale-up), CPU pinning, and processor type (CPU vs GPU) configurations have shown to be influential on the virtualization overhead. Accordingly, the goal of this research is to study the impact of these potentially decisive deployment options on the E2E performance, thus, usability of the DL applications. To that end, we measure the impact of four popular execution platforms (namely, bare-metal, virtual machine (VM), container, and container in VM) on the E2E inference time of four types of DL applications, upon changing processor configuration (scale-up, CPU pinning) and processor types. This study reveals a set of interesting and sometimes counter-intuitive findings that can be used as best practices by Cloud solution architects to efficiently deploy DL applications in various systems. The notable finding is that the solution architects must be aware of the DL application characteristics, particularly, their pre- and post-processing requirements, to be able to optimally choose and configure an execution platform, determine the use of GPU, and decide the efficient scale-up range.

In its third round of submissions, MLCommons released results for MLPerf Inference v1.0. MLPerf is a set of standard AI inference benchmarking tests using seven different applications. These seven tests include a range of workloads that include computer vision, medical imaging, recommender systems, speech recognition, and natural language processing. MLPerf benchmarking measures how fast a trained neural network can process data for each application and its form factor. The results allow unbiased comparison between systems.

The deep learning company, Deci, has announced a broad strategic business and technology collaboration with Intel to optimise deep learning inference on Intel Architecture (IA) CPUs. As one of the first companies to participate in the Intel Ignite startup accelerator, Deci will now work with Intel to deploy innovative AI technologies to mutual customers. The collaboration is intended to take a significant step towards enabling deep learning inference at scale on Intel CPUs, reducing costs and latency, and enabling new applications of deep learning inference. New deep learning tasks can be performed in a real-time environment on edge devices and companies that use large scale inference scenarios can dramatically cut cloud or datacentre cost, simply by changing the inference hardware from GPU to Intel CPU. "By optimising the AI models that run on Intel's hardware, Deci enables customers to get even more speed and will allow for cost-effective and more general deep learning use cases on Intel CPUs," said Deci CEO and co-founder Yonatan Geifman.

MLCommons, an open engineering consortium, launches its industry-academic partnership to accelerate machine learning innovation and broaden access to this critical technology for the public good. The non-profit organization initially formed as MLPerf, now boasts a founding board that includes representatives from Alibaba, Facebook AI, Google, Intel, NVIDIA and Professor Vijay Janapa Reddi of Harvard University; and a broad range of more than 50 founding members. The founding membership includes over 15 startups and small companies that focus on semiconductors, systems, and software from across the globe, as well as researchers from universities like U.C. Berkeley, Stanford, and the University of Toronto. MLCommons will advance development of, and access to, the latest AI and Machine Learning datasets and models, best practices, benchmarks and metrics. An intent is to enable access to machine learning solutions such as computer vision, natural language processing, and speech recognition by as many people, as fast as possible.

Graphics chip giant Nvidia mopped up the floor with its competition in a benchmark set of tests released Wednesday afternoon, demonstrating better performance on a host of artificial intelligence tasks. The benchmark, called MLPerf, announced by the MLPerf organization, an industry consortium that administers the tests, showed Nvidia getting better speed on a variety of tasks that use neural networks, from categorizing images to recommending which products a person might like. Predictions are the part of AI where a trained neural network produces output on real data, as opposed to the training phase when the neural network system is first being refined. Benchmark results on training tasks were announced by MLPerf back in July. Many of the scores on the test results pertain to Nvidia's T4 chip that has been in the market for some time, but even more impressive results were reported for its A100 chips unveiled in May.