Latest news with #NvidiaDGXCloud
The Star
22-05-2025
- Business
- The Star
Trading ideas: YTL Power, BHIC, MMHE, DC, Bumi Armada, Meta Bright, Inari, Public Bank, Supermax, SP Setia, Perdana, Apex, Solarvest, Sarawak Plantation
KUALA LUMPUR: Here is a recap of the announcements that made headlines in Corporate Malaysia. YTL Power International Bhd 's first supercomputer in Malaysia, deploying the Nvidia GB200 Grace Blackwell Superchip on Nvidia DGX Cloud, is on track to go live by the third quarter of this year. Boustead Heavy Industries Corporation Bhd has entered into a memorandum of understanding with Airbus Helicopters for potential collaboration as an industrial partnership in Malaysia. Malaysia Marine and Heavy Engineering Holdings Bhd has secured an injunction against a notice of arbitration issued by its subcontractor in relation to a dispute under a subcontract for the Bokor Phase 3 redevelopment project in Sarawak. DC Healthcare Holdings Bhd is strengthening its brand ecosystem by integrating Dr. Chong Clinic, Dr. Chong Slimming, and NewB Premium Skincare, while broadening its skincare product portfolio. Bumi Armada Bhd 's wholly-owned subsidiary, Armada Kojo BV, has inked a production sharing contract with Indonesia's Energy and Mineral Resources Ministry for the exploration block in Makassar Strait, Indonesia. Meta Bright Group Bhd and ChargeHere EV Solution Sdn Bhd are exploring collaboration opportunities within the rapidly growing electric vehicle ecosystem. In the 3QFY25, Inari Amertron Bhd 's net profit fell 24.7% to RM55.5mn compared with RM73.7mn last year. Public Bank Bhd announced a quarterly net profit of RM1.75bn, up from RM1.65bn in the year-ago quarter, as revenue rose to RM7.31bn, an increase from RM6.65bn in the previous comparative quarter. Supermax Corp Bhd reported a wider net loss of RM23.8mn in the 3QFY25, dragged by higher operating expenses and a RM12.7mn unrealised foreign exchange loss following the depreciation of the US dollar against the ringgit. S P Setia Bhd recorded a net profit after tax of RM89mn for 1QFY25, mainly attributable to operational efficiency and effective cost management. Perdana Petroleum Bhd slipped into the red with a net loss of RM18mn in 1QFY25 from a net profit of RM6mn a year earlier due to lower revenue and lower contribution from third-party vessel chartering and a marginal increase in vessels direct costs. Apex Healthcare Bhd net profit for 1QFY25 dropped 7.1% to RM17.6mn from RM21.2mn in the same quarter last year on lower public sector sales. Solarvest Holdings Bhd reported a net profit of RM20.5mn for 4QFY25, nearly tripling from RM7.7mn a year earlier, driven by increased roll-outs of utility-scale solar projects under the Corporate Green Power Programme. For 1QFY25, Sarawak Plantation Bhd 's net profit rose to RM22.6mn from RM19.1mn in the previous corresponding quarter, mainly because of higher realised average selling price of crude palm oil and palm kernel.
Forbes
20-03-2025
- Business
- Forbes
Nvidia Benchmark Recipes Bring Deep Insights In AI Performance
Nvidia DGX Cloud Data Center As AI workloads and accelerated applications grow in sophistication and complexity, businesses and developers need better tools to assess their infrastructure's ability to handle the demands of both training and inference more efficiently. To that end, Nvidia has been working on a set of performance testing tools, called DGX Cloud Benchmark Recipes, that are designed to help organizations evaluate how their hardware and cloud infrastructure perform when running the most advanced AI models available today. Our team at HotTech had a chance to kick the tires on a few of these recipes recently, and found the data they can capture to be extremely insightful. Nvidia's toolkit also offers a database and calculator of performance results for GPU-compute workloads on various configurations, including the number of Nvidia H100 GPUs and cloud service providers, while the recipes allow businesses to run realistic performance evaluations on their own infrastructure. The results can help guide decisions on whether to invest in more powerful hardware, cloud provider service levels, or tweak configurations to better meet machine learning demands. These tools also take a holistic approach that incorporates network technologies for optimal throughput. Nvidia DGX Cloud Benchmarking Recipes are a set of pre-configured containers and scripts that users can download and run on their own infrastructure. These containers are optimized for testing the performance of various AI models under different configurations, making them very valuable for companies looking to benchmark systems, whether on prem or in the cloud, before committing to larger-scale AI workloads or infrastructure deployments. Nvidia DGX Cloud Time To Train Performance Report In addition to offering static performance data, time to train and efficiency calculated from its database, Nvidia has recipes readily available for download that let businesses run real-world tests on their own hardware or cloud infrastructure, helping them understand the performance impact of different configurations. The recipes include benchmarks for training models like Meta's Llama 3.1 and Nvidia's own Llama 3.1 branch, called Nemotron, across several cloud providers (AWS, Google Cloud, and Azure), with options for adjusting factors like model size, GPU usage, and precision. The database is broad enough to cover popular AI models, but it is primarily designed for testing large-scale pre-training tasks, rather than inference on smaller models. The benchmarking process also allows for flexibility. Users can tailor the tests to their specific infrastructure by adjusting parameters such as the number of GPUs and the size of the model being trained. The default hardware configuration in Nvidia's database of results uses the company's high-end H100 80GB GPUs, but it is designed to be adaptable. Although currently, it does not include consumer or prosumer-grade GPUs (e.g., RTX A4000 or RTX 50) or the company's latest Blackwell GPU family, these options could be added in the future. Running the DGX Cloud Benchmarking Recipes is straightforward, assuming a few prerequisites are met. The process is well-documented, with clear instructions on setting up, running the benchmarks, and interpreting the results. Once a benchmark is completed, users can review the performance data, which includes key metrics like training time, GPU usage, and throughput. This allows businesses to make data-driven decisions about which configurations deliver the best performance and efficiency for their AI workloads. This could also go a long way in helping companies maintain green initiatives in terms of meeting power consumption and efficiency budgets. While the DGX Cloud Benchmarking Recipes offer valuable insights, there are a few areas where Nvidia's tools could be expanded. First, benchmarking recipes are currently focused primarily on pre-training large models, not on real-time inference performance. Inference tasks, such as token generation or running smaller AI models, are equally important in many business applications. Expanding the toolset to include more detailed inference benchmarks would provide a fuller picture of how different hardware configurations handle these real-time demands. Additionally, by expanding the recipe selection to include lower-end or even higher-end GPUs (like Blackwell or even competitive offerings), Nvidia could cater to a broader audience, particularly businesses that don't require the massive compute power of a Hopper H100 80GB cluster for every workload. Regardless, Nvidia's new DGX Cloud Benchmarking Recipes look like a very helpful resource for evaluating the performance of AI compute infrastructure, before making major investment decisions. They offer a practical way to understand how different configurations—whether cloud-based or on-premises—handle complex AI workloads. This is especially valuable for organizations exploring which cloud provider best meets their needs, or if the company is looking for new ways to optimize existing infrastructure. As AI's role in business and our everyday lives continues to grow, tools like this will become essential for guiding infrastructure decisions, balancing performance versus cost and power consumption, and optimizing AI applications to meet real-world demands. As Nvidia expands these recipes to include more inference-focused benchmarks and potentially expands its reference data with a wider range of GPU options, these tools could become even more indispensable to businesses and developers of all sizes.
