NVIDIA’s AI systems are helping startups develop robocars and grocery stores transform into giant supercomputers. NVIDIA’s DRIVE GPUs power these systems. This technology also enables the company to accelerate research in fields like drug discovery and healthcare. It has even helped make environmental predictions.
Graphcore’s “intelligence-processing unit” transforms grocery stores into giant supercomputers
In a world where AI is a key element in every job, Graphcore’s “intelligence-processor unit” could turn grocery stores into giant supercomputers. The company’s AI chip is based on a 3D wafer-on-wafer processor. It has been designed for production systems as well as prototypes. The AI chip is suitable for both production and prototype systems and can be scaled up to supercomputer levels.
In the research, Graphcore has drawn from principles and new techniques in natural-language processing. Zohren likened the Oxford-Man model to a computer program that can translate sentences into English. To achieve this feat, an AI program needs to be able to process large amounts of data quickly. The Oxford-Man model relies on a pizza-box-sized system called the Graphcore “intelligence-processing unit” (IPU). The new chip is 10 times faster than a standard GPU.
Graphcore’s new chips will accelerate the training of AI systems. The company says they will also help to reduce power consumption. The company has also announced that its “Good Computer” chip will be among the most powerful AI supercomputers by 2024. This is a groundbreaking move for the AI industry. It could lead to the widespread adoption of AI technology in the automotive industry.
Graphcore’s IPU is a new kind of processor that can be used to speed up machine intelligence. Its massively parallel chip architecture allows it to deliver state-of-the-art performance and support for new and advanced AI models. It also enables AI researchers to tackle new types of work previously impossible.
But the company has not yet revealed how many IPUs it will be able to fit in a grocery store’s nook. The Good Supercomputer could be capable of supporting larger models than Cerebras’ recently announced system, which clusters 192 C-2s together. Depending on the configuration, it could cost up to $120 million. Graphcore plans to give more details about its Good Supercomputer in future quarters.
HPE’s GreenLake platform
HPE and NVIDIA have partnered to bring NVIDIA AI Enterprise software to the HPE GreenLake platform. The suite provides end-to-end cloud-native AI solutions, optimized for any organization. With AI enterprise software, organizations can easily deploy, manage, and benefit from AI technology.
The HPE GreenLake platform for HPC includes HPE Apollo 6500 Gen10 servers that support NVIDIA GPU A100, A40, and A30 models. It also includes HPE Slingshot high-performance networking, which enables large data-intensive workloads. It also offers HPE Parallel File System Storage for higher throughput and lower latency.
HPE’s GreenLake platform allows HPE customers to deploy cloud-native applications and legacy workloads. It offers a service level agreement (SLA) and guaranteed uptime. Similar to other public cloud services such as Amazon Web Services and Google Cloud, GreenLake offers a variety of tools and infrastructure to help organizations manage and run their applications.
HPE also announced a private cloud offering. The platform consists of multiple layers of cloud computing, edge devices, and management. HPE’s GreenLake private cloud offers bare-metal infrastructure, virtualized environments, and managed services. It also offers cloud management and monitoring.
HPE GreenLake for NVIDIA AI Enterprise is a managed solution that minimizes risk and expense associated with an enterprise-grade AI platform. It also comes with a unified control panel and centralized analytics. This platform is ideal for large-scale AI deployments.
HPE has also made acquisitions to beef up its AI offerings. In the last year alone, it acquired Determined AI, a San Francisco-based company, and Zetro, a software provider. Both companies offer software that allows ML engineers to quickly and easily implement models.
HPE’s GreenLake platform offers an integrated cloud ecosystem that includes storage, compute, virtual machines, and machine learning operations. It also offers 24×7 monitoring of the infrastructure. It is easy to install and manage, and comes with a free trial.
The company is committed to improving its GreenLake platform with new features and AI services. It’s also focused on implementing innovative solutions that integrate AI and ML.
NVIDIA’s AI Enterprise 2.0
NVIDIA’s AI Enterprise 2.0 includes multiple data science and AI tools. These include TensorFlow, PyTorch, RAPIDS, Triton Inference Server, and TensorRT. This suite is geared towards organizations looking to implement AI into their business processes.
The AI Enterprise 2.0 suite includes a low-code toolkit for developing and deploying machine-learning workloads. It also includes support for Red Hat OpenShift and Domino Data Lab’s ML operations platform. It’s also compatible with VMware vSphere deployments. NVIDIA touts AI Enterprise as a one-stop shop for developing and deploying enterprise AI workloads.
The NVIDIA AI Enterprise 2.0 software suite is cloud-native and designed to accelerate AI deployment across industries. It is integrated with Red Hat OpenShift, the industry’s leading enterprise Kubernetes platform. With this integration, customers can deploy AI-powered applications on virtualized or bare metal platforms.
Nvidia and Red Hat have worked together to enable Nvidia GPUs in OpenShift Science cluster. They’ve also extended their Cloud Service offering to include NVIDIA’s AI Enterprise. Both companies are developing AI solutions aimed at bringing more businesses into the AI business.
HPE Ezmeral and NVIDIA have partnered for several years. They’ve validated NVIDIA RAPIDS on HPE Ezmeral, which makes GPU acceleration more efficient in workloads and reduces data prep time. NVIDIA’s AI Enterprise software suite also integrates with popular AI frameworks like TensorFlow and PyTorch. Using this software, enterprises can build AI solutions without deep AI expertise.
AI enterprise 2.0 also includes customer support and data science tools. These tools help organizations run their workloads faster and more effectively. It also includes operator support for cloud deployments and virtualized GPU offerings. In addition, AMD launched the TAO toolkit last month, which is a pre-programmed starter kit for common workloads that requires only a few lines of coding. It also does not require the user to be an expert in Python or TensorFlow.
NVIDIA’s AI platform continues to dominate AI benchmarks. It accounted for 90 percent of the entries in MLPerf 2.0 benchmarks, which measures AI inference performance. The benchmarks are based on AI use cases such as speech recognition, natural language processing, object detection, and image classification.