Cadence’s software is used to turn electronic design concepts into reality for the most dynamic market application, including hyperscale computing, 5G communications, automotive, mobile, aerospace, consumer, industrial, and life sciences. We are excited about the rapid innovation in the Arm processor space and have successfully run a number of our products including Xcelium, Jasper etc. on the memory optimized AWS Graviton4-based Amazon EC2 R8g instances, delivering compelling price performance benefits. The new AWS Graviton4-based Amazon EC2 X8g instances have the largest memory footprint ever offered on a Graviton instance, enabling larger more memory-intensive EDA workloads to run more efficiently than ever.

Siemens EDA customers require a range of software and compute platform solutions in order to address the ever-increasing complexity of IC development. The combination of Siemens EDA software and Amazon EC2 instances provides users with the ability to find the optimal balance of performance and cost-efficiency. The forthcoming AWS Graviton4-based instances family expansion with the higher memory per core in Amazon EC2 X8g instances will open up use for additional categories of EDA software. Siemens is excited to extend support for AWS Graviton across multiple products as part of our deep partnership and mutual commitment to making our mutual customers successful.

As system design complexity increases, there is greater demand for compute and memory resources. With the rise of Arm architecture-based compute instances on the cloud, we're partnering with AWS to help our customers scale their compute-intensive workloads seamlessly on the cloud using our industry-leading AI-driven EDA suite, resulting in faster time-to-market.

Leveraging Amazon EC2 X8g instances with AWS Graviton processors has dramatically improved our ability to process massive datasets for AI training, offering an ideal blend of high memory capacity, energy efficiency, and cost-effectiveness that accelerates our pursuit of advanced machine learning at scale.

Accelerating chip design and implementation while managing cost is critical to our ability to enable our partners with the Arm technology they need to innovate. With increasingly complex processor designs required across an array of application domains, the selection of the optimal compute and memory performance play a key role in innovating more rapidly and reducing our time-to-market. The AWS Graviton4-based Amazon EC2 X8g instances massively expands the applicability of Graviton to important new areas such as silicon layout, while improving the efficiency of existing large memory use cases. In our testing, the X8g instances reduced time-to-results for one such workload - formal verification - by 50% compared to the previous generation X2gd instances.

At DataStax, we are always striving to enhance the performance and cost-efficiency of our AI PaaS for our customers. DataStax Astra DB has been leveraging AWS Graviton instances to deliver outstanding throughput and performance while keeping costs manageable. We are thrilled about the upcoming release of AWS Graviton4-based Amazon EC2 X8g instances and have seen it boost throughput and performance right away. We look forward to incorporating X8g instances into our infrastructure as soon as it becomes generally available, further advancing the capabilities of Astra DB for our users.

Rescale is thrilled to introduce Amazon EC2 X8g instances powered by AWS Graviton4 processors to our customers. The advanced capabilities of the X8g instances, including their impressive memory bandwidth combined with high core count per node and enhanced network bandwidth, will enable us to deliver even more power-efficient and cost-performant computational resources to our customers.

Row Zero workbooks run on Amazon EC2. Previously, we used Amazon EC2 X2gd and R7g instances to strike a balance between performance and costs. The new AWS Graviton4-based Amazon EC2 X8g instances offer compelling performance and cost for our memory-bound workloads. Our key latency-critical workloads such as pivot table operations, large CSV imports, and COUNTIFs saw a 42% performance improvement over previous generation X2gd instances and 28% lower cost per GB of memory than R7g instances. Looking forward, we expect the adoption of X8g instances will deliver an immediate performance boost while reducing our costs.

In November of last year, we announced that SAP HANA Cloud was live on AWS Graviton-based instances, leading to a 35% price performance improvement for analytics workloads, and an estimated 45% reduction in carbon footprint for SAP HANA Cloud. SAP has found that SAP HANA Cloud on AWS Graviton4-based instances shows up to 25% performance improvement for analytical workloads, and up to 40% improvement for transactional workloads compared to AWS Graviton3-based instances. We are excited about the new AWS Graviton4-based Amazon EC2 X8g instances offering up to 3TiB of memory for our larger memory bound solutions. We plan to include X8g instances among other AWS Graviton-based instances across several services in SAP BTP and SAP HANA Cloud.

Vector similarity searches are computationally intensive, requiring powerful hardware for optimal performance. Zilliz Cloud, known for its high performance and scalability, optimizes its service for these demanding workloads. Today, customers utilize AWS Graviton3-based Amazon EC2 R7g instances, offering superior performance and cost-effectiveness compared to the previous R6i solutions. Zilliz Cloud will soon offer instances on AWS Graviton4-based Amazon EC2 X8g instances, providing a powerful scale-up option to the Zilliz Cloud portfolio. These new instances will provide customers with even faster, more efficient vector search services.