James Corcoran

Time series, machine learning, and risk analytics continue to push the limits of compute, memory, and system design. The latest STAC Benchmark results provide independent, reproducible performance data across leading hardware and software platforms.

Time series, machine learning, and risk analytics continue to push the limits of compute, memory, and system design. The latest STAC Benchmark results provide independent, reproducible performance data across leading hardware and software platforms.

Time series and risk analytics workloads continue to push the limits of compute, memory, and system design. The latest STAC Benchmark results provide independent, reproducible performance data across leading hardware and software platforms.

Independent, reproducible performance data is critical as firms deploy machine learning and AI systems into production trading and analytics workflows. In this session, James will disclose the latest STAC ML and AI benchmark results, providing objective comparisons across hardware and software platforms.

Time series and risk analytics workloads continue to push the limits of compute, memory, and system design. The latest STAC Benchmark results provide independent, reproducible performance data across leading hardware and software platforms.

Independent, reproducible performance data is critical as firms deploy machine learning and AI systems into production trading and analytics workflows. This session delivers the latest STAC ML and AI benchmark results, providing objective comparisons across hardware and software platforms.

Time series and risk analytics workloads continue to push the limits of compute, memory, and system design. The latest STAC Benchmark results provide independent, reproducible performance data across leading hardware and software platforms.

Independent, reproducible performance data is critical as firms deploy machine learning and AI systems into production trading and analytics workflows. This session delivers the latest STAC ML and AI benchmark results, providing objective comparisons across hardware and software platforms.

Time series and risk analytics workloads continue to push the limits of compute, memory, and system design. The latest STAC Benchmark results provide independent, reproducible performance data across leading hardware and software platforms.

Independent, reproducible performance data is critical as firms deploy machine learning and AI systems into production trading and analytics workflows. This session delivers the latest STAC ML and AI benchmark results, providing objective comparisons across hardware and software platforms.

Time series and risk analytics workloads continue to push the limits of compute, memory, and system design. The latest STAC Benchmark results provide independent, reproducible performance data across leading hardware and software platforms.

Independent, reproducible performance data is critical as firms deploy machine learning and AI systems into production trading and analytics workflows. This session delivers the latest STAC ML and AI benchmark results, providing objective comparisons across hardware and software platforms.

In this session, James will present the latest STAC-ML benchmark, which includes evaluation metrics for platforms running Gradient Boosted Trees. James will also highlight some of the findings the STAC research team uncovered in the development of this new benchmark, and present some of the performance numbers observed on AWS, Intel, and AMD stacks.

In this session, James will present STAC research covering LLM inferencing platforms, highlighting where bottlenecks occur and performance tradeoffs exist. He'll also unveil the latest STAC-AI research and benchmark results.

In this session, James will present the latest STAC-ML benchmarks, which includes evaluation metrics for platforms running Gradient Boosted Trees in addition to neural network models. James will also highlight some of the findings the STAC research team uncovered in the development of this new benchmark, and present the performance numbers observed on AWS, Intel, and AMD stacks.

In this session, James will present STAC research covering LLM inferencing platforms, highlighting where bottlenecks occur and performance tradeoffs exist. He'll also unveil the latest STAC-AI research and benchmark results.

In this session, James will present STAC research covering LLM inferencing platforms, highlighting where bottlenecks occur and performance tradeoffs exist. He'll also unveil the latest STAC-AI benchmark results and submissions.

In this session, James will present the latest STAC-ML benchmark, which includes evaluation metrics for platforms running Gradient Boosted Trees. James will also highlight some of the findings the STAC research team uncovered in the development of this new benchmark, and present some of the performance numbers observed on AWS, Intel, and AMD stacks.

Join James Corcoran for an in-depth look at the latest results from the STAC-AI benchmark, including our recent benchmark audits. This session will also introduce new STAC research exploring the performance trade-offs of LLM serving frameworks.

Join James Corcoran for an in-depth look at the latest results from the STAC-AI benchmark, including our recent benchmark audits. This session will also introduce new STAC research exploring the performance trade-offs of LLM serving frameworks.

Join James Corcoran for an in-depth look at the latest results from the STAC-AI benchmark, including our recent benchmark audits. This session will also introduce new STAC research exploring the performance trade-offs of LLM serving frameworks.

James will provide updates on STAC-AI™, an LLM benchmark suite guided by the priorities of financial firms, which measures a full solution stack -- from the model to the metal™, including the first audit results and insights.