RMACC 2026

As universities race to provide generative AI access to students and faculty, the cost of commercial subscriptions at institutional scale quickly becomes unsustainable. At Boise State University, we built boisestate.ai—an open source AI platform powered by AWS Bedrock. This presentation shares the practical lessons learned from developing and operating the platform, including the consumption-based paradigm shift associated with pay-per-token pricing, seven specific cost optimization strategies (from prompt caching to semantic tool filtering), and approaches for making AI institutionally aware through MCP servers and agent skills. We'll also discuss why 2026 is shaping up to be the year of the AI agent, and how progressive disclosure and codified institutional knowledge are key to building AI that doesn't just chat — but actually gets work done for your campus.

Follow Montana State University’s journey to implement a flexible and open-source generative AI suite, backed by our Tempest HPC system. Learn how we were able to leverage existing infrastructure and open-source platforms to provide powerful tools to all our faculty, staff, and students with no direct cost or token restrictions. Takeaways and how the local service coheres with a broader AI portfolio will be discussed.

Growing data volumes, new computing paradigms, and increasing hardware heterogeneity are driving the need to execute code on diverse distributed computing resources, many of which are outside the bounds of the researcher's institution. This need may be driven by (a) the desire to compute closer to data acquisition sources, (b) exploit specialized computing resources such as hardware accelerators, (c) provide real-time processing of data, (d) reduce energy consumption (e.g., by matching workload with hardware), and (e) scale simulations beyond the limits of a single computer.

Globus Compute addresses these needs by delivering a hybrid cloud platform implementing the Function-as-a-Service (FaaS) paradigm. Researchers first register their desired function with a cloud-hosted service, they can then request invocation of that function with arbitrary input arguments to be executed on remote cyberinfrastructure. Globus Compute manages the reliable and secure execution of the function, provisioning resources, staging function code and inputs, managing safe and secure execution (optionally using containers), monitoring execution, and asynchronously returning results to users via the cloud platform.

This tutorial will describe use cases for FaaS in science and demonstrate how Globus Compute can provide a common interface and approach for portable execution across different systems. Attendees will experiment with Globus Compute on virtual machines and learn how to deploy Globus Compute on their HPC cluster or other advanced computing system.

This panel will explore the role of artificial intelligence in high-performance computing (HPC) environments, highlighting innovative applications that enhance computational efficiency and data analysis. Attendees will gain insights into future trends and collaborative strategies for leveraging AI.

Discover how Google Cloud works with High Performance Computing by integrating specialized infrastructure and managed storage with powerful, cloud-native orchestration tools. This session highlights how these advancements accelerate scientific discovery and engineering design, providing the fastest time-to-insight for mission-critical research.

This work describes a scalable system for automated patent search and analysis that integrates large language models with function calling to support data retrieval and classification. The approach combines conventional data extraction from the US Patent and Trademark Office with semantic similarity search and structured function execution to enable accurate and reproducible patent management applicable to real-world institutional data analysis challenges.