Company Background

The Texas Advanced Computing Center (TACC) at the University of Texas at Austin is a National Science Foundation (NSF) funded research center supporting the full spectrum of scientific inquiry — from engineering and the natural sciences to the humanities, social sciences, and cultural studies. Through large-scale modeling, simulation, and real-world data analysis, TACC enables breakthrough discoveries that have contributed to multiple Nobel Prize-winning research programs.

What TACC Does

TACC operates some of the world's largest clusters for open compute, including the Frontera supercomputer, one of the most powerful systems in the world at its launch, and the next-generation Horizon supercomputer, which delivers 10x Frontera's performance.

AI is central to how TACC empowers its user community. Internally, AI coding agents help researchers build and understand software modules, answer complex questions, and accelerate new discoveries. Across thousands of users spanning a wide range of disciplines, AI enables researchers to analyze vast quantities of complex data and surface insights that would otherwise be out of reach. 

Challenge: High-Performance AI Within Strict Power and Space Constraints

TACC supports thousands of users with an exceptionally diverse range of workloads, but not every application demands supercomputer-class resources. Many workloads, such as coding, run on systems that must fit within strict power, cooling, and floor space constraints while still delivering exceptional performance.

TACC's requirements were specific and demanding:

• Fast, low-latency coding models. Researchers use coding agents to create and manage projects and to analyze previous work to plan next steps. They need access to the latest and best coding models, such as MiniMax M2.7. 
• Broad open-source model support. As a research institution, almost everything TACC runs is open source so users can easily collaborate. Any system in their data center must run a broad range of open-source models.
• Energy efficiency as a core priority. While TACC's largest supercomputers consume up to 15 MW, the center also needs to run lighter workloads cost-effectively, making power consumption a decisive factor when evaluating systems.
• A fit for existing data center infrastructure. New systems needed to slot into current power, cooling, and floor-space limits without costly retrofits.

Solution: SambaStack™ for Efficient, Open, Low-Latency Inference

TACC uses SambaStack™ to power a broad range of AI workloads. Its OpenAI-compatible API makes integration straightforward, allowing TACC to incorporate SambaStack into existing infrastructure, including Splunk-based logging and other internal services, without disruption.

“The technology is fantastic," said Niall Gaffney, director of Data Intensive Computing, TACC. "We’re always about better solutions for solving problems.”

For TACC's engineers, that OpenAI-compatible endpoint meant no rewrite of existing pipelines because SambaStack dropped straight into their toolchain.

Coding Agents Powered by MiniMax M2.7

Many of the researchers at TACC take advantage of coding agents so that they no longer have to write test modules or comment their code manually. SambaStack's support for leading open-source models enables it to run the full suite of coding agents TACC relies on, including MiniMax M2.7.

Air-Cooled Design: Reliability Without the Leak Risk

Reliability and simplicity of deployment also matter. Other systems in TACC's data center require full liquid cooling, and have experienced coolant leaks. SambaStack's air-cooled design eliminates that risk and simplifies installation, dropping into TACC’s existing data center without specialized cooling infrastructure.

Energy Savings vs. GPU-Based Alternatives

Combined with a lower energy footprint than other systems, SambaStack lets TACC run the workloads it needs, reduce operating costs, and stay within the physical constraints of its existing data center.

Why TACC Chose SambaStack

TACC chose SambaStack because it delivered high-performance AI inference while solving the operational constraints that ruled out other systems. Five factors drove the decision:

1. Air-cooled design. No liquid cooling means no coolant-leak risk and a straightforward install in TACC's existing data center.
2. Energy efficiency. Lower power draw than GPU-based alternatives keeps lighter workloads cost-effective, which is critical for a data center already managing up to 15 MW of supercomputing load.
3. OpenAI-compatible API. SambaStack integrated cleanly with TACC's existing infrastructure, including Splunk-based logging, with no pipeline rewrites.
4. Broad open-source model support. SambaStack runs the open-source models TACC's collaborative research community depends on, including MiniMax M2.7.
5. Low-latency inference for coding agents. Fast execution. With industry-leading performance on MiniMax M2.7, researchers' agentic coding workflows stay responsive.

Gaffney finished by saying, "Working with SambaNova is working with them. Working with the team is what has made the difference."

The result: TACC runs the AI workloads its community needs, reduces operating costs, and fits everything within the power, cooling, and floor-space limits of its existing facility. The best of both worlds: flexibility and efficiency in one system.