* The STAC-M3 measures time-series database performance used in algorithmic trading and backtesting workloads
* Supermicro Petascale servers, Intel Xeon 6 processors, Micron™ 9550 SSDs and DDR5 memory, and KX Software’s kdb+ database were showcased
* Results show that lower latency queries accelerate algorithm testing and increase the number of testable trading strategies
SAN JOSE, Calif. and NEW YORK, Oct. 28, 2025 /PRNewswire/ — Super Micro Computer, Inc. (SMCI), a Total IT Solution Provider for AI/ML, HPC, Cloud, Storage, and 5G/Edge, today announced record-breaking results for the STAC-M3 benchmark, in collaboration with Intel and Micron, at the STAC Summit in New York City. STAC, an independent testing organization focused on the financial industry, leads the STAC Benchmark™ Council with over 500 financial services institutions and more than 70 technology companies participating.
“The record-breaking test results are a testament to both Supermicro’s workload optimized building block product design enabling us to achieve first-to-market leadership and our collaboration with leading technology providers Intel, Micron and KX Software,” said Vik Malyala, SVP Technology & AI at Supermicro. “Supermicro prides itself on its close collaboration with leading industry partners and its ability to deliver the most leading-edge technology to customers to gain a competitive advantage. In the world of high frequency quantitative trading, the time-to-market and performance advantage we’re able to deliver can be measured in our customers’ trading profits.”
For more information on Supermicro’s Petascale servers, please visit All-Flash NVMe Servers for Advanced Computing | Supermicro.
The STAC-M3 benchmark focuses on real-time quantitative trading using simulated market bid-ask and settled trade data for thousands of assets. STAC-M3 is a full-stack benchmark including compute, storage, networking, and software in a multi-user environment. It measures query response time for scenarios often used in algorithmic trading, risk management and trade strategy backtesting commonly performed by banks, hedge funds, quantitative trading firms and trading exchanges. Large data volumes, low latency, and complex processing capabilities are common characteristics of high-frequency trading and analysis environments. Financial firms have a growing need to collect, store, and analyze more data than ever before. Tick data analysis helps firms quickly react to market changes to maximize profits and manage risk.
