ScaleFlux Tackles AI Data Center Inefficiencies with Flash Storage Solutions
ScaleFlux is addressing inefficiencies in AI data centers by re-engineering flash storage to boost performance, as announced in a press release. The company has developed custom SSD controllers with hardware-based data compression, which accelerates performance without overburdening main processors.
JB Baker, VP of Marketing and Product Management at ScaleFlux, highlighted the inefficiencies in AI data centers, where storage and memory often lag behind processing power. A significant portion of a data center's power is consumed by cooling, leading to underutilization of high-performance processors. ScaleFlux's solution allows their drives to perform tasks like compression at seven gigabytes per second, using significantly less power than traditional methods.
This innovation not only enhances performance but also translates into substantial cost savings for enterprises, both in terms of reduced operational costs and increased revenue potential.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Silicon Brief or Daily AI Brief.
Also, consider following us on social media:
More from Data Centers
Sep 17 Teledyne LeCroy Releases PCIe 7.0 Protocol Exerciser Sep 17 XKL Adds Low Latency Optical Links for Distributed AI Sep 17 Sentry AI deploys 100th SolarGuard security trailer Sep 17 Terragrit Secures Investment From National Grid Partners Sep 17 FirstLight Fiber Signs Hyperscaler for 240 Mile Albany to Boston RouteSilicon Brief
Weekly coverage of AI hardware developments including chips, GPUs, cloud platforms, and data center technology.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
NVIDIA Invests in Cloverleaf Infrastructure for US AI Data Centers
DIMAAG-AI and Toshiba Develop Battery Storage for AI Data Centers
XKL Adds Low Latency Optical Links for Distributed AI
Huawei Introduces OceanStor M900 Storage for AI Inference
New Constructs Warns AI Compute Spending Is Not Profit
Daily AI Brief: the AI news that matters, in your inbox.