MOU Terms

Under the terms of the MOU, the companies will evaluate:

  • Technical validation of Everspin MRAM with MaxLinear’s acceleration, compression and encryption platform for AI and data-intensive server workloads
  • Market development initiatives targeting hyperscale cloud, AI infrastructure and enterprise server customers
  • Long-term manufacturing, supply and commercialization opportunities

AI models and inference workloads continue to grow while server architectures are under pressure to support larger data sets, expanding key-value (KV) caches and increased data movement across systems. These demands are driving interest in new approaches that combine persistent memory, acceleration and compression to improve performance, power efficiency and infrastructure utilization.

The MOU establishes a framework for the companies to evaluate the use of Everspin's persistent, low-latency MRAM technology with MaxLinear's storage compression, encryption and hardware acceleration platform. The work will focus on metadata management, log data, write buffering and cache functions, as well as other data-intensive workloads, with the goal of improving responsiveness, reliability and data persistence in next-generation server architectures.

"AI is forcing system architects to rethink where persistent memory fits in the server memory hierarchy," said Sanjeev Aggarwal, president and CEO of Everspin Technologies. "At Everspin, we continue to advance MRAM for applications where speed, persistence and endurance need to work together. We look forward to collaborating with MaxLinear to bring that innovation into AI server architectures that need persistent memory closer to critical data, creating a path for next-generation MRAM-based solutions."

"The rapid growth of AI is driving the need for smarter ways to manage, move and access data across servers," said Vikas Choudhary, senior vice president, connectivity and storage at MaxLinear. "Combining acceleration, compression, and persistent memory creates an opportunity to improve resource utilization, reduce data movement and deliver more predictable performance for demanding AI workloads. Our collaboration with Everspin is focused on evaluating innovative architectures that can help customers scale efficiently as AI deployments continue to expand."