Kove's Software-Defined Memory Boosts AI Inference Workloads

September 11, 2025
Kove has announced benchmark results showing that its software-defined memory solution can handle AI inference workloads up to five times larger than local DRAM, with reduced latency.

Kove has announced benchmark results demonstrating that its software-defined memory solution, Kove:SDM™, can handle AI inference workloads up to five times larger than local DRAM, with reduced latency. This announcement was made during CEO John Overton's keynote at the AI Infra Summit 2025, announced in a press release.

Kove:SDM™ allows for dynamic memory allocation across servers, creating larger elastic memory pools that perform like local DRAM. This innovation addresses the memory bottleneck in AI inference, enabling faster processing and reducing energy consumption. Benchmarks conducted on Oracle Cloud Infrastructure showed significant performance improvements for Redis and Valkey, two widely used engines in AI inference.

The results indicate that Kove:SDM™ can accelerate AI inference by eliminating key-value cache evictions and redundant GPU computations, potentially saving enterprises millions annually. The solution is available now and can be deployed without changes to existing applications or code, running on any x86 hardware supported by Linux.

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