Nvidia’s new technique cuts LLM reasoning costs by 8x without losing accuracy

Nvidia researchers developed dynamic memory sparsification (DMS), a technique that compresses the KV cache in large language models by up to 8x while maintaining reasoning accuracy — and it can be ...

'Observational memory' cuts AI agent costs 10x and outscores RAG on long-context benchmarks

As AI agents move into production, teams are rethinking memory. Mastra’s open-source observational memory shows how stable ...
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New memory structure helps AI models think longer and faster without using more power

Researchers from the University of Edinburgh and NVIDIA have introduced a new method that helps large language models reason more deeply without increasing their size or energy use. The work, ...
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ZeroPoint’s nanosecond-scale memory compression could tame power-hungry AI infrastructure

AI is only the latest and hungriest market for high-performance computing, and system architects are working around the clock to wring every drop of performance out of every watt. Swedish startup ...