The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...

Enterprise AI teams are moving beyond single-turn assistants and into systems expected to remember preferences, preserve project context and operate across longer horizons.

Google unveils TurboQuant, PolarQuant and more to cut LLM/vector search memory use, pressuring MU, WDC, STX & SNDK.

A more efficient method for using memory in AI systems could increase overall memory demand, especially in the long term.

Google’s TurboQuant cuts KV cache memory, but Morgan Stanley says cheaper AI inference will boost demand for DRAM/storage.

When Aquant Inc. was looking to build its platform — an artificial intelligence service that supports field technicians and agents teams with an AI-powered copilot to provide personalized ...

Memory stocks fell Wednesday despite broader technology sector strength, with shares dropping after Google unveiled ...

Shares of memory and storage-related companies, including Micron Technology Inc MU and SanDisk Corp SNDK, are trading lower ...

Kioxia America, Inc. today announced the successful demonstration of high-dimensional vector search scaling to 4.8 billion vectors on a single server using its open-source KIOXIA AiSAQ(TM) approximate ...

The latest trends in software development from the Computer Weekly Application Developer Network. This week sees the move to general availability for vector search for Amazon MemoryDB. Amazon MemoryDB ...

The rapid evolution of semiconductor devices has amplified the demand for advanced automated test equipment (ATE) that can handle increasingly complex test scenarios for logic devices. ATE vector ...