AI's insatiable appetite for memory chips is crowding out all other buyers — and the consequences will ripple through every ...

Nvidia's KV Cache Transform Coding (KVTC) compresses LLM key-value cache by 20x without model changes, cutting GPU memory costs and time-to-first-token by up to 8x for multi-turn AI applications.

This approach can be viewed as a memory plug-in for large models, providing a fresh perspective and direction for solving the ...

What if your AI could remember every meaningful detail of a conversation—just like a trusted friend or a skilled professional? In 2025, this isn’t a futuristic dream; it’s the reality of ...

Researchers at the Tokyo-based startup Sakana AI have developed a new technique that enables language models to use memory more efficiently, helping enterprises cut the costs of building applications ...

The term “memory wall” was first coined in the 1990s to describe memory bandwidth bottlenecks that were holding back CPU performance. The semiconductor industry helped address this memory wall through ...

In the fast-paced world of artificial intelligence, memory is crucial to how AI models interact with users. Imagine talking to a friend who forgets the middle of your conversation—it would be ...

Memories.ai is building a large visual memory model that can index and retrieve video-recorded memories for physical AI.