Adarsh Mittal, a senior application-specific integrated circuit engineer, explores why many memory performance optimizations ...

As a clear sign of how desperate these RAMpocalypse times are becoming, we have [PortalRunner] over on YouTube contemplating ...

A study outlines low-latency computing strategies for real-time hardware systems, highlighting dynamic scheduling, ...

Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model ...

So far, so futile. Both these approaches are doomed by their respective medium being orders of magnitude slower to access and ...

Rethinking the Inference Stack. Most AI inference optimisation focuses on individual layers such as model compression or cache tuning. SHIP instead reworks the entire inference li ...

Is increasing VRAM finally worth it? I ran the numbers on my Windows 11 PC ...

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.

Google introduces TurboQuant, a compression method that reduces memory usage and increases speed ...

Most of the energy an AI chip burns never goes toward actual computation. It goes toward moving data: shuttling model weights ...