Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the ...

On March 25, 2026, Google Research published a paper on a new compression algorithm called TurboQuant. Within hours, memory ...

TurboQuant vector quantization targets KV cache bloat, aiming to cut LLM memory use by 6x while preserving benchmark accuracy ...

The dynamic interplay between processor speed and memory access times has rendered cache performance a critical determinant of computing efficiency. As modern systems increasingly rely on hierarchical ...

SK Hynix, Samsung and Micron shares fell as investors fear fewer memory chips may be required in the future.

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

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

Enterprise AI applications that handle large documents or long-horizon tasks face a severe memory bottleneck. As the context grows longer, so does the KV cache, the area where the model’s working ...

A technical paper titled “HMComp: Extending Near-Memory Capacity using Compression in Hybrid Memory” was published by researchers at Chalmers University of Technology and ZeroPoint Technologies.