Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in ...

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

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

Google researchers have proposed TurboQuant, a two-stage quantization method that, according to a recent arXiv preprint, can ...

Sandisk Corp.’s NAND thesis stays strong. Learn why the SNDK stock dip may be headline-driven and why it could retest highs.

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in which the probabilities of tokens occurring in a specific order is ...

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 ...

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 ...

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

Modern multicore systems demand sophisticated strategies to manage shared cache resources. As multiple cores execute diverse workloads concurrently, cache interference can lead to significant ...