As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

Google Research published TurboQuant on Tuesday, a training-free compression algorithm that quantizes LLM KV caches down to 3 bits without any loss in model accuracy. In benchmarks on Nvidia H100 GPUs ...

If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what ...

Google said this week that its research on a new compression method could reduce the amount of memory required to run large language models by six times. SK Hynix, Samsung and Micron shares fell as ...

Micron Technology (MU) shares fell to $339 Monday as fears over Alphabet’s (GOOGL) TurboQuant AI memory-compression algorithm raised concerns about long-term demand for high-bandwidth memory across ...

Micron (MU) reported Q1 FY2026 revenue of $13.64B, up 57% year-over-year with non-GAAP EPS of $4.78, but capital expenditures surged 68% to $5.39B in a bet on sustained AI-driven memory demand.

Google said its algorithm can cut the amount of memory required to run a specific aspect of LLMs by at least a factor of six, helping reduce the overall cost of AI. Investors fear this could reduce ...

The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” ...