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

Google researchers have proposed TurboQuant, a method for compressing the key-value caches that large language models rely on during inference. In a preprint, the team reports up to six times lower KV ...

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

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI chatbots. The cache grows as conversations lengthen, ...

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

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 ...

In the eighties, computer processors became faster and faster, while memory access times stagnated and hindered additional performance increases. Something had to be done to speed up memory access and ...