The algorithm achieves up to an eight-times performance boost over unquantized keys on Nvidia H100 GPUs.

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 ...

MIT researchers developed Attention Matching, a KV cache compaction technique that compresses LLM memory by 50x in seconds — without the hours of GPU training that prior methods required.

Google has published TurboQuant, a KV cache compression algorithm that cuts LLM memory usage by 6x with zero accuracy loss, ...

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.

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, ...

Google’s announcement of TurboQuant is weighing on the share prices of memory companies, as the technology is expected to cut artificial ...

Accelerating memory-dependent AI processes, Penguin's MemoryAI KV cache server increases memory capacity by integrating 3 TB of DDR5 main memory and up to eight 1 TB CXL Add-in Cards (AICs). Penguin ...

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 ...

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