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.
Nvidia's KV Cache Transform Coding (KVTC) compresses LLM key-value cache by 20x without model changes, cutting GPU memory ...
The research introduces a novel memory architecture called MSA (Memory Sparse Attention). Through a combination of the Memory ...
Why it matters: A RAM drive is traditionally conceived as a block of volatile memory "formatted" to be used as a secondary storage disk drive. RAM disks are extremely fast compared to HDDs or even ...
How lossless data compression can reduce memory and power requirements. How ZeroPoint’s compression technology differs from the competition. One can never have enough memory, and one way to get more ...
For the past few years, AI infrastructure has focused on compute above all other metrics. More accelerators, larger clusters and higher FLOPS drove the conversation to make the most of GPUs. This ...
Accelerating memory-dependent AI processes, Penguin's MemoryAI KV cache server increases memory capacity by integrating 3 TB ...
Lightbits Labs Ltd. today is introducing a new architecture aimed at addressing one of the most stubborn bottlenecks in large ...
This breakthrough could make AI far more practical for large-scale use as the method promises to cut cloud computing costs and process huge datasets faster.
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