Nvidia's KV Cache Transform Coding (KVTC) compresses LLM key-value cache by 20x without model changes, cutting GPU memory costs and time-to-first-token by up to 8x for multi-turn AI applications.
This approach can be viewed as a memory plug-in for large models, providing a fresh perspective and direction for solving the ...
MIT researchers developed Attention Matching, a KV cache compaction technique that compresses LLM memory by 50x in seconds — ...
Researchers from the University of Edinburgh and NVIDIA have introduced a new method that helps large language models reason more deeply without increasing their size or energy use. The work, ...
A technical paper titled “HMComp: Extending Near-Memory Capacity using Compression in Hybrid Memory” was published by researchers at Chalmers University of Technology and ZeroPoint Technologies.
GOTHENBURG, Sweden, Feb. 20, 2025 /PRNewswire/ -- ZeroPoint Technologies AB today announced a breakthrough hardware-accelerated memory optimization product that enables the nearly instantaneous ...
Video compression has become an essential technology to meet the burgeoning demand for high‐resolution content while maintaining manageable file sizes and transmission speeds. Recent advances in ...
Forward-looking: It's no secret that generative AI demands staggering computational power and memory bandwidth, making it a costly endeavor that only the wealthiest players can afford to compete in.
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