Nvidia researchers have developed a new technique, dynamic memory sparsification (DMS), that has slashed the memory needs of large language models (LLMs) by a factor of eight, according to multiple reports. This breakthrough, coupled with the development of a lightweight C library called vdb, promises to significantly reduce the computational bottlenecks hindering the wider adoption of LLMs in real-world applications.
The DMS technique compresses the key value (KV) cache, allowing LLMs to process more information without sacrificing speed or accuracy, according to reports. This innovation enables LLMs to "think" longer and explore more solutions, potentially overcoming a major hurdle in enterprise adoption, as stated in a VentureBeat report.
Simultaneously, a header-only C library named vdb has been created to efficiently store and search high-dimensional vector embeddings. This library, as detailed on Hacker News, offers features such as multiple distance metrics (cosine, euclidean, dot product), optional multithreading support, and the ability to save and load databases to and from disk. The library is designed to be lightweight, with no dependencies except pthreads for multithreading.
The vdb library is implemented in a single header file, vdb.h. Its usage involves including the header file and compiling with a C compiler. The library allows users to create a database, add vectors, and search for similar vectors using various distance metrics. Python bindings are also available, as noted on Hacker News.
The combination of DMS and vdb offers a promising solution for reducing the costs and improving the performance of LLMs. By compressing the KV cache and providing an efficient vector database, Nvidia is aiming to make LLMs more accessible and practical for a wider range of applications.
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