NVIDIA Researchers Introduce KVTC Transcoding Pipeline to Compress Key-Value Caches by 20x for Efficient LLM Performance
Running Large Language Models (LLMs) at scale is a major engineering challenge due to the management of the Key-Value (KV) repository. As models grow in size and processing power, KV’s cache footprint grows and becomes a major bottleneck in throughput and performance. In modern Transformers, this cache can take many gigabytes. NVIDIA researchers presented KVTC … Read more