Understanding KV Cache: The Hidden Memory Cost of Serving LLMs (melchi.me)

A recent discussion on Key-Value (KV) cache has unveiled the often-overlooked memory implications for self-hosting large language models (LLMs). While many focus solely on the model parameters—like the approximately 140 GB needed for a 70B model in BF16 mode—KV cache adds significant memory consumption during inference. As KV cache scales with the number of tokens processed and the number of concurrent requests, it can quickly balloon to as much as 1.25 TiB for 16 users with long contexts. This hidden memory cost underscores the importance of careful planning for VRAM when self-hosting LLMs. The article dives into various strategies to manage KV cache more efficiently, such as Multi-Query Attention (MQA) and Grouped-Query Attention (GQA), which can drastically cut down on KV memory usage while maintaining model performance. For instance, MQA reduces the KV cache by 98.4% compared to a typical Multi-Head Attention (MHA) configuration by sharing K and V across query heads. These advancements not only help in minimizing the resource footprint on GPUs but also boost the scalability of LLMs, allowing developers to optimize memory allocation effectively. This knowledge will be vital for those looking to enhance the performance and efficiency of their AI applications.