KV Cache Growth is Overtaking Parameter Count as the Primary VRAM Bottleneck

A popular community post highlights that for long-context LLM inference (such as 100k to 200k tokens), KV cache memory usage and bandwidth constraints are becoming larger VRAM bottlenecks than static model parameter sizes. Every generated token increases key and value memory states, causing models that easily fit into GPU memory at start to run out of VRAM under deep context. This shift suggests future local LLM optimization will prioritize reducing persistent memory state and data movement rather than solely shrinking model weights via quantization. It fundamentally changes how open-weight models must be designed and deployed on consumer hardware with restricted VRAM capacity. While architectural techniques like Grouped-Query Attention (GQA), Multi-Query Attention (MQA), and KV cache quantization reduce the memory footprint per token, the cache still grows linearly with context length. Consequently, memory movement and persistent state retention become the core bottlenecks for long-context local inference.