KV Cache, Not Model Size, Is Why Your Local LLM Crashes Mid-Conversation

Developers running large language models locally often encounter out-of-memory crashes not at startup but deep into a conversation, which can make the failure seem random or unexplained. The root cause is a second, largely overlooked memory consumer called the KV cache, which grows with every token the model reads or generates during a session. Most setup guides focus only on fitting the model's weights into available VRAM — a fixed number calculated from parameter count and quantization level — while ignoring the KV cache entirely. Unlike model weights, the KV cache is dynamic and expands continuously as the conversation lengthens, eventually exhausting the GPU memory that appeared sufficient at load time. Understanding and budgeting for KV cache growth alongside model weights is essential for stable long-context inference on consumer hardware.