Understanding KV Cache: The Hidden Memory Cost of Serving LLMs

Tue, 19 May 2026 17:45:00 +1000

How attention architectures evolved to keep KV cache from eating your GPU, and what that means if you self-host.

Already comfortable with KV cache and attention? Skip the theory and jump straight to the interactive KV Cache Calculator to size VRAM for your model, batch size, and target GPU.

If you’re planning to self-host a large language model, you’ve probably sized VRAM based on parameters alone. A 70B model in BF16 needs roughly 140 GB just for weights. That’s the easy part: 70 billion parameters × 2 bytes.

Self-Hosting on Melchi

Understanding KV Cache: The Hidden Memory Cost of Serving LLMs