Estimate LLM KV cache memory with a simple formula to avoid VRAM errors
Tactic · dev.to · stat: 340GB LLM inference engineers can use a simple formula to estimate VRAM usage from the KV cache, a primary cause of out-of-memory errors. The calculation multiplies layers,…
Tactic · dev.to · stat: 340GB
LLM inference engineers can use a simple formula to estimate VRAM usage from the KV cache, a primary cause of out-of-memory errors. The calculation multiplies layers, hidden dimensions, context length, and bytes per parameter. For a Llama 3.1 70B model at 128K context, the cache alone requires 340GB.
Long context makes KV cache the primary VRAM bottleneck, not weights. Founders must budget for inference VRAM based on context length and batch size, not just static model weight.
Every claim ties to a primary source. See our methodology.