The headline is about KV Cache consuming VRAM and how to estimate it. Let me rewrite this following the rules.

Short summary

LLM inference engineers commonly hit out-of-memory on production models due to KV cache growth, not model weights. Use the formula: 2 × layers × hidden_dim × context_length × bytes_per_param to estimate per-token cache size. Apply optimizations in priority order: vLLM (30-50% savings), FP8 quantization (2× reduction), GQA architectures (4-8× reduction), or reduced context windows.

• •KV cache grows linearly with batch size and context length, often exceeding model weight memory at production scale
• •Use the provided formula to estimate cache requirements before deployment to avoid OOM errors
• •Six optimization levers ranked by impact: vLLM memory packing, FP8 quantization, GQA architectures, sliding window, batch reduction, context limits

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