The original title is about inference optimization techniques. Let me rewrite this as a punchy mobile feed headline.

Short summary

A practical guide to LLM inference optimization for teams without hyperscaler budgets, covering KV cache management, quantization schemes, and latency tradeoffs. The key insight is that KV cache memory—not model weights—typically limits concurrent users on a single GPU, and quantization choice should be benchmarked per task rather than guessed. Includes concrete sizing formulas, a quantization comparison table, and routing code for selecting precision levels by task profile.

• •KV cache memory, not model weights, is usually the bottleneck for concurrent requests on a single GPU
• •Quantization level should be benchmarked per task—structured extraction degrades faster than casual chat at int4
• •Continuous batching and PagedAttention-style memory management are the highest-leverage techniques to borrow from vLLM/TGI

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