#efficiency

Not all optimizations are equal. Prefix caching saves 40%. Quantization saves 50%. Smart routing saves 60%. Know which levers move the needle for your workload.

One GPU can serve many customers without sharing data. Isolation at the request level, not the hardware level. The economics work when you get it right.

Full fine-tuning updates billions of parameters. LoRA updates millions. The 0.1% of parameters can capture 80% of the adaptation. Know when that's enough.

Full attention is O(n²). Sliding window attention is O(n). The trade: lose long-range dependencies, gain linear scaling. Often worth it.

Most queries don't need the full context. Selecting the right 12% often preserves 95% of quality at a fraction of the cost and latency.

Standard attention needs O(n²) memory. Memory-efficient variants need O(n). Same output, 10x less peak memory.