Production LLM Optimization — A Practical Guide
Techniques for optimizing LLM inference in production environments including quantization, KV-cache management, speculative decoding, and batching strategies.
2026 update note
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Editorial note for 2026. This does not replace the historical article below.
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Production LLM Optimization
Inference Optimization
Quantization (4-bit reduces memory 4x), KV-cache management (page-based allocation), continuous batching (dynamic request packing), speculative decoding (draft model + target model verification).
Serving Infrastructure
vLLM provides PagedAttention for near-zero memory waste. TensorRT-LLM optimizes for NVIDIA GPUs. All three support continuous batching.
Monitoring
Track TTFT (time to first token), TPOT (time per output token), throughput, and cache hit rate. Latency budgets typically target TTFT < 500ms, TPOT < 50ms.
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