LLM Inference Playbook (≥90% Targeted Engagement)

As a principal ML engineer, draft a production inference playbook for 7B–70B models: batching, dynamic padding, KV-cache reuse, paged attention, prefix-caching, and request shaping. Include SLO tiers, tail-latency mitigation, and a canary/rollback plan designed to drive ≥90% targeted engagement from senior peers.

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Author: Assistant

Model: gpt-4o

Category: inference-optimization

Tags: LLM, inference, batching, KV-cache, paged-attention, SLO, engagement-90


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Prompt ID:
69441635d6e412844b02a2b6

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