Once an LLM Hits Production, the Problem Changes: Inference Engineering Explained
mdancho84 · x · 2026-11-13
Data scientists obsess over accuracy; AI engineers obsess over milliseconds and GPU memory—because once an LLM hits production, what matters shifts to latency, throughput, KV cache, batching, GPU utilization, routing, and cost per request. That is Inference Engineering.
The first thing to understand: LLM inference has two very different workloads. Prefill (processing the prompt and building the KV cache) is compute-heavy, while Decode (generating tokens one at a time) tends to be memory-bandwidth bound. This single distinction explains much of modern LLM infrastructure: FlashAttention reduces memory movement, PagedAttention improves KV-cache management, continuous batching keeps GPUs busy, prefix caching avoids repeated work, and GQA/MQA shrink KV memory. You don't need to be a CUDA expert, but these inference-side fundamentals are essential when moving from data science to AI engineering.
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