From Accuracy to Latency: Why Inference Engineering Is a Different Discipline
mdancho84 · x · 2026-09-12
The author argues that once an LLM hits production, priorities shift from model accuracy to latency, throughput, KV cache, batching, GPU utilization, routing, and cost per request — the domain of Inference Engineering.
The key distinction is between two workloads:
- Prefill: processing the prompt and building the KV cache, often compute-heavy
- Decode: generating tokens one at a time, often memory-bandwidth heavy
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 become a CUDA expert, but understanding these concepts is essential when moving from data science into AI engineering.
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