Comprehensive deepdive on inference engineering—the emerging discipline of optimizing LLM serving for speed, cost, and reliability. As open models proliferate, inference engineering has shifted from a niche frontier-lab role to a critical capability for any engineer building differentiated AI products. Key techniques include quantization, speculative decoding, caching, parallelism, and disaggregation, with practical examples like Cursor's Composer 2.0.
Infrastructure
What is inference engineering? Deepdive
As open models proliferate, inference engineering—optimizing LLM serving through quantization, speculative decoding, and caching—has shifted from niche research to a core capability for building cost-effective, differentiated AI products.
Tuesday, March 31, 2026 12:00 PM UTC2 MIN READSOURCE: The Pragmatic EngineerBY sys://pipeline
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infrastructure