Inference engineering is the discipline of optimizing how AI models serve predictions in production, covering the full stack from CUDA kernels to multi-cloud infrastructure. As open models like DeepSeek and Llama reach parity with closed models, more engineering teams can now apply inference engineering to reduce latency, cost, and improve availability. The key layers are runtime (single-GPU optimization), infrastructure (autoscaling, multi-cloud), and tooling (developer abstractions). Five core techniques for accelerating inference are explained in depth: quantization (reducing weight precision for 30-50% performance gains), speculative decoding (generating draft tokens to produce multiple tokens per forward pass), prefix caching (reusing KV cache across requests with shared prefixes), parallelism (tensor and expert parallelism across GPUs), and disaggregation (separating prefill and decode phases onto independent workers). The field is still early, with relatively few specialists, making it a high-opportunity area for engineers willing to invest in learning it.
Table of contents
1. Setting the stage: why is inference so important?2. What is inference?3. When is inference engineering needed?4. What hardware does inference use?5. What software does inference use?6. What infrastructure does inference need?7. Five approaches to make inference fasterTakeawaysSort: