Part 3 — Implementation/Engine-Level: Choosing the Runtime That Gives You These for Free

You now know how to make the model fast (Part 1) and how to build a stable serving layer around it (Part 2). The final question is: which engine actually implements all of this without forcing you to write a custom scheduler from scratch? The theme of this part: inference engines are not neutral wrappers. They bake in specific opinions about batching, KV cache memory layout, prefix caching, and kernel selection. Pick the engine that aligns with your pain points, and you get chunked prefill, continuous batching, and paged KV cache for free.