A language model server is not only a model behind an HTTP endpoint. Its behavior depends on memory layout, token scheduling, kernels, routing, hardware, and the economics of each request.
The 12-week Inference and Serving roadmap begins with the model mechanics required to reason about that system. Tokenization, attention, transformer blocks, sampling, checkpoints, LoRA, quantization, and structured output establish what the engine must execute and preserve.
The path then turns those operations into serving mechanisms. Inference engines organize execution. KV caches retain state between generated tokens. Paged attention changes how that state is managed. Continuous batching mixes work from different requests. Model routing chooses a path based on cost or capability rather than sending every query to the same model.
Weeks five through eight move closer to the hardware. Flash Attention, speculative decoding, GPU utilization, and inference hardware are treated as tradeoffs, not isolated tricks. A faster kernel can still lose at the system level if it increases memory pressure, complicates batching, or only helps a shape the service rarely sees.
The final phase connects cost and latency with local inference, browser runtimes, and WebGPU. The synthesis requires measurement across the path. Useful evidence includes the workload, model and precision, batch behavior, memory use, latency distribution, throughput, and the quality boundary that the optimization must keep.
This perspective changes the learning question. Instead of asking which serving framework is fastest, ask which resource is limiting the workload and which scheduling or representation change addresses that limit.
The complete roadmap is at https://learn.significanthobbies.com/curriculum/roadmaps/inference-serving-12w.
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