Why it matters
Sujee Maniyam and Dylan Bristot explain why identical open-model weights can behave differently across serving stacks. Their overview covers routing requests toward reusable KV state, offloading that state from GPU memory, separating prefill from decode, and using a small draft model whose tokens a larger model verifies. Quantization and application-specific draft training introduce additional quality and workload dependencies. The presentation supplies mechanisms but not sufficient benchmark conditions to generalize its reported speedups.
My takeaway: Benchmark your model and traffic with a fixed quality gate. Measure latency, throughput, cache hit rate and cost per successful task while changing one serving setting at a time; include cache-transfer and rejected-draft overhead.