Yunmo Koo argues that identical model weights can produce different application experiences on different inference providers. His game-generation examples are reported demonstrations, not controlled evidence that one service universally wins. He identifies routing, memory, quantization, scheduling and kernels as parts of the serving system that should be evaluated with the intended workload.
Yunmo Koo describes prefix caching for long, repetitive agent inputs. Reusing earlier computation can reduce prefill work, but distributed replicas need cache-aware routing to retain that benefit. He outlines a hierarchy across GPU memory, host RAM and disk for balancing capacity and access speed.
Yunmo Koo distinguishes draft-model speculation from reuse of patterns already present in context. Proposed tokens are checked by the target model; drafting and validation both add costs, so a useful strategy depends on the workload and batch conditions. He presents dynamic selection and adaptation to serving traffic as the approach used by his team.
Yunmo Koo adds error rates, timeouts and rate limits to the latency discussion because a failed call can disrupt a multi-step agent. His central evaluation criterion is the application’s own prompt lengths, traffic patterns and operational needs. Vendor rankings, uptime snapshots and customer testimonials are not treated as independently verified performance guarantees.
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