THE METER
SECTION 03
ISSUE 001
Hardware-Aware Model Evaluation
Projection: NVIDIA separates prefill and decode, transfers KV state, and routes caches; Qualcomm emphasizes near-memory bandwidth, disaggregated inference, and bandwidth per watt. Model evaluation should therefore run on real serving stacks. The hypothesis is supported when hardware differences materially reorder models on throughput, tail latency, energy, or utilization.
Why this idea is here
What the evidence establishes.
NVIDIA documents separately scalable prefill and decode pools, KV-state transfer, cache-aware routing, and distributed inference; Qualcomm announces agentic CPUs, near-memory high-bandwidth compute, disaggregated inference, liquid-cooled racks, and claimed improvements in bandwidth per watt. These are source-backed premises for this inference; they do not by themselves prove broad adoption or the eventual outcome.
Source ledger
Read the sources.
- S01NVIDIA Dynamo: Disaggregated Serving
official technical documentation / published 2026-07-09 / retrieved 2026-07-09
- S02Qualcomm Dragonfly Data Center Roadmap
official hardware announcement / published 2026-06-24 / retrieved 2026-07-09