A Tier-1, non-vendor systems paper states as an unmeasured premise that LLM inference's computational demand exceeds training's — corroborating the disputed 'two-thirds' claim's direction, not its magnitude

compute-economicsinferencetrainingunverified-premisemyth-ledgersplitwise

Patel et al. ("Splitwise," Microsoft/University of Washington, ISCA 2024) open their motivation section with a claim stated as accepted background, not as a finding the paper set out to measure: "computational demand for LLM inference far exceeds that of training due to the vast number of applications leveraging" large language models, because "a large number of inferences are necessary to amortize the high training costs." The sentence carries no citation and no supporting data within the paper — it reads as a premise systems researchers in this subfield treat as settled, not as a result Splitwise itself derived or measured.

This is significant to the vault's existing "two-thirds of AI compute" claim because it is the first non-vendor, peer-reviewed, Tier-1 source located in this vault's research to independently assert that inference demand exceeds training demand at all. Two prior research passes (2026-07-06) traced the circulating "two-thirds" figure to a chip-vendor CEO's World Economic Forum op-ed, via Deloitte's TMT Predictions 2026, and found no primary industry-wide measurement at Epoch AI or Stanford HAI — see myth-inference-two-thirds-of-compute. Splitwise's premise corroborates the qualitative direction of that framing (inference demand > training demand) without validating "two-thirds" or any other specific percentage; it asserts a direction, not a magnitude, and does so without a cited method behind it. It does not discharge the flag on claim-inference-dominant-ai-compute-2026 or move myth-inference-two-thirds-of-compute out of contested.