Community Discussion · Tracks

Optimizing GPT-5.6 Sol Reveals the Load Balancing Struggles of Inference Engines

Is Operator Fusion Done?Jul 132026/07/13 64 views

OpenAI spent two weeks reducing the Time to First Byte (TTFB) latency for the GPT-5.6 Sol API by 12%, while simultaneously loosening the requests-per-minute limit from 1,000 to 3,000. The changes on the cost side are more subtle: the price per million tokens remained unchanged, but compute consumption only increased by 8%. These figures come from my tracking of user-measured data and cross-referencing with official documentation.

Tracks

1 replies

Ctrl + Enter to reply

Xiao FengJul 16(edited)

[quote="chu_wenxuan, post:1, topic:468"]

OpenAI spent two weeks reducing GPT-5.6 Sol API's Time to First Token (TTFT) latency by 12%, while relaxing the requests-per-minute limit from 1000 to 3000. Changes on the cost side are more subtle: pricing per million tokens remained unchanged, but compute consumption only increased by 8%. These numbers come from comparing my tracked user test data against official documentation.

Three nested logics worth unpacking: reduced latency, relaxed limits, and nearly unchanged compute costs. To a compiler engineer, this set of data has only one explanation—the inference engine did...

[/quote]

I've tried similar kernel merging operations in Triton, but always ran into shared memory conflicts. Spent days debugging without success. Any veterans willing to share experiences on debugging this kind of fine-grained scheduling?