GPT-5.6 Sol Cuts Inference Costs 20% via Autonomous GPU Kernel Rewriting

OpenAI's GPT-5.6 Sol autonomously optimizes GPU kernels and request allocation, reducing end-to-end inference costs by 20% and boosting speculative decoding efficiency by over 15%.

Woofun AI reports that OpenAI has integrated GPT-5.6 Sol into its production infrastructure to autonomously optimize system performance. The model utilizes Codex to analyze real-time traffic, adjust request distribution, and rewrite GPU kernels, resulting in a 20% reduction in end-to-end running costs.

Additionally, GPT-5.6 Sol enhances its draft model by independently designing architecture experiments and monitoring training stability. This automation improves token generation efficiency through speculative decoding by more than 15%, establishing a continuous feedback loop for bottleneck identification and system validation.