Together AI introduces Aurora and claims a 1.25x speed gain over a static speculator
Together AI says Aurora learns from live inference traces and delivered a 1.25x additional speedup over a strong static speculative decoding baseline.
Together AI is pitching Aurora as a way to keep speculative decoding tuned while traffic shifts. The March 31, 2026 post describes an RL loop that learns from live inference traces without pausing serving.
The headline claim is a 1.25x extra gain over a strong static speculator. If that holds outside one benchmark, it can change serving economics for teams with heavy generation volume.
The broader pattern is clear. Inference optimization is moving from one-time offline tuning toward continuous adaptation.
For more context on model economics and deployment tradeoffs, see our report on Veo 3.1 Lite pricing signals.
Together AI's performance claim is documented in the Aurora technical post.
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