DEVOURED
Building an Inference Stack That Improves Itself
Decagon's AutoResearch system automates inference optimization by diagnosing bottlenecks and testing changes against production traffic replays in a self-improving loop.
What: Engineers Pavi Dhiman and Cyrus Asgari describe how AutoResearch agents use GPU profiling and controlled experiments to achieve up to 2.5x throughput gains for model endpoints.
Why it matters: Automating the inference stack optimization process reflects a maturing MLOps landscape where manual tuning is increasingly infeasible for production-grade, multi-endpoint AI deployments.
Takeaway: If you are running multi-endpoint inference, consider implementing a replay-based testing harness that isolates prefill and decode latency to identify specific resource bottlenecks.
Deep dive
- Uses agents to coordinate experiments across serving parameters, GPU layouts, and engine patches.
- Relies on high-fidelity traffic replays derived from production logs to validate optimizations.
- Employs subagents to investigate competing hypotheses in parallel, such as speculative decoding vs. scheduling changes.
- Maintains a shared memory system to record successful and failed experiments across different model endpoints.
- Requires human review for final production deployment of automated findings.
Decoder
- Speculative decoding: An optimization technique where a smaller 'draft' model predicts tokens that a larger 'target' model verifies in parallel to increase throughput.
- Quantization: Reducing the precision of model weights (e.g., from FP16 to FP8) to save memory and increase speed.
- Prefill vs. Decode: Prefill is the initial phase of processing the prompt, while decode is the autoregressive generation of tokens; these phases have different computational bottlenecks.
Original article
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