Olive — Optimize Models for Faster Inference

Practical Notes

• Setup time ~30 minutes (env + install + one optimize run)
• Quantitative knob from README: --precision int4 is an explicit measurable target
• GitHub stars + forks (verified): see Source & Thanks

In agent products, optimization is often the cheapest “quality win”: you can keep the same prompts and tools while reducing latency enough to make multi-step plans feasible.

Practical workflow:

1. Define a target metric (latency, memory, cost) and hardware target.
2. Run Olive optimizations from a config or scripted CLI invocation.
3. Benchmark the optimized model in your actual agent loop (not only in an isolated benchmark).

Treat artifacts as build outputs: version them, and attach the exact command/config used so results are reproducible.

FAQ

Q: Is Olive only for ONNX? A: The README highlights ONNX-related paths, but the project is positioned as a general model optimization toolkit with configurable pipelines.

Q: How do I know optimization helped agents? A: Measure end-to-end agent latency and success rate with the optimized model in the loop.

Q: What should I version-control? A: Your Olive config/commands plus benchmark notes and artifact hashes/paths.