Background to the issue
Parallel calls to multiple APIs can lead to response latency and expense spikes, requiring precise control of resource allocation.
optimization strategy
- Smart throttling:configure
task_timeout: 30Automatically terminate inefficient queries in seconds - Layered calls:Set in fast_config.yaml.
model_tiers:
- 首选项: [gpt-4o]
- 备选项: [gemini-flash] - Cache reuse:start using
--cache-dir ./cacheStoring Historical Responses
Direct reuse of results for similar queries - Cost monitoring:integrated (as in integrated circuit)
usage_tracker.pyScripts are displayed in real time:
- Token consumption
- Number of API calls
- Estimated costs
best practice
For tasks that are not time-sensitive:
1. Utilization--offline-modeRun the local model first
2. Submission of dispute outcomes to cloud-based model arbitration only
Reduces API overhead above 60%
This answer comes from the articleMassGen: A Multi-Intelligence Collaborative Task Processing SystemThe
May not be reproduced without permission:AI productivity tools " How to avoid MassGen's waste of resources in multi-model collaboration?
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