Key Takeaways
- Inference is where AI costs accumulate: a single training run is a one-time expense, but inference runs continuously in production.
- Quantization (reducing precision from 32-bit to 4-bit) can make models 4-8x faster with minimal quality loss.
- API-based inference (OpenAI, Anthropic) is simplest; self-hosted inference is cheaper at scale but requires GPU infrastructure.
Optimizing Inference Cost and Speed
For production AI, inference optimization directly impacts user experience and profitability:
- Model selection: Use the smallest model that meets quality requirements. GPT-3.5 is 10x cheaper than GPT-4 and sufficient for many tasks.
- Caching: Store responses for identical or similar queries. Semantic caching can reuse results for paraphrased questions.
- Streaming: Return tokens as they are generated rather than waiting for the complete response. Improves perceived latency.
- Batching: Process multiple requests simultaneously on the same GPU. Increases throughput by 2-4x.
Cost Optimization
Route simple queries to smaller, cheaper models (GPT-3.5, Haiku) and complex queries to powerful models (GPT-4, Opus). This hybrid approach can reduce costs by 60-80% while maintaining quality where it matters.
Frequently Asked Questions
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