Inference Infrastructure
The total ecosystem of computing hardware and software optimization engines that enables trained AI models to respond to user requests in real time within production services.
Detailed explanation
Why It Matters for Tool and Infrastructure Selection
Between 80% and 90% of AI service costs occur during production inference, not model development. Choosing the wrong infrastructure leads to cost surges as the service grows, and slow response times directly cause user churn. Finding the optimal 'cost-performance' sweet spot aligned with the model size and expected traffic is therefore critical to business sustainability.
Key Considerations
- Monthly Token Usage: Managed APIs are advantageous for under 50 to 100 million tokens per month; dedicated GPU servers (self-hosting) are preferable above that threshold.
- Time to First Token (TTFT): Whether under 200 ms can be guaranteed for real-time chatbots or agent services.
- Hardware Generation: Whether modern architectures supporting FP8/FP4 quantized inference (such as NVIDIA Blackwell) are utilized.
- Scalability: Whether serverless GPUs or autoscaling are supported to automatically scale resources during traffic spikes.
Selection Example
For a customer service agent where real-time response is critical, choosing a high-performance dedicated infrastructure based on TensorRT-LLM is appropriate; for an internal document summarization service where cost reduction takes priority, opting for low-cost serverless GPUs or dedicated inference chip (LPU) infrastructure is suitable, even if latency occurs.