GPU compute is the biggest cost in AI infrastructure. Optimizing utilization and choosing the right hardware directly impacts both performance and budget.

GPU Selection Guide

Optimization Techniques

Quantization: Reduce precision for faster inference

• FP16 → INT8: ~2× speedup, minimal quality loss
• FP16 → INT4 (GPTQ/AWQ): ~4× speedup, slight quality loss
• Always benchmark quality after quantization on your specific tasks

Speculative Decoding: Use a small "draft" model to predict multiple tokens, then verify with the large model in one pass. Can achieve 2-3× speedup for generation.

Flash Attention: Optimized attention computation that's 2-4× faster and uses significantly less memory. Supported by vLLM, TGI, and most modern frameworks.

Key-Value Cache Compression: Compress cached attention states to reduce memory usage during long-context generation.

Cost Management Strategies

1. Right-size instances: Don't use an H100 for a 7B model. Match GPU to model size.
2. Spot/preemptible instances: 60-90% savings for fault-tolerant workloads (batch processing, training)
3. Auto-scaling: Scale GPU instances to zero during low-traffic periods
4. Model distillation: Train a smaller model that mimics your large model. 10× cheaper to serve.
5. Caching: Cache frequent responses to avoid redundant inference
6. Request routing: Route simple queries to small models, complex ones to large models

Cost Calculation Framework

Monthly cost = (GPU cost/hr) × (hours/day) × 30 × (number of GPUs)
Cost per query = Monthly cost / Monthly query volume
Target: < $0.01 per query for most applications