Scaling LLM Inference

Serving LLMs at scale requires attention to latency, cost, and reliability.

Strategies

• Use model distillation and quantization to reduce compute
• Employ batching and optimized kernels for GPU throughput
• Use caching for repeated prompts and responses

Deployment Patterns

• Autoscaling inference clusters with load-based scaling
• Hybrid on-device + cloud inference for latency-sensitive apps
• Monitor latency, throughput, and cost per request