Optimizing LLM Inference at Scale

When deploying Large Language Models (LLMs) in production, inference optimization becomes critical for both cost and user experience. This guide explores battle-tested techniques for achieving high-throughput, low-latency inference.

Key Optimization Strategies

1. Model Quantization

Quantization reduces model precision from 32-bit to 8-bit or even 4-bit representations, dramatically reducing memory footprint and improving inference speed:

2. Batching and Dynamic Batching

Implement continuous batching to maximize GPU utilization:

• Static batching: Process fixed-size batches
• Dynamic batching: Continuously add requests to batches
• Iteration-level scheduling: Remove completed sequences immediately

3. Key-Value Cache Optimization

The KV cache stores attention states to avoid recomputation. Optimize it through:

• PagedAttention: Manage KV cache like OS virtual memory
• Multi-Query Attention (MQA): Share keys and values across attention heads
• Grouped-Query Attention (GQA): Balance between MQA and full attention

Deployment Architecture

For production deployments, consider this architecture:

Each GPU instance runs optimized inference with:

• vLLM or TensorRT-LLM for serving
• Ray Serve for distributed coordination
• Prometheus for metrics collection

Benchmarking Results

Our tests on Llama 2 70B showed:

Conclusion

Optimizing LLM inference requires a holistic approach combining model-level optimizations, efficient serving infrastructure, and careful monitoring. The techniques outlined here have been proven in production at scale.