Maximizing LLM Efficiency: How to Optimize vLLM for 4x Inference Speed on Shared GPU Cloud Servers

Deploying Large Language Models (LLMs) on shared GPU infrastructure often leads to resource contention and latency spikes. This comprehensive guide explores advanced vLLM optimization strategies—including PagedAttention, tensor parallelism, and dynamic batching—to achieve a 4x increase in inference throughput while minimizing operational costs in enterprise cloud environments.