Go big or go OOM: the art of scaling vLLM

Scaling vLLM deployments for high, variable loads is addressed through a two-pronged approach to avoid out-of-memory errors. The first method focuses on vertical, single-node optimization by carefully tuning vLLM configuration parameters based on specific workload characteristics like sequence length and traffic patterns. This process involves using tools like Auto-Tune vLLM and Optuna to run benchmark trials and find a Pareto-optimal configuration that maximizes throughput. The second angle, horizontal scaling, addresses multi-node deployment to further enhance robustness and performance under load.