Enterprise vLLM scaling strategies debated

Scaling open-source inference is the real bottleneck for enterprise AI adoption, and vLLM is at the center of the solution space.

• –Load balancing across multiple vLLM instances requires custom proxy routing to manage KV cache state effectively
• –Continuous batching and PagedAttention are critical for maximizing GPU utilization in high-concurrency environments
• –Enterprise deployments increasingly rely on Kubernetes operators and Triton inference server integrations to manage vLLM at scale