Load Balancing
Load balancing distributes inference requests across multiple model replicas or GPUs to maximise throughput, minimise latency, and provide fault tolerance under varying traffic.
What is Load Balancing?
Load balancing distributes inference requests across multiple model replicas or GPUs to maximise throughput, minimise latency, and provide fault tolerance under varying traffic.
Load balancing distributes inference requests across multiple model replicas or GPUs to maximise throughput, minimise latency, and provide fault tolerance under varying traffic.
Where is it used?
Kubernetes `Service` with multiple vLLM pods, NVIDIA Triton with multi-instance routing, and cloud LBs (ALB, CloudLB) balance LLM traffic; KServe autoscales replicas on GPU metrics.
How to build it
Deploy 4 vLLM pods behind a Kubernetes `ClusterIP` Service with `kubectl expose deployment vllm --port=8000`, and use `requests` to confirm round-robin routing via pod logs.