Load Balancing
Definition
Load balancers sit in front of model server pools and distribute requests using algorithms such as round-robin, least-connections, or weighted routing based on server capacity. For AI workloads, load balancing accounts for GPU memory constraints — routing requests to servers with available VRAM, avoiding GPU out-of-memory errors. Session-aware load balancing can pin users to specific servers for multi-turn conversations. Health checks continuously monitor server status, automatically removing unhealthy instances from the rotation.
Why It Matters
Load balancing enables horizontal scaling of AI inference workloads, which is critical for meeting variable demand. Without load balancing, a single model server becomes both a bottleneck and a single point of failure. Proper load balancing reduces tail latencies by preventing any server from accumulating a backlog. For GPU-intensive LLM inference, intelligent load balancing that routes based on available GPU memory and active request queue depth significantly outperforms simple round-robin approaches.
How It Works
A load balancer maintains a pool of healthy model server addresses, continuously updated through health check probes. For each incoming request, it selects a server based on the chosen algorithm — for LLM inference, least-connections or queue-depth-aware routing minimizes average request wait time. When a server fails health checks (missing heartbeats or returning 5xx errors), it is removed from the pool until it recovers. Traffic is smoothly redistributed to remaining healthy servers.
Load Balancing for AI APIs
Load Balancer
Round-robin / least-connections / latency-aware
Instance 1
24% load
Instance 2
31% load
Instance 3
45% load
No single instance overloaded → consistent low latency
Real-World Example
A company serving a multi-tenant LLM runs six A100 GPU servers. Their load balancer monitors each server's active request count and GPU memory utilization. During a marketing email blast that spikes traffic 8x, the load balancer routes requests to the three servers with the shortest queues, automatically brings two warm standby servers into rotation, and maintains a p99 latency under 3 seconds throughout the event.
Common Mistakes
- ✕Using round-robin load balancing for LLM inference without accounting for variable request processing times — short requests finish quickly while long requests pile up on unlucky servers
- ✕Not configuring health check timeouts appropriately — a slow model server may still pass health checks while failing real user requests
- ✕Failing to handle session affinity for stateful multi-turn conversations, sending follow-up messages to servers without the conversation context
Related Terms
API Gateway
An API gateway is a managed entry point that sits in front of AI model serving endpoints, handling authentication, rate limiting, request routing, load balancing, and monitoring for all incoming API traffic.
Kubernetes
Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized AI model serving workloads across clusters of machines.
Model Serving
Model serving is the infrastructure that hosts trained ML models and exposes them as APIs, handling prediction requests in production with the latency, throughput, and reliability requirements of real applications.
Inference Server
An inference server is specialized software that hosts ML models and handles prediction requests with optimized batching, hardware utilization, and concurrency—outperforming generic web frameworks for AI workloads.
Inference Latency
Inference latency is the time between submitting an input to a deployed AI model and receiving the complete output — typically measured in milliseconds for classification models and seconds for large language models — directly impacting user experience and system design.
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