How do I run low-latency inference on a GPU cluster?

To run low-latency inference on a GPU cluster, focus on 5 things: model serving architecture, batching strategy, GPU utilization, networking, and autoscaling.

1) Use a proper inference server

Don’t serve models with a simple Flask/FastAPI app unless traffic is very small. Use an inference server that supports:

• Dynamic batching
• TensorRT / ONNX / TorchScript
• Concurrent request handling
• Model versioning
• GPU memory management

Good options:

• NVIDIA Triton Inference Server
• Ray Serve
• KServe / Seldon
• vLLM or TensorRT-LLM for LLMs
• BentoML for simpler setups

2) Optimize the model

Lower latency usually comes from reducing compute:

• Quantize to FP16, BF16, INT8, or even 4-bit if quality allows
• Compile/trace the model with TensorRT, TorchScript, or ONNX Runtime
• Prune or distill a smaller model
• Use smaller batch sizes or token limits for LLMs
• Keep the model loaded in GPU memory continuously

For LLMs specifically:

• Use paged attention / KV-cache optimizations
• Use continuous batching (vLLM, TensorRT-LLM)
• Prefer shorter context windows if possible

3) Batch carefully

Batching improves throughput but can hurt latency if done badly.

Use:

• Dynamic micro-batching with a short timeout
• Separate latency-sensitive and throughput-oriented traffic
• Small max batch sizes for low-latency endpoints

Rule of thumb:

• For real-time requests, use a tiny batching window like 1–10 ms
• Tune max batch size so GPUs stay busy without queueing too long

4) Place requests near the model

Network overhead can dominate latency if poorly designed.

Best practices:

• Keep clients and inference pods in the same region/zone
• Use gRPC instead of REST when possible
• Avoid unnecessary hops: API gateway → service mesh → proxy → model server can add overhead
• Use persistent connections
• Co-locate preprocessing/postprocessing with the server if it’s cheap

5) Scale smartly

Low latency requires avoiding cold starts and overloaded GPUs.

• Keep a minimum replica count > 0
• Use GPU-aware autoscaling
• Scale on:
  • GPU utilization
  • queue length
  • request latency
• Warm up models on startup
• Use pod preloading so the model is already on GPU before traffic arrives

6) Reduce contention on GPU

A single GPU may serve multiple models/replicas, but contention increases latency.

• Prefer one latency-critical model per GPU if possible
• If sharing, isolate with:
  • NVIDIA MIG on supported GPUs
  • Kubernetes GPU requests/limits
• Watch for memory fragmentation and context-switch overhead

7) Use the right cluster orchestration

On Kubernetes:

• Use the NVIDIA GPU Operator
• Schedule with node affinity/taints
• Use HPA/KEDA with custom metrics
• Pin inference pods to GPU nodes
• Use topology-aware scheduling when multi-GPU communication matters

8) Measure the right metrics

Track:

• p50 / p95 / p99 latency
• time to first token and tokens/sec for LLMs
• Queue time vs compute time
• GPU memory usage
• GPU utilization
• Request rejection / timeout rate

Often the bottleneck is queueing, not raw GPU speed.

A practical architecture

For low-latency inference in a cluster:

1. Client sends request via gRPC
2. Load balancer routes to nearest healthy inference pod
3. Inference server does:
   • light preprocessing
   • dynamic micro-batching
   • GPU execution
   • postprocessing
4. Autoscaler keeps enough warm replicas
5. Monitoring captures p95 latency and queue length

Recommended stack examples

For general ML models

• Kubernetes + NVIDIA GPU Operator
• Triton Inference Server
• ONNX Runtime / TensorRT backend
• Prometheus + Grafana
• KEDA or HPA

For LLMs

• Kubernetes + GPU Operator
• vLLM or TensorRT-LLM
• gRPC or HTTP streaming
• Autoscaling based on queue depth and tokens/sec

Common mistakes

• Using large batch sizes for latency-sensitive traffic
• Not warming up models
• Putting API logic in front of the model server
• Overcommitting GPUs
• Ignoring p99 latency
• Serving from a different zone/region
• Not using half precision or quantization

If you want, I can give you:

1. a reference Kubernetes deployment,
2. a Triton/vLLM setup, or
3. a latency-tuning checklist for your specific model type.