I need inference infrastructure that can burst during peak traffic

To support bursting inference during peak traffic, you want an architecture that can scale quickly, protect latency, and control cost. A good pattern is:

Recommended approach

Use a baseline + burst design:

• Baseline capacity: keep a small, always-on pool for steady traffic
• Burst capacity: add ephemeral workers/GPUs/replicas when demand spikes
• Autoscaling trigger: scale on request rate, queue depth, GPU utilization, or latency SLOs
• Traffic routing: send requests through a load balancer / inference gateway
• Queueing for smoothing: optionally buffer non-interactive jobs to absorb spikes

Common infrastructure options

1) Kubernetes + autoscaling

Best if you want flexibility and already run on K8s.

• HPA/KEDA for pod autoscaling
• Cluster autoscaler or Karpenter to add nodes
• GPU node pools for model serving
• Inference servers like:
  • Triton Inference Server
  • vLLM
  • TGI
  • Ray Serve
  • BentoML

Good for:

• mixed workloads
• multi-model serving
• gradual scaling
• cost control with spot instances

2) Managed model serving

Best if you want faster setup and less ops.

Examples:

• AWS SageMaker endpoints
• GCP Vertex AI endpoints
• Azure ML online endpoints
• Replicate / Modal / Fireworks / Together depending on use case

Good for:

• rapid deployment
• built-in autoscaling
• managed infra

Tradeoff:

• less control over burst behavior and cost tuning

3) Serverless GPU / on-demand inference

Best for spiky or unpredictable traffic.

• Spin up inference workers only when requests arrive
• Use scale-to-zero or near-zero
• Good for lower-QPS bursty workloads

Tradeoff:

• cold starts
• less suitable for strict low-latency interactive traffic unless warmed

Key design elements for bursting

Autoscaling signals

Use more than CPU:

• request queue length
• p95 latency
• in-flight requests
• GPU memory / utilization
• token throughput for LLMs

Warm pool

Keep a few pre-warmed replicas to avoid cold starts during surges.

Batch and micro-batch

For LLMs and embedding workloads, micro-batching can improve throughput significantly.

Queue-based admission control

If traffic can tolerate it:

• place requests into a queue
• prioritize interactive requests
• shed load gracefully when overloaded

Multi-region or multi-zone

For large spikes or regional traffic:

• replicate across zones
• use global load balancing
• fail over automatically

Model optimization

To burst efficiently, reduce per-request cost:

• quantization
• TensorRT / ONNX / compilation
• KV cache optimization
• smaller distilled models
• caching frequent results

Example burst architecture

1. Requests hit API Gateway / Load Balancer
2. Inference gateway routes to healthy warm pods
3. KEDA/HPA observes queue depth and scales pods
4. Cluster autoscaler/Karpenter adds GPU nodes
5. New pods spin up from prebuilt images
6. Excess traffic optionally goes to queue or fallback model

Practical recommendation

If you want a strong default setup:

• Kubernetes
• vLLM or Triton
• KEDA + HPA
• Karpenter or Cluster Autoscaler
• Warm pool of GPU pods
• Spot instances for burst nodes
• Queue + fallback policy

This gives you:

• fast burst handling
• cost efficiency
• good operational control

Questions to choose the right design

If you answer these, I can recommend a concrete stack:

1. What model type are you serving: LLM, vision, embedding, or classic ML?
2. What latency target do you need?
3. What is your traffic pattern: predictable peaks or sudden spikes?
4. Do you need GPUs, and if so which type?
5. Are you already on Kubernetes or a cloud platform?

If you want, I can propose a reference architecture for AWS, GCP, Azure, or bare Kubernetes.