Traditional Horizontal Pod Autoscaling (HPA) based on CPU or memory is often ineffective for AI workloads. A GPU can be at 100% utilization while your inference queue explodes due to batching misconfiguration.
To scale correctly, you must use inference-specific metrics.
Scaling on the Right Metrics
We recommend scaling based on:
- Inference Queue Depth: The number of requests waiting for a GPU.
- Duty Cycle: The percentage of time the GPU is actively processing.
- Concurrency: The number of active KV cache sequences.
Managing Scale-Up and Scale-Down
GPU cold starts are expensive (often 60s+). Your autoscaling policy should include:
- Aggressive Scale-Up: Start new nodes early to absorb predicted burst traffic.
- Graceful Scale-Down: Use long cooldown periods to avoid "thrashing" and to maintain warm capacity during minor fluctuations.
Final Takeaway
GPU autoscaling is a balance between cost and performance. By scaling on inference-specific metrics and maintaining a warm buffer, you can ensure that your platform meets demand while minimizing wasted spend.
Need help implementing intelligent GPU autoscaling? We help teams design metric-driven scaling policies, optimize cold starts, and balance cost with production performance. Book a free infrastructure audit and we’ll review your scaling and capacity strategy.
