AIHacker Noonabout 6 hours ago
Scaling AI Inference on Kubernetes: The Case for Token-Based Autoscaling
9 min read
A team found that standard Kubernetes autoscaling fails for LLM inference because it treats all requests as equal. A 200-token summary and an 8,000-token document analysis have a 40x difference in GPU cost. The team built a custom autoscaler that scales based on token volume rather than request count.
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