Storage Scaling Bottlenecks in Large LLM Inference Clusters
KV cache grows without bounds, forcing engineers to choose which storage tier fails first.
KV cache grows without bounds, forcing engineers to choose which storage tier fails first.
Scaling LLM inference isn't linear: utilization and scheduling matter more than GPU count.
Careful stage-by-stage design determines whether GPUs train efficiently or starve for tokens.
Failures at massive scale demand checkpoints so frequent that I/O overhead dominates training time.
A tiered memory hierarchy solves long-context LLM serving bottlenecks.
How PagedAttention solved the memory crisis that made LLM serving uneconomical.
Compressing KV cache with FP8 halves memory while preserving accuracy.
Power and cooling infrastructure now costs as much as the GPUs themselves.
Staffing and managing AI infrastructure now costs as much as the compute itself.
KV cache transfers between GPU pools drive network costs more than computation or model size.
Optimizer state, not weights, drives checkpoint costs during LLM training.
Self-hosted LLMs cost 95% more than their free model weights suggest.