Fuse is 95% cheaper and 10x faster than NFS

Summary

With the rapid scaling of AI deployments, efficiently storing and distributing model weights across distributed infrastructure has become a critical bottleneck. Here's my analysis of storage solutions optimized specifically for model serving workloads.The Challenge: Speed at ScaleModel weights need to be loaded quickly during initialization and potentially shared across multiple inference nodes. While local NVMe storage offers blazing-fast speeds of 5-7 Gbps with direct GPU attachment, this approach doesn't scale when you need to:Distribute the same model weights to multiple nodes simultaneouslyUpdate models across a fleet of serversHandle dynamic scaling where new nodes need rapid access to model weightsTwo Architectural Approaches for Distributed Model Storage1. NFS-Based Solutions for Model WeightsNFS provides a straightforward path for centralizing model storage. Multiple inference nodes can mount a shared directory containing model weights, enabling:Single source of truth for model versionsSimple model updates (write once, available everywhere)POSIX-compliant operations that work seamlessly with existing ML frameworks2. FUSE-Based Solutions with Intelligent CachingFUSE implementations can provide smarter model distribution through:Lazy loading of model layers (load only what's needed, when it's needed)Local caching with intelligent eviction policiesTiered storage strategies (hot models in SSD, warm on CDN, cold in object storage)Scalability First we will talk about the scalability what we are looking 0 to n machines. How do we increase aggregate throughput as demand grows? What happens if instead of 1 client 100 clients ask for the data How easy is it to scale for fan out workloads NFS Scaling FUSE Scaling Vertical scaling through faster hardware. Horizontal scaling requires complex clustering solutions Vertical scaling through complex caching mechanisms; virtually unlimited horizontal scale Performance can degrade with many concurrent clients Performance scale...