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Fuse is 95% cheaper and 10x faster than NFS
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 Scale Model weights need to be loaded quickly during initialization and potentially shared
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: Cloud providers offer native FUSE-based solutions that can bridge the gap between object storage economics and NFS-like performance. Compliance: Supporting every POSIX operation that PyTorch / JAX / TensorFlow might call and use for loading Intelligence: Understanding ML access patterns and optimizing for them automatically
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