High-Performance Model Weight Storage and Distribution in Cloud Environments

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 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 simultaneously
• Update models across a fleet of servers
• Handle dynamic scaling where new nodes need rapid access to model weights

Two Architectural Approaches for Distributed Model Storage

1. NFS-Based Solutions for Model Weights

NFS 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 versions
• Simple model updates (write once, available everywhere)
• POSIX-compliant operations that work seamlessly with existing ML frameworks

2. FUSE-Based Solutions with Intelligent Caching

FUSE 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 policies
• Tiered 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

Operational Cost

Let's talk about what costs look like for NFS vs Fuse backed storage. What would it means in cost on both operation terms and flexibility

Practical Deployment on CLOUD

There are some overall pointers for considering storage,Let's talk about real world deployment of there storage solutions. What would it cost in terms for deploying this for Major Cloud providers and what can you expect in terms for speed and durability for these solutions in Real world

Let’s talk about solution available across cloud providers for NFS based storage:

Real-World Cost Impact for Model Storage (10TB)

Budget Tier: $2,320/month

• Suitable for dev/staging environments
• Handles light concurrent access
• Max Throughput: ~1,000 MB/s for 10TB

Performance Tier: $6,920/month

• Production-grade for high-concurrency serving
• 3x the cost, but 10-20x the throughput
• Max Throughput: ~10,240 MB/s for 10TB

The buys you the ability to serve hundreds of nodes simultaneously without bottlenecks—often the difference between 5-minute and 30-second model deployment times at scale.

Fuse options that are cloud provider specific

Fuse options that are Cross Cloud

Real-World Cost Impact for Model Storage (10TB)

Standard Tier: $220/month

• Production-grade for high-concurrency serving
• Max Throughput: ~500 MB/s per node ( can be tuned for more )

Which is better for ML models weights ?

Cost - Model storage quickly becomes prohibitively expensive with NFS. With modern models ranging from tens of gigabytes to over a terabyte each, and fast NFS solutions charging $500-1,500 per TB monthly. FUSE cuts storage costs by 95% compared to NFS, despite both ultimately serving the same purpose: read-only blob distribution.

Performance - NFS's central server architecture becomes a critical bottleneck during scale-out events. Ironically, FUSE-backed object storage achieves 10x better aggregate throughput than 'high-performance' NFS during critical scaling events—when 50 nodes pull models simultaneously, FUSE delivers 25 GB/s total while NFS saturates at 2.5 GB/s, often delivering worse throughput than parallel object storage requests to S3 or GCS.

Getting Start with Cloud-Native FUSE Mounters

Cloud providers offer native FUSE-based solutions that can bridge the gap between object storage economics and NFS-like performance. Here's a practical path to production:

• AWS: Use Mountpoint for S3
• GCP: Deploy GCSFuse
• Azure: Leverage BlobFuse2

Tune for ML Workload Characteristics

• Page Size: Increase from default 4KB to 1-2MB to match model file chunk
• Prefetch Depth: Configure aggressive read-ahead (256MB+) since model loading is sequential
• Concurrency: Set parallel stream counts to 8-12 threads for multi-GB models
• Cache TTL: Trigger cache population before pod scheduling to ensure models are locally cached

The Future for Fuse

We need FUSE to evolve from "making object storage barely usable" to "making object storage indistinguishable from local storage" for ML workloads. This means:

1. Speed: Matching NVMe performance (5-10 GB/s) through kernel bypass and parallelization.
2. Compliance: Supporting every POSIX operation that PyTorch / JAX / TensorFlow might call and use for loading
3. Intelligence: Understanding ML access patterns and optimizing for them automatically