Running AI Workloads on Azure Silk Virtual SAN

AI applications can generate a large amount of data traffic. Model files, datasets, checkpoints, logs, temporary files, and inference workloads can all put pressure on storage.

For AI workloads running in Azure, storage architecture matters almost as much as compute selection. Azure Silk Virtual SAN is designed to provide high-performance storage capabilities that can be used by demanding workloads.

The right configuration depends on the workload, access pattern, capacity requirements, and performance requirements.

What Is Azure Silk Virtual SAN?

Azure Silk Virtual SAN is a software-defined storage technology designed for Azure infrastructure.

At a high level, the architecture separates compute workloads from the underlying storage services:

AI Application
      |
      v
Azure Compute
      |
      v
Silk Virtual SAN
      |
      v
Storage resources

This separation can help applications consume storage without treating individual physical storage devices as application-level resources.

Why Storage Matters for AI

AI workloads can be storage-intensive for several reasons.

A training or inference environment may need to handle:

Large datasets
Model weights
Checkpoints
Feature data
Temporary processing files
Logs and monitoring data

For example:

Training Job
    |
    +--> Read dataset
    |
    +--> Load model
    |
    +--> Write checkpoints
    |
    +--> Write logs
    |
    +--> Repeat

If storage cannot keep up with the workload, faster GPUs may not translate into better overall application performance.

Identify the Workload First

Before selecting a storage configuration, classify the AI workload.

Workload	Typical storage pattern
Model training	Large sequential and repeated reads
Inference	Frequent model reads and application data access
Checkpointing	Large sequential writes
Data preprocessing	High read/write activity
Model development	Mixed access patterns
Logging	Frequent smaller writes

The same storage configuration is not necessarily suitable for every workload.

Separate Dataset and Model Storage

A useful architecture is to separate durable datasets from frequently accessed working data.

For example:

                    AI Workload
                         |
             +-----------+-----------+
             |                       |
        Dataset Store           Working Storage
             |                       |
       Original data        Model / temporary data

The dataset may require durability and capacity, while working storage may require low latency and high throughput.

Separating these concerns can make capacity planning easier.

Consider Data Locality

AI workloads can move large amounts of data between storage and compute.

A simplified flow is:

Storage
   |
   | Dataset
   v
Compute
   |
   | Processed data
   v
GPU / AI workload

If the workload repeatedly reads the same data, caching can reduce unnecessary storage traffic.

For example, an application might cache frequently used model files locally:

from pathlib import Path

model_path = Path("/models/model.bin")

if not model_path.exists():
    download_model()

load_model(model_path)

The exact caching strategy depends on the application and deployment environment.

Plan Capacity Separately From Performance

Storage capacity and storage performance are different requirements.

A workload may need:

Capacity:     10 TB
Throughput:   High
Latency:      Low
IOPS:         High

Increasing capacity does not automatically solve every performance problem.

Start by measuring:

Total storage capacity
Read throughput
Write throughput
IOPS
Latency
Queue depth
Data growth

Then select a configuration that meets the actual workload requirements.

Checkpointing Can Be Expensive

Training workloads often save checkpoints.

For example:

def save_checkpoint(model, path):
    # Save model state
    model.save(path)

If checkpoints are large and written frequently, storage traffic can become significant.

A better design may involve:

Saving checkpoints at an appropriate interval.
Retaining only required versions.
Separating temporary checkpoints from long-term storage.
Monitoring write throughput.

Do not assume that saving more checkpoints always provides better protection.

Monitor Storage During AI Jobs

A GPU utilization graph alone does not tell you whether storage is limiting the workload.

Monitor both compute and storage:

GPU utilization
CPU utilization
Memory usage
Storage throughput
Storage latency
IOPS
Network throughput

For example:

GPU utilization:     Low
Storage read rate:   High
Storage latency:     High

This pattern may indicate that the workload is waiting for data rather than fully using the available GPU capacity.

Storage Bottlenecks Can Look Like Compute Problems

Consider a training job:

Dataset read
     |
     v
Preprocessing
     |
     v
GPU processing
     |
     v
Checkpoint write

If dataset reads are slow, GPU utilization can fall.

Developers may initially try to increase GPU capacity, but the actual bottleneck is storage.

This is why end-to-end monitoring is important.

Use Appropriate File Organization

Large AI datasets should be organized around the application's access pattern.

Avoid unnecessarily creating millions of tiny files when the workload frequently needs to scan them together.

Depending on the application, formats that group data efficiently can reduce filesystem overhead and improve sequential access.

The correct format depends on the framework and workload.

Security Considerations

AI datasets can contain sensitive information.

Storage design should therefore include:

Access control
Encryption
Network security
Identity-based access
Audit logging
Backup and recovery planning

Applications should use managed identities or appropriate workload identities where supported instead of embedding storage credentials in source code.

Avoid:

STORAGE_KEY = "secret-value"

Use the platform's supported identity and secret-management mechanisms instead.

Common Mistakes

Choosing Storage Based Only on Capacity

A large storage allocation does not automatically provide the required throughput or latency.

Monitoring Only GPU Utilization

Low GPU usage can sometimes be caused by storage or data pipeline bottlenecks.

Treating All AI Workloads the Same

Training, inference, checkpointing, and preprocessing have different storage patterns.

Writing Checkpoints Too Frequently

Large, frequent writes can create unnecessary storage traffic.

Ignoring Security

AI datasets can contain valuable or sensitive information and should be protected accordingly.

Troubleshooting Storage Bottlenecks

When an AI workload performs below expectations, check the following:

Measure storage latency.
Check read and write throughput.
Compare storage activity with GPU utilization.
Check whether data is repeatedly reread.
Review checkpoint frequency.
Check network throughput where applicable.
Verify that the workload has sufficient storage capacity.

A useful diagnostic question is:

Is the workload waiting for storage,
or is storage waiting for the workload?

That distinction helps identify the actual bottleneck.

Best Practices

Define capacity and performance requirements separately.
Match storage architecture to the AI workload.
Monitor storage and compute together.
Cache frequently accessed data where appropriate.
Plan checkpoint storage deliberately.
Separate temporary and durable data where practical.
Use identity-based access instead of hard-coded credentials.
Test the workload with production-like data volumes.
Monitor storage growth before capacity becomes a problem.

Advantages and Considerations

Area	Advantage	Consideration
AI workloads	Can support storage-intensive applications	Configuration must match workload
Scalability	Separates storage considerations from application compute	Capacity and performance still require planning
Performance	Designed for demanding storage workloads	Measure actual application behavior
Operations	Centralized storage architecture can simplify management	Monitoring remains important
Security	Can be integrated into Azure security controls	Access policies must be configured correctly

Summary

Storage is a critical part of an AI platform. Faster GPUs cannot compensate for a storage layer that cannot deliver data quickly enough.

Azure Silk Virtual SAN can be considered as part of an Azure architecture for storage-intensive AI workloads, but the correct configuration depends on the workload's capacity, throughput, latency, and access patterns.

Start with measurements rather than assumptions. Identify how the application reads, writes, caches, and checkpoints data, then size the storage architecture around those requirements.

For production AI systems, monitor storage and compute together. This makes it much easier to determine whether the real bottleneck is the GPU, CPU, network, or storage layer.

AI applications can generate a large amount of data traffic. Model files, datasets, checkpoints, logs, temporary files, and inference workloads can all put pressure on storage.

The right configuration depends on the workload, access pattern, capacity requirements, and performance requirements.

What Is Azure Silk Virtual SAN?

Azure Silk Virtual SAN is a software-defined storage technology designed for Azure infrastructure.

At a high level, the architecture separates compute workloads from the underlying storage services:

AI Application
      |
      v
Azure Compute
      |
      v
Silk Virtual SAN
      |
      v
Storage resources

This separation can help applications consume storage without treating individual physical storage devices as application-level resources.

Why Storage Matters for AI

AI workloads can be storage-intensive for several reasons.

A training or inference environment may need to handle:

Large datasets
Model weights
Checkpoints
Feature data
Temporary processing files
Logs and monitoring data

For example:

Training Job
    |
    +--> Read dataset
    |
    +--> Load model
    |
    +--> Write checkpoints
    |
    +--> Write logs
    |
    +--> Repeat

If storage cannot keep up with the workload, faster GPUs may not translate into better overall application performance.

Identify the Workload First

Before selecting a storage configuration, classify the AI workload.

Workload	Typical storage pattern
Model training	Large sequential and repeated reads
Inference	Frequent model reads and application data access
Checkpointing	Large sequential writes
Data preprocessing	High read/write activity
Model development	Mixed access patterns
Logging	Frequent smaller writes

The same storage configuration is not necessarily suitable for every workload.

Separate Dataset and Model Storage

A useful architecture is to separate durable datasets from frequently accessed working data.

For example:

                    AI Workload
                         |
             +-----------+-----------+
             |                       |
        Dataset Store           Working Storage
             |                       |
       Original data        Model / temporary data

The dataset may require durability and capacity, while working storage may require low latency and high throughput.

Separating these concerns can make capacity planning easier.

Consider Data Locality

AI workloads can move large amounts of data between storage and compute.

A simplified flow is:

Storage
   |
   | Dataset
   v
Compute
   |
   | Processed data
   v
GPU / AI workload

If the workload repeatedly reads the same data, caching can reduce unnecessary storage traffic.

For example, an application might cache frequently used model files locally:

from pathlib import Path

model_path = Path("/models/model.bin")

if not model_path.exists():
    download_model()

load_model(model_path)

The exact caching strategy depends on the application and deployment environment.

Plan Capacity Separately From Performance

Storage capacity and storage performance are different requirements.

A workload may need:

Capacity:     10 TB
Throughput:   High
Latency:      Low
IOPS:         High

Increasing capacity does not automatically solve every performance problem.

Start by measuring:

Total storage capacity
Read throughput
Write throughput
IOPS
Latency
Queue depth
Data growth

Then select a configuration that meets the actual workload requirements.

Checkpointing Can Be Expensive

Training workloads often save checkpoints.

For example:

def save_checkpoint(model, path):
    # Save model state
    model.save(path)

If checkpoints are large and written frequently, storage traffic can become significant.

A better design may involve:

Saving checkpoints at an appropriate interval.
Retaining only required versions.
Separating temporary checkpoints from long-term storage.
Monitoring write throughput.

Do not assume that saving more checkpoints always provides better protection.

Monitor Storage During AI Jobs

A GPU utilization graph alone does not tell you whether storage is limiting the workload.

Monitor both compute and storage:

GPU utilization
CPU utilization
Memory usage
Storage throughput
Storage latency
IOPS
Network throughput

For example:

GPU utilization:     Low
Storage read rate:   High
Storage latency:     High

This pattern may indicate that the workload is waiting for data rather than fully using the available GPU capacity.

Storage Bottlenecks Can Look Like Compute Problems

Consider a training job:

Dataset read
     |
     v
Preprocessing
     |
     v
GPU processing
     |
     v
Checkpoint write

If dataset reads are slow, GPU utilization can fall.

Developers may initially try to increase GPU capacity, but the actual bottleneck is storage.

This is why end-to-end monitoring is important.

Use Appropriate File Organization

Large AI datasets should be organized around the application's access pattern.

Avoid unnecessarily creating millions of tiny files when the workload frequently needs to scan them together.

Depending on the application, formats that group data efficiently can reduce filesystem overhead and improve sequential access.

The correct format depends on the framework and workload.

Security Considerations

AI datasets can contain sensitive information.

Storage design should therefore include:

Access control
Encryption
Network security
Identity-based access
Audit logging
Backup and recovery planning

Applications should use managed identities or appropriate workload identities where supported instead of embedding storage credentials in source code.

Avoid:

STORAGE_KEY = "secret-value"

Use the platform's supported identity and secret-management mechanisms instead.

Common Mistakes

Choosing Storage Based Only on Capacity

A large storage allocation does not automatically provide the required throughput or latency.

Monitoring Only GPU Utilization

Low GPU usage can sometimes be caused by storage or data pipeline bottlenecks.

Treating All AI Workloads the Same

Training, inference, checkpointing, and preprocessing have different storage patterns.

Writing Checkpoints Too Frequently

Large, frequent writes can create unnecessary storage traffic.

Ignoring Security

AI datasets can contain valuable or sensitive information and should be protected accordingly.

Troubleshooting Storage Bottlenecks

When an AI workload performs below expectations, check the following:

Measure storage latency.
Check read and write throughput.
Compare storage activity with GPU utilization.
Check whether data is repeatedly reread.
Review checkpoint frequency.
Check network throughput where applicable.
Verify that the workload has sufficient storage capacity.

A useful diagnostic question is:

Is the workload waiting for storage,
or is storage waiting for the workload?

That distinction helps identify the actual bottleneck.

Best Practices

Define capacity and performance requirements separately.
Match storage architecture to the AI workload.
Monitor storage and compute together.
Cache frequently accessed data where appropriate.
Plan checkpoint storage deliberately.
Separate temporary and durable data where practical.
Use identity-based access instead of hard-coded credentials.
Test the workload with production-like data volumes.
Monitor storage growth before capacity becomes a problem.

Advantages and Considerations

Area	Advantage	Consideration
AI workloads	Can support storage-intensive applications	Configuration must match workload
Scalability	Separates storage considerations from application compute	Capacity and performance still require planning
Performance	Designed for demanding storage workloads	Measure actual application behavior
Operations	Centralized storage architecture can simplify management	Monitoring remains important
Security	Can be integrated into Azure security controls	Access policies must be configured correctly

Summary

Storage is a critical part of an AI platform. Faster GPUs cannot compensate for a storage layer that cannot deliver data quickly enough.

Start with measurements rather than assumptions. Identify how the application reads, writes, caches, and checkpoints data, then size the storage architecture around those requirements.

For production AI systems, monitor storage and compute together. This makes it much easier to determine whether the real bottleneck is the GPU, CPU, network, or storage layer.