ZNS SSDs & AI: Rewriting Enterprise Storage TCO

Introduction: Why ZNS Technology Matters Now

AI is reshaping enterprise storage. As AI training and inference generate new data patterns, traditional architectures struggle. Zoned Namespace (ZNS) SSDs address write amplification and optimize TCO for AI data centers.

This explores how ZNS intersects with QLC NAND, NVMe 2.x, and enterprise SSD design for modern AI infrastructure.

Understanding Write Amplification

Write amplification (WA) is a critical bottleneck in enterprise SSDs. Traditional SSDs use Flash Translation Layers (FTL) for garbage collection and wear leveling, causing data to be written multiple times internally.

AI workloads intensify this. Training LLMs or processing inference creates mixed sequential and random writes. Traditional SSDs face 3x-10x write amplification, impacting performance and endurance.

What is ZNS?

ZNS technology, standardized in NVMe 2.0, reimagines host-storage interfaces. Instead of flat address spaces, ZNS divides namespaces into zones—contiguous ranges requiring sequential writes.

This shifts data placement responsibility from controller to host. Eliminating complex FTL logic and requiring sequential zone writes significantly reduces write amplification.

Key ZNS Concepts:

• Zone: Contiguous logical blocks written sequentially
• Zone Capacity: Usable capacity for host data
• Write Pointer: Next writable address
• Zone States: Empty, Open, Closed, Full, Read-only, or Offline

NVMe 2.x and ZNS Evolution

NVMe 2.0 (2021) introduced ZNS through the Zoned Namespace Command Set. NVMe 2.x versions enhanced capabilities with improved management, error handling, and NAND compatibility.

Key improvements:

• Enhanced zone operation interfaces
• Improved telemetry and monitoring
• Better power loss protection
• Refined namespace sharing

These ensure seamless enterprise integration while maintaining AI framework compatibility.

QLC NAND Economics

QLC NAND stores four bits per cell, delivering higher density and lower cost than TLC or MLC. Challenges include lower endurance, slower writes, and variable read latency.

ZNS with QLC creates synergies:

Endurance Enhancement: Sequential zone writes mitigate QLC limitations. ZNS-enabled QLC drives achieve 3-5 DWPD versus 1 DWPD for conventional QLC.

Cost Optimization: QLC offers 33% higher density at lower cost. With ZNS efficiency, this substantially improves TCO.

Performance Predictability: Sequential zone writes align with QLC characteristics, avoiding random-write performance cliffs.

AI Workload Patterns

AI training and inference differ from traditional workloads:

Training:

• Large sequential dataset reads
• Checkpoint writes at epochs
• Burst write for gradient accumulation
• Predictable parameter updates

Inference:

• High-bandwidth model loading
• Mixed random cache/KV-store access
• Append-only logging

These align well with ZNS. AI frameworks can organize data into zones, maximizing sequential access and minimizing write amplification.

TCO Analysis

TCO encompasses acquisition, power, capacity efficiency, replacement cycles, and operations. For AI at scale, these factors compound.

Capacity Efficiency: Traditional SSDs maintain 20-28% over-provisioning. ZNS reduces this to 7-10%, delivering 15-20% more usable capacity.

Power: Simplified logic and fewer background operations yield 15-25% lower power consumption—meaningful savings for thousands of drives.

Endurance: Reduced write amplification extends lifespan. ZNS QLC can match TLC endurance at lower cost per TB.

Operations: While requiring application awareness, modern AI frameworks increasingly support zone-aware storage. Long-term benefits offset integration effort.

Implementation Considerations

Deploying ZNS requires planning:

Software Integration: Applications need zone awareness or middleware. Open-source projects like RocksDB, F2FS, and ZNS libraries provide this.

Zone Management: Optimal zone size balances flexibility and efficiency. Typical deployments use 256MB-2GB zones.

Garbage Collection: Device-level GC reduces, but application-level GC remains necessary. AI checkpoints simplify zone recycling.

Failure Handling: Zone error handling differs from block storage. Robust monitoring and state management are essential.

Industry Adoption and Future Outlook

Major cloud providers and AI companies are deploying ZNS. NVMe 2.x standardization, QLC maturity, and AI workload growth favor adoption.

Future developments:

• Enhanced zone management in NVMe 2.1+
• Native ZNS in AI frameworks and MLOps
• Hybrid architectures with computational storage
• AI-driven storage optimization telemetry

Performance Benchmarks: ZNS vs Conventional SSDs

Testing shows ZNS advantages for AI workloads:

Sequential Write: ZNS maintains 3GB/s+ throughput under sustained writes, while QLC SSDs degrade as buffers saturate.

Write Amplification: AI training on ZNS achieves <1.5x amplification vs 3-5x on traditional SSDs.

Tail Latency: Sequential zone writes reduce latency variability, critical for training consistency.

Challenges and Limitations

ZNS adoption faces hurdles:

Application Complexity: Zone-aware programming requires expertise. Legacy infrastructure faces migration challenges.

Ecosystem Maturity: ZNS tooling and best practices lag conventional storage.

Random Write Performance: True random-write workloads may not benefit and could see penalties.

Strategic Recommendations

Organizations should:

• Workload Characterization: Profile I/O to identify sequential write opportunities
• Pilot Deployments: Start with non-critical training workloads
• Hybrid Strategies: Combine ZNS for checkpoints with conventional SSDs for metadata
• Vendor Collaboration: Leverage vendor expertise and reference architectures

Conclusion: ZNS as Strategic Enabler

ZNS, QLC economics, and AI workloads converge to transform enterprise storage. By addressing write amplification and optimizing TCO, ZNS enables efficient AI infrastructure.

As NVMe 2.x matures and AI frameworks adopt zone-awareness, adoption will accelerate. Organizations embracing ZNS today gain competitive advantage.

The question is not whether ZNS will transform storage, but how quickly organizations adapt to leverage it.