Analyst Report | Storage Impact of AI Lifecycle
As AI transitions from experimentation to enterprise-scale deployment, storage infrastructure has become a critical success factor. Traditional storage systems are no longer sufficient to support the demands of modern AI workloads—especially inference, which now dominates the enterprise AI agenda.
This landing page explores how WEKA’s NeuralMesh™ and Augmented Memory Grid™ are revolutionizing AI infrastructure by addressing the unique challenges of the AI lifecycle:
Key Challenges in AI Storage
- Legacy systems cause bottlenecks in training and inference
- GPU memory limitations create the “memory wall”
- Poor metadata handling and I/O throughput stall pipelines
- Tokenomics—cost per token—now drives business viability
NeuralMesh™: Purpose-Built for AI
- Microsecond latency and high IOPS for real-time inference
- Distributed architecture with single namespace for scalability and fault tolerance
- Service-oriented design for modularity, elasticity, and multi-tenancy
- Storage-as-code for seamless DevOps and MLOps integration
Augmented Memory Grid™: Breaking the Memory Wall
- Extends GPU memory to petabyte scale
- Enables persistent KV cache reuse
- Achieves 41x faster time to first token (TTFT) and 24% lower token throughput costs
Real-World Impact
Stability AI reduced storage costs by 95% per terabyte and achieved 93% GPU utilization using WEKA’s solutions—accelerating training cycles and improving sustainability.
White Paper from 