4.4X More Bandwidth, 56% Lower Latency and Linear Performance Scalability
As organizations scale their GPU computing environments for artificial intelligence and machine learning(AI/ML) and high-performance computing (HPC), the biggest bottleneck isn’t compute—it’s I/O. Modern GPUs are astonishingly fast. But when storage can’t keep up, you pay for idle GPUs, longer training cycles, and slower time-to-first-token.
A new performance analysis conducted by a leading quantitative research firm using their own production-grade GPU servers shows how the Hammerspace Tier 0 architecture eliminates these bottlenecks by turning GPU server–local NVMe drives into a tier of high-performance, shared storage.
Testing was conducted using both the Flexible I/O (FIO) benchmark to compare bandwidth, IOPs, and latency across different block sizes, and the el bencho benchmark suite to evaluate performance across real-world AI/ML and HPC workloads
The results speak for themselves:
Read the full analysis here.
The Hammerspace Tier 0 solution also demonstrated good scalability characteristics, maintaining linear performance scalability as additional clients were added to the test environment.
Why This Matters for Enterprise AI
A Hammerspace Tier 0 architecture delivers major performance gains across the entire AI/ML pipeline, accelerating everything from data loading to distributed training. Random sampling throughput increased, sharded training access was faster, and checkpointing time was reduced by 66%.
And the performance benefits extend beyond training into inference, RAG, and agentic AI.
Faster random access and significantly lower latency, reduce time-to-first-token (TTFT) and improve the responsiveness of real-time and user-facing AI applications. Even in scenarios where latency is slightly higher for certain operations, the substantial throughput advantages deliver meaningful end-to-end improvements. Overall, a Tier 0 architecture provides faster training cycles, higher GPU efficiency, more resilient model management, and lower-latency inference – capabilities essential for an AI Data Platform and for scaling enterprise AI across hybrid cloud environments.
Learn More
Download the full whitepaper, “Accelerating AI/ML Workloads with a Hammerspace Tier 0 Architecture” to see the data and learn how to design a storage architecture that can keep pace with your GPUs.
Visit the Tier 0 webpage
Ready to get started? Take a self-guided tour, set up a 1:1 demo or book a meeting with Hammerspace experts today
