Twice the Performance. One-Eighth the Infrastructure. Built for AI and HPC Without the Complexity.
“Enterprise AI needs simplicity—but not at the cost of infrastructure sprawl. Hammerspace proves there’s a better way. 2-3x more performance with 93% less hardware.”
Summary
- Hammerspace delivered 2X the IO500 10-node challenge score and 3X the bandwidth of VAST—using just nine nodes compared to VAST’s 128. This results in higher performance with a fraction of the hardware, power, and infrastructure complexity to deploy and manage.
- Hammerspace ranked in the Top 10 highest IOEasy Write and IOEasy Read score in the 10-node challenge.
Hammerspace recently submitted our inaugural entry to the IO500 benchmark for ISC 2025—and the results speak volumes. We delivered HPC-class parallel file system performance using a simple, standards-based data platform architecture. Client access was through standard pNFS—no proprietary software, just built-in Linux.
Enterprise AI teams are prioritizing simplicity as they build out their data infrastructure. But in many cases, simplicity comes at a hidden cost: disaggregated and scale-out NAS systems require significantly more infrastructure to reach performance goals—and often hit scalability limits as storage node counts grow. These solutions may appear easy to use, but they trade efficiency and scalability for that ease.
Hammerspace proves that you don’t have to choose between simplicity and efficiency. Our architecture delivers performance that rivals HPC parallel file systems—without the need for exotic storage, custom networking, or large-scale server sprawl. The result: high performance, low friction, and a clear path to AI readiness—on the infrastructure you already have.
The IO500 Benchmark
The IO500 is an open benchmark suite designed to evaluate how storage systems handle common scientific and HPC workloads. It is now being looked at closely by hyperscalers, neoclouds, and enterprises as they build out their AI infrastructure.
The tests measure systems based on both bandwidth and metadata performance to provide a holistic view. The detailed test results are boiled down to three numbers; a composite bandwidth figure, a composite IOPS figure, and a calculated overall numeric score.
Benchmark Results and Discussion
| Hammerspace Results, 10-Node Research Cluster ISC25 | ||
|---|---|---|
| Overall Score | Bandwidth GiB/s | IOPS kIOPS |
| 63.77 | 37.25 | 109.16 |
Looking at the “10-Node Research” list, Hammerspace ranks alongside traditional HPC file systems like Lustre, Spectrum Scale, WekaFS, and BeeGFS—systems often tuned for peak “hero” numbers in highly customized environments. But our results tell a different story.
Hammerspace delivered leading performance using standard Linux, standard Ethernet networking, standard NVMe storage servers, and a single metadata controller. No custom hardware. No exotic fabrics. No proprietary clients. Even our metadata search used the default Linux “pfind” command as compiled with the IO500 source—not a benchmark-optimized variant.
This benchmark validates a critical shift: Enterprise AI doesn’t need complexity to get performance. The future of enterprise AI infrastructure is standards-based and enterprise-ready—built on the hardware and networks organizations already have, not on proprietary silos.
While vendors like VAST and Pure Storage are aggressively pursuing this market, both require disruptive infrastructure overhauls: forklift upgrades, massive hardware footprints, and migrating applications to their new silos. Hammerspace takes a fundamentally different approach. We stand alone in delivering superior performance—2X the IO500 score, 3X the bandwidth, and 31% higher metadata path IOPS than VAST, for example —while using just a fraction of the infrastructure. No new silos. No data copying. No re-platforming. Just performance and efficiency, on your terms.
| Overall Score | Bandwidth GiB/s | IOPS kIO/s | Server Nodes | Capacity PiB | |
|---|---|---|---|---|---|
| VAST Data | 31.08 | 11.61 | 83.22 | 128 | 3.8 |
| Hammerspace | 63.77 (2X Higher) | 37.25 (3X Higher) | 109.16 (31% Higher) | 9 (119 Fewer) | 0.492 (~1/8th) |
Hammerspace turned in solid results overall, and our data platform did particularly well on the test known as “IOEasy.” This test focuses on streaming, large-block IO, as used when loading training data, model saving, and similar operations. Hammerspace placed in the top ten results for IOEasy Write and Read among entrants on the Ten-Node Research list.
| Ten-Node Research List IOEasy Write | ||||
|---|---|---|---|---|
| IOEasy Write Rank | IO500 Score | System Name | Filesystem Type | IOEasy Write GiB/s |
| 1 | 1 | Cheeloo-1 | OceanFS2 | 2480.69 |
| 2 | 6 | Aurora | DAOS | 1473.27 |
| 3 | 3 | ParaStor | ParaStor | 905.49 |
| 4 | 10 | Endeavour | DAOS | 538.83 |
| 5 | 4 | StarStor | StarStor | 502.88 |
| 6 | 14 | SuperMUC-NG Phase2 | DAOS | 455.22 |
| 7 | 11 | SuperMUC-NG-Phase2-10 | DAOS | 434.74 |
| 8 | 33 | Cumulus | Lustre | 429.28 |
| 9 | 70 | Hammerspace | pNFSv4.2 | 420.35 |
| 10 | 17 | Cumulus | DAOS | 360.44 |
| Ten-Node Research List IOEasy Read | ||||
|---|---|---|---|---|
| IOEasy Read Rank | IO500 Score | System Name | Filesystem Type | IOEasy Read GiB/s |
| 1 | 1 | Cheeloo-1 | OceanFS2 | 2438.99 |
| 2 | 6 | Aurora | DAOS | 1301.33 |
| 3 | 3 | ParaStor | ParaStor | 776.42 |
| 4 | 4 | StarStor | StarStor | 770.07 |
| 5 | 10 | Endeavour | DAOS | 586.05 |
| 6 | 35 | MEGWARE XiRAID Classic | Lustre | 580.14 |
| 7 | 70 | Hammerspace | pNFSv4.2 | 446.79 |
| 8 | 15 | Meadowgate | DAOS | 394.94 |
| 9 | 33 | Cumulus | Lustre | 381.45 |
| 10 | 9 | OceanStor Pacific | OceanFS | 357.78 |
For organizations building AI infrastructure, Hammerspace software proves you can achieve performance at scale without creating a new silo or undergoing a forklift upgrade.
I’m Building AI Infrastructure, Not Running a Supercomputer—Why Should I Care About this Benchmark?
If you’re leading AI infrastructure in an enterprise or neocloud, it’s easy to assume that IO500 results are only relevant to elite HPC labs and supercomputing sites. But today’s AI workloads—training, inferencing, RAG, fine-tuning—place similar demands on infrastructure: they’re IOPS-heavy, metadata-sensitive, and require sustained, high-bandwidth access to massive data sets.
What matters isn’t just raw performance—it’s how that performance is achieved:
– Can it start small and scale without complexity?
– Can it integrate with existing storage?
– Can it deliver results without requiring a forklift upgrade?
– Can it avoid creating yet another storage silo to handle the I/O patterns of AI?
Too often, achieving performance means copying all your data, moving your applications, and rearchitecting your environment—just to make your data AI-ready. It shouldn’t have to be that way.
This is where the Hammerspace IO500 submission will help enterprises to design AI infrastructure that will meet their needs for the entire AI data backbone. The submission confirms that enterprise AI infrastructure can achieve HPC-class performance without specialized hardware, exotic networking, or proprietary clients—using a standards-based global file system that works with the infrastructure and data you already have.
With Hammerspace, organizations can assimilate existing siloed data into a unified global namespace—without migrating data or creating a new data storage silo. This makes current environments instantly AI-ready, eliminating the need to re-architect systems or move massive data sets. It’s not just high performance—it’s high performance made easy.
Hammerspace IO500 Test Configuration
The Hammerspace test setup was straightforward: 10 clients, eight Linux storage servers with NVMe drives, and a single metadata server, all connected over 400GbE.
The hardware is powerful, but not remarkably so – it’s off-the shelf stuff. There’s nothing special about the network, either, it’s just Ethernet using RDMA (RoCE). Nothing you couldn’t find in any enterprise data center. File access was via pNFSv4.2 with Flexfiles which is standard and built into every modern Linux distribution today.
This bears emphasizing – the client machines are just running stock Linux. There is no special client software, special kernel, or agent of any kind installed or required. The storage servers are also just running Linux, in this case a build with additional performance patches that we are contributing upstream into the open-source kernel. This is what a standards-based system looks like.
Note that “standards-based” doesn’t mean “standards-only.” The NFS standards define a set of basic tools. With any craft, quality depends more on the skill and experience of the craftsman than the tools themselves. Nobody has more experience with pNFSv4.2 and Flexfiles than Hammerspace, and we designed many of its tools. It is our pNFS metadata server implementation that enables the highest performance, and combining that raw performance with a comprehensive set of data services is what makes Hammerspace’s Data Platform uniquely powerful.
More About the IO500
IO500 participants range from small organizations to huge government labs, with entries in four categories:
| Category | Who Can Submit | Client Count | Client Size | Storage System Capacity/Nodes |
|---|---|---|---|---|
| Production | Production Customer | Any | Unlimited | Unlimited |
| 10-Node Production | Production Customer | 10 | Unlimited | Unlimited |
| Research | Anyone | Any | Unlimited | Unlimited |
| 10-Node Research | Anyone | 10 | Unlimited | Unlimited |
As you might expect, the scores cover a wide range, about five orders of magnitude. This is due to the range of different systems participating, and the fact that clients and storage systems tested can be of unlimited size, (and price points). If you want the top spot you’ll need to spend millions of dollars to get there.
The “10-Node” categories introduce a limit of 10 on the client count (clients are what run the benchmark code) as a way to compare setups that are a little more constrained. Hammerspace submitted results in the “10-Node Research” category.
The benchmark consists of a range of tests focusing on different workload patterns summarized in the table below. Tests involve read, write, delete, and stat operations. Results are output for each test and then combined and summarized.
| Workload | Details | AI Workload Relevance |
|---|---|---|
| IOEasy | File per process, large block sequential IO | Similar to training data loading and model saving |
| IOHard | Shared single file for all processes, 47KB+ IO | Small, random IO typical in optimizer state and KV cache updates |
| MDEasy | Directory per process, zero-length files | Pure metadata speed test |
| MDHard | Small files (3901 bytes) Single shared directory for all processes | Mimics checkpoint/restore logic, where file create/delete dominates |
| Find | Finding relevant objects based on patterns | Metadata search operation – custom Find algorithms are allowed |
Conclusion
Hammerspace’s IO500 results underscore the strengths of our simple, standards-based approach to high-performance storage. They show Hammerspace’s unique ability to enable organizations to leverage familiar enterprise technology and existing storage infrastructure to power their AI journey with unmatched efficiency.
We’re not chasing benchmarks with complexity—we’re redefining what’s possible with simplicity.
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