Introduction
Hammerspace Tier 0 turns GPU server-local NVMe into high-performance shared storage, managed and protected by Hammerspace. Tier 0 storage is up to 10x faster than networked storage – so you can reduce checkpointing time for AI training and HPC, and improve response times for inferencing and agentic AI. It can be activated in hours – on-prem or in the cloud – with no clients or agents to install, so you can put GPUs to work immediately. And it enables optimal use of assets you already own so you can reduce the need for external flash storage – to reduce costs, and gain back the power and rack space those systems would otherwise consume.
This Competitive Brief provides a high-level comparison between Hammerspace Tier 0 and select competitors – Vast Data, Weka, and DDN with Lustre – followed by a more detailed side-by-side comparison against Weka’s Converged Mode offering.
| Hammerspace | Vast Data | Weka | DDN with Lustre | |
|---|---|---|---|---|
| Local NVMe Option | Yes, Tier 0 | No Tier 0 equivalent | Yes, Weka Converged Mode | Yes – Persistent Client Cache |
| Client Connectivity | No client to install – just Linux. | N/A | Requires Weka client | Requires Lustre client |
| Operational Complexity | Deploy on standard Linux without modification. | N/A | Complex, kernel module dependent | Complex, kernel module dependent |
| Use of Compute Resources | Minimal, runs in kernel space | N/A | High overhead on compute nodes | Low overhead, but kernel coupled |
| GPU Utilization Efficiency | Maximized via local data access, up to 10x faster than external storage | Bottlenecked by shared storage and external network | High | Variable; limited by Lustre client |
| Data Orchestration | Global namespace, policy-driven tiering to disk or NVMe | Limited only to Vast Data Platform | Limited, flash-to-object tiering on NVMe only | Requires custom scripting |
| Hybrid / Multi-Cloud Ready | Yes; single namespace across sites / clouds. Data is orchestrated based on policy | Limited. Vast clusters in the cloud lack performance and scale. | Data must be tiered to object and re-hydrated | No global namespace, no ability to orchestrate data between sites/clouds |
Hammerspace Tier 0 Compared to Weka Converged Mode
This table provides a more in-depth, side-by-side comparison between Hammerspace Tier 0 and Weka Converged Mode.
| Capability | Hammerspace Tier 0 | Weka Converged Mode |
|---|---|---|
| Architecture Model | Disaggregated metadata/data | Metadata + data services co-located on compute nodes |
| Client Connectivity | No client to install – just Linux. | Requires Weka Client |
| Compute Resource Consumption | Lightweight client; metadata services run elsewhere | CPU/memory overhead; runs in user space |
| GPU Utilization Efficiency | High | High |
| Data Orchestration | Global namespace, policy-based data movement | No built-in data movement or orchestration across nodes |
| Hybrid / Multi-Cloud Readiness | Cloud-native, supports hybrid with unified namespace | Must tier data into S3 and then rehydrate for use in the cloud. |
| Data Locality Optimization | Automatically places data near jobs (AI/ML aware) | Static; tied to filesystem distribution logic |
| Minimum Deployment | Any number of GPU servers | WEKA will require 128 GPU servers minimum or a deal size of $300K TCV. |
Summary
Hammerspace Tier 0 is the only option for local NVMe that is standards-based, does not require a proprietary client, and minimizes compute resource consumption in order to maximize GPU utilization and accelerate AI pipelines on-prem and in-cloud.