System gaps
Catch assembly, configuration, and dead-on-arrival issues before delivery.
Ship racks with performance confidence.
ClusterReady
Validate complete AI racks under real workloads. Revenue-ready at delivery, Day 1.
What standard checks miss
Catch failures before Day 1.
Component checks miss system-level failures. ClusterReady tests the complete rack under AI workloads before it ships.
Silent failures
Expose RCCL, NVLink, and bandwidth failures that standard testing misses.
Delayed revenue
Accelerate revenue generation in hours, not weeks.
Low output
We bring your hardware to its full performance potential (e.g., tokens per second).
Why it matters
10×
Faster rack validation.
AI workloads and XPerf tests turn weeks of manual validation into hours of automated process.
Validation sequence
Bring an assembled rack to production-ready.
Four automated stages take every rack from arrival to a verified, optimized, revenue-ready state.
-
01
Pre-flight health check
Check GPU, CPU, memory, network, and storage.
Make sure the rack is in the health status.
-
02
AI workloads validate
Run MLPerf and XPerf workloads at cluster scale.
Expose link, thermal, bandwidth, and scaling failures.
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03
Stress test the limits
Push every server and network link to peak load.
Find marginal hardware before deployment.
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04
Fix and optimize
Turn every finding into a clear fix.
The only tool that offers diagnosis, remediation suggestions, and optimization guidance.
What we validate
The rack as one system.
Hardware, software, and AI workloads are validated together, not in isolation.
Hardware
GPU health, PCIe, network links, storage, thermals, and power.
Software
OS, kernel, GPU drivers, RDMA stack, containers, and connectivity.
AI workloads
Inference + training, throughput, accuracy, latency, scaling efficiency, power consumption, and temperature.
Case study
AMD MI325X rack. First public performance result.
4 nodes. 32 AMD MI325X GPUs. 256 GB HBM3e. RoCE and RDMA.
- FLUX.1-DEV performancevs. same-system baseline
- +20%
- Multi-node scaling efficiency1-node to 4-node
- 95.8%
- Llama 2 70B tokens per second
- 33,695
- RCCL bandwidth gain
- 10×
Supported hardware
Works with the GPUs you ship today.
More GPUs are continuously added.
MI355X · MI350X · MI325X · MI300X
GB300 · B300 · H200 · H100
Get started
Download ClusterReady.
Choose the build for your validation workstation.
Windows
x64 · Windows 10 or later
[ Download Windows ]macOS
Apple Silicon and Intel · macOS 11.0 or later
[ Download macOS ]
Linux
x64 · Ubuntu 22.04 or compatible
[ Download Linux ]Take Control of Your Data Center
Validate faster, prevent downtime, and get more from every accelerator.