MAXFlops
High-performance GPU and CPU clusters for materials, life-science, and AI research teams — VirtualLab handles design, operation, and tuning.
- Bare-metal GPU and CPU clusters with an integrated job scheduler
- Container-based, reproducible research environments
- Native integration with Materials Square and D3Square
- Full-service operations, monitoring, and tuning
Compute your team can actually use
- GPU
- A100 · H100 · RTX
- PB
- Parallel storage
- 24/7
- Monitoring & response
- SLA
- Guaranteed availability
Core capabilities
Infrastructure, scheduling, and operations bundled together so researchers do not carry the operations burden.
Bare-metal GPU & CPU
Full access to physical nodes with no virtualization overhead — network and storage designed for HPC workloads.
Container workflows
Singularity and Docker-based research environments make reproducing the same setup for papers and benchmarks straightforward.
Job scheduler
Standard schedulers like Slurm for team-level resource allocation, with priority, quota, and fair-share policies.
Monitoring & observability
Real-time view of utilization, queue status, and job history — spot bottlenecks quickly and act on them.
VirtualLab stack integration
Submit Materials Square and D3Square jobs directly to MAXFlops, with licenses and data pipelines included.
Full-service operations
Managed service option covers hardware procurement, installation, tuning, and incident response.
How teams adopt it
From dedicated cluster builds to rented resources, pick the path that fits your situation.
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01
Define requirements
Workload profile, concurrent users, storage and network requirements — scoped together.
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02
Design the cluster
GPU/CPU composition, interconnect, storage tiers, and software stack designed to fit the workload.
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03
Build or migrate
Install on-prem or place in the VirtualLab colocation facility. Migration from existing environments supported.
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04
Operate and evolve
Ongoing monitoring, incident response, and tuning. Scale up and upgrade as your workload grows.
Who uses it
Materials & energy R&D
DFT, MD, and ML-based screening and data generation pipelines at scale.
Life sciences & AI drug discovery
Protein structure prediction, molecular docking, and generative-model training workloads.
Universities & national labs
Shared research clusters with per-researcher quota and governance.
Manufacturing & engineering
Large-scale numerical analysis, CFD, FEA, and AI-based optimization simulation.
We unblock the infrastructure holding your R&D back
Tell us your workload, team size, and data characteristics — we will put together a fitting configuration and adoption path.