HPC Capacity Expansion

A system that was correctly sized at delivery can be entirely under-spec two years later. Workloads grow, GPU appetite explodes, AI/ML projects demand new architectures. Our HPC capacity expansion service lets you scale without halting production, without wasting prior investment, and without piling up technical debt.

“Expansion” is not one thing

Depending on the situation, we apply one of four approaches — or a combination:

Scale-out

Adding compute nodes to the existing cluster. Often the fastest, most economical path.

Analysis of whether the existing network topology can absorb new nodes
Zero-downtime node addition — bringing capacity online while jobs are still running
Image consistency: new nodes match the existing software stack bit-for-bit
Performance homogeneity verification (new nodes must match existing performance)

Scale-up

Upgrading the existing nodes with more powerful components.

Memory expansion (especially relevant for simulation workloads)
Adding an NVMe scratch tier
CPU upgrades (with compatibility validation)
Migration to a faster interconnect

GPU and accelerator expansion

By far the most frequently requested scenario in the AI / ML / generative-AI era.

Selecting the right model from NVIDIA A100, H100, H200, B100/B200 generations for your workload
Verifying expansion headroom on existing servers (slots, power, cooling)
Topology planning for new GPU nodes with NVLink and InfiniBand
A software stack aligned with PyTorch, TensorFlow, JAX
Reference architectures for LLM training and inference

Storage tier expansion

As workloads grow, the bottleneck is often I/O rather than compute.

Parallel filesystem expansion (BeeGFS / Lustre / Spectrum Scale)
Two-tier architecture: fast NVMe scratch + capacity-rich object store
Data lifecycle policies: hot / warm / cold tier
Backup and disaster recovery strategy

Modernisation and tech refresh

Sometimes the right answer is partial modernisation rather than expansion:

Cases where five-year-old nodes are so inefficient that one new node delivers more than three old ones
Migrating from legacy Ethernet to InfiniBand HDR/NDR
Moving from a legacy parallel filesystem to a modern alternative
Transformation from traditional HPC to AI-centric infrastructure

These choices double effective performance while protecting prior investment. Our consulting team determines which path delivers the highest return — independently.

What is included

Capacity analysis report — growth projections based on actual workload trends
Architectural renewal recommendations (scale-out / scale-up / GPU / storage)
BOM and TCO modelling
Procurement coordination (if Mevasis sources the hardware)
Zero-downtime rollout in production, or a planned maintenance window
Acceptance testing for the new components and homogeneity validation against the existing system
User training on how to use the new capacity
Updates to the operations and support contract

Customer outcome

Phased growth instead of doubling the investment in one go — friendlier to budget cycles
0–4 hours of production downtime in typical expansions; very often zero
Performance homogeneity — old and new nodes share the same queue without surprises
For AI workload migration, 3–6 months saved versus building a new system from scratch

Common short questions

We want to add GPUs but worry our existing servers can't handle the power and cooling.

The first step is a power and cooling audit: rack-level draw, room capacity, PDU adequacy. Often the answer is yes; if not, we typically propose a “GPU node chassis” plus a separate mini-rack solution. For H100/H200-class deployments, site readiness must be revisited from scratch.

Our system is 7 years old. Is expansion or modernisation the smarter call?

The honest answer comes from measuring performance per kilowatt-hour of your workload. Old hardware often hides large electricity costs; one new-generation node can outperform three old ones in total productivity. The decision falls out of the consulting report.

Production cannot stop. How do you handle expansion?

Compute-node addition is typically zero-downtime: new nodes join SLURM in “drain” state, get imaged, are validated, then opened to the queue. If the network or storage layer must be touched, a planned maintenance window (usually weekend, 4–8 hours) is sufficient.

Should I build a separate cluster for AI/ML?

Not necessarily. Many of our customers run AI workloads alongside HPC on the same cluster, using a separate GPU partition and appropriate scheduler policy. If the workloads have very different criteria (latency-sensitive inference, continuous training), a separate architecture may make sense — that decision belongs in the consulting phase.

The lifecycle continues

Capacity expansion is not really an end-step; it is the start of the next round of strategic thinking. New workloads bring new questions; consulting begins again, and the cycle repeats. Mevasis is the long-term technical partner who keeps that continuity intact.