4 GPU AI Server
An entry-point rackmount AI server for teams moving beyond the workstation.
We help you choose, source, and procure the right infrastructure — no obligation.
Configuration at a Glance
Tailored per engagement. Full technical overview below.
Overview
The 4 GPU AI Server is the natural next step for teams that have outgrown desktop GPUs and need rack-mounted, manageable, always-on compute. Nexus Compute specifies the GPU type, interconnect, and supporting infrastructure to your workload, then sources and configures the complete system for deployment in your facility or colocation.
Who This Solution Is For
Business Benefits
Shared, always-on compute
A rack server serves the whole team continuously, replacing contended desktop GPUs and idle cloud instances.
Configured to your workload
We match the GPU choice — from RTX to data-center cards — to whether you train, fine-tune, or serve.
Manageable and remote
Out-of-band management and rack form factor fit standard IT operations and remote administration.
A clear scaling path
Start at four GPUs and grow into our 8-GPU and cluster solutions as demand increases.
Typical Business Use Cases
Shared team training and fine-tuning workloads
Production inference serving for internal applications
Consolidating GPU access across multiple developers
On-premises pilots before committing to a cluster
Industry Applications
Technical Overview
A 4U dual-socket platform supporting four GPUs — configurable from RTX-class to data-center cards — with high-core-count CPUs, large ECC memory, redundant power, and out-of-band management. We finalize GPU and interconnect choices with you.
| GPU Capacity | 4× GPUs (RTX 5090 / RTX PRO / H100 PCIe — configurable) |
| CPU | Dual AMD EPYC or Intel Xeon |
| System Memory | Up to 1.5TB ECC |
| Storage | Hot-swap NVMe (configurable capacity) |
| Networking | 25/100GbE; InfiniBand optional |
| Management | IPMI / out-of-band remote management |
| Power | Redundant N+1 power supplies |
| Form Factor | 4U rackmount |
Specifications are indicative and configured to each engagement. Request a quote for a configuration tailored to your requirements.
Frequently Asked Questions
Which GPU should I choose?
It depends on your workload. Training and large-model work favor data-center GPUs; cost-sensitive inference and development can use RTX-class cards. Our specialists recommend the right fit during your quote.
Do I need a data center to host it?
It can run in a server room with adequate power and cooling, or we can advise on colocation. We confirm facility requirements as part of the quote.
Can it join a cluster later?
Yes. We specify networking that allows it to be incorporated into a larger cluster as your needs grow.
Procurement Assistance
Source the 4 GPU AI Server with Nexus Compute
Tell us your requirements and a procurement specialist will help you specify, source, and quote the right configuration — typically within two business days. No obligation.
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