AI infrastructure engineering
Infrastructure engineering for growing teams.
We design, deploy and support servers, data systems and open-source AI infrastructure for small and mid-sized organizations.
Start with an assessment, deployment or focused technical pilot.
Services
From architecture to a working system.
Consulting, server deployment, system debugging and ongoing support—delivered by the engineers doing the work.
Infrastructure consulting
Architecture, hardware selection, capacity planning, procurement review and deployment strategy.
Server deployment
Component validation, assembly, firmware, Linux installation, remote access and handoff.
System debugging
CUDA, drivers, containers, multi-GPU behavior, thermals, storage and performance bottlenecks.
Ongoing support
Maintenance, upgrades, incident troubleshooting and planned expansion after deployment.
How projects begin
Start with a defined technical scope.
We begin with clear requirements, deliverables and next steps.
Review workload, constraints and current environment.
Receive a proposed architecture, risks and next steps.
Validate the workload on a real system before scaling.
Typical projects
Common customer projects.
Deploy an internal model server
Prepare a dedicated system for private inference, remote access and a repeatable application stack.
Build a multi-GPU workstation or server
Validate power, thermals, lanes, memory, storage and software compatibility before handoff.
Resolve instability or poor performance
Investigate driver conflicts, container issues, GPU visibility, throttling and system bottlenecks.
Move from cloud experiments to dedicated hardware
Translate an existing workload into a practical server configuration and operating plan.
Set up image and video generation systems
Configure reliable environments for repeated generation, queues and team access.
Validate before buying eight GPUs
Benchmark the actual workload and identify whether scale-up is justified.
Why X-YORK
One team across hardware, systems and applications.
Our team combines Carnegie Mellon computer science training with hands-on experience across servers, electrical and low-voltage work, databases, e-commerce systems and AI deployment.
Read our company story →Built around the growing need for practical modern infrastructure.
Direct access, custom scope and no need for a large internal infrastructure team.
Open-source AI, databases, e-commerce systems, networking and operations.
Available hardware
Dedicated GPU systems.
Monthly reference pricing starts at $549 for RTX 5090 systems and $899 for RTX PRO 6000 systems. Multi-GPU configurations are quoted to specification.
RTX 5090 systems
Single-, dual-, four- and eight-GPU configurations for generation, experimentation and parallel workloads.
See engagement options →RTX PRO 6000 systems
Single-, dual-, four- and eight-GPU configurations where larger GPU memory is important.
Request a configuration →Need help with a system or deployment?
Tell us what you are building and where you are blocked.