The mission is the same across all of them. The form it takes depends on the environment.
AI factories · Training infrastructure · Large-scale inference
For model training, large-scale inference, and sovereign AI build-outs where your IP stays inside the perimeter. Liquid-cooled, GPU-dense, rack-optimized — benchmarked against your actual workload before a single unit ships.
GPU Compute Nodes
HPC Rack Systems
Liquid Cooling
Storage Arrays
Field AI · On-premises inference · Ruggedized deployments
For models that have to run inside the hospital, on the factory floor, in the vehicle, inside the bank network — where cloud latency fails and data sovereignty is non-negotiable.
Edge Inference Appliances
Ruggedized Nodes
In-Vehicle Compute
5G MEC Platforms
Local inference · HPC research · Clinical AI · Developer compute
For researchers, engineers, clinicians, and analysts who need local model inference without the data center. Validated for the models they actually run. The data stays on the machine.
GPU Workstations
Clinical AI Terminals
Research Compute
Local LLM Inference
GPU upgrades · Liquid cooling conversions · Architecture work
You don’t have to start over. GPU retrofits, liquid cooling conversions, memory and network upgrades — we do the work that Tier-1 OEMs won’t. Built around what you already own.
GPU Retrofits
Liquid Cooling Conversion
Memory Upgrades
Power & Thermal
The IT industry built its infrastructure discipline on standardization — and for 30 years, that was right. AI workloads break that assumption. Every model has different hardware requirements. Every deployment environment imposes its own constraints on top of the workload. The cost of applying a standardized answer to a precision problem compounds in every direction. Equus is built for the moment when standard stops being the right answer.
AI workloads impose constraints that standard compute was never designed for. Energy density up to 10x what most facilities were built to support. GPU components backordered 6–12 months on the open market. Performance requirements that only reveal themselves under real inference load. Deployment environments — hospital racks, factory floors, vehicles, air-gapped facilities — that impose their own hardware requirements on top of the workload. The organizations that move fast are the ones with a partner who already knows how to solve these.
The IT industry built its procurement, support, and vendor management processes around predictable, standardized workloads. Apply that same process to AI infrastructure and every assumption breaks. Wrong GPU configuration means poor inference performance under real load — a failure that doesn’t show up until the model is in production. Standard data center power infrastructure wasn’t designed for 40–120kW GPU density. Depot repair support doesn’t serve a hospital network running clinical AI at 2am. And the hardware most buyers specify isn’t available on the open market — NVIDIA Blackwell lead times running 6–12 months, HBM memory in shortage. The heterogeneous IT stack was built for a different problem. Applying it to AI workloads doesn’t reduce risk. It creates it.
The most overlooked risk in AI infrastructure isn’t getting the hardware wrong. It’s assuming you can get new hardware at all — on the timeline and at the grow your deployment requires.
Equus builds the hardware layer beneath your model — custom-configured, model-validated compute that runs your inference, your training, your edge deployment, exactly where it needs to run. Built for your specific workload, your specific environment — not a generic configuration applied to both.
We build for the entire journey — your desk, your edge, your data center. The workstation you train on. The cluster you validate against. The inference node you ship to your customer. Purpose built for each phase, not configured from a catalog.
And unlike a Tier-1 OEM, we stay. Lifecycle management, on-site support, refresh cycles — the infrastructure relationship that keeps your model and IP running at year five, not just at deployment.
Full-rack AI infrastructure for model training and large inference deployments. Liquid-cooled, GPU-dense, benchmarked for your workload before it ships.
Compact inference nodes for factory floors, hospitals, vehicles, and remote sites. Ruggedized for the environments where cloud doesn’t work.
Your model runs against our hardware in our Innovation Lab before deployment. Not spec-matched. Performance-validated.
For AI software companies that need on-premises deployments at customer sites. We build what your model and IP ship on.
EQCare service plans, on-site technicians, and dedicated account managers. The infrastructure relationship that lasts past go-live.
Tell us your model, your quantization, your serving framework, and where it needs to run. We’ll tell you exactly what hardware it needs — and validate it before it ships.
We are the hardware layer beneath your software product.
Deploy into constrained environments, hospitals to factory floors.
Large-scale inference and sovereign data centers.
