Reconext has brought three new AI-focused servers online in our New York data center! The systems are owned and operated in-house, dedicated to AI development and model deployment.
What This Means for Your Program
Reconext now operates 24 AI accelerators in our own data center, on hardware in the same class used by major AI labs for large language model training. What that means in practice:
Stronger Data Control
Sensitive workloads are processed within Reconext-managed infrastructure. Customer data does not travel through external cloud environments to power AI capabilities.
Faster Operational Insight
With dedicated compute in-house, Reconext teams can run analysis more readily and surface patterns, exceptions, and performance trends faster than manual review allows.
Improved Quality Support
In-house AI capacity lets Reconext build and run inspection, diagnostics, and exception-handling support at a scale that is difficult to achieve otherwise, bringing more consistency to quality-sensitive processes.
Better Forecasting and Planning
Predictive analytics can identify demand patterns and process risks before they become problems. Dedicated compute makes those models accessible rather than occasional.
More Automation
Document review, workflow routing, reporting, and data extraction are areas where AI can take on manual work. This infrastructure gives Reconext’s teams the capacity to build and run those automations at scale.
Smarter Customer-Facing Tools
The infrastructure supports future dashboards and service intelligence tools, giving customers better visibility into their programs without requiring manual effort on either side.
How It Works
Three servers now operate inside our New York data center, delivering 24 AI accelerators and approximately 1.25 terabytes of dedicated accelerator memory combined.
The centerpiece is built around eight Intel Gaudi2 accelerators, the same class of hardware used by major AI labs for large language model training. It carries 768 GB of high-bandwidth memory in a single chassis and handles sustained, large-scale training work.
The two supporting systems are NVIDIA GPU-based servers configured for inference, fine-tuning, and analytics workloads, a proven platform for production AI applications.
All three systems run on Ubuntu Linux LTS, with mirrored storage and built-in remote management for reliability.
Infrastructure at a Glance
| Capability | What It Supports | Customer Benefit |
| 24 AI accelerators | Parallel processing for AI and analytics workloads | Faster development of AI-enabled tools and automation |
| 1.25 TB of dedicated accelerator memory | Larger models and datasets | Better ability to analyze complex operational data |
| Intel Gaudi2 training accelerators | AI training and model development | Custom tools that reflect Reconext workflows and customer needs |
| NVIDIA V100 GPUs with NVLink | Inference, fine-tuning, and analytics | Proven AI workloads and production-oriented business applications |
| 1 TB of RAM per server | Large-scale data preparation | Reduces bottlenecks when working with large operational datasets |
| Fast NVMe and tiered storage | Rapid access to models, datasets, and working files | Speeds up analysis and AI development cycles |
| High-speed 100GbE networking | Movement of large datasets between systems | Faster processing and better integration with internal systems |
| Mirrored storage | Protection against drive failure | Supports reliability and continuity for important workloads |
| Out-of-band management | Remote monitoring and troubleshooting | IT can respond faster and maintain the environment more efficiently |
| Ubuntu Linux LTS | Stable operating platform | A durable foundation for secure, maintainable AI development |
Where the Work Gets Done
Each of Reconext’s core service lines has something to gain here. The in-house AI environment gives teams the capacity to run these capabilities on real program data, within Reconext infrastructure.
ITAD Programs
AI can speed up asset classification, surface recovery trends, and flag exceptions that manual review would miss. Reporting that once required manual assembly can be generated automatically.
DCD Programs
Large-scale data center decommissioning involves a lot of moving parts. AI can support predictive planning, flag exceptions across complex datasets, and give customers clearer visibility into project status.
Repair, Refurbishment, and Lifecycle Services
AI-assisted diagnostics and failure trend analysis make quality and turnaround more consistent. Parts forecasting becomes more reliable when pattern recognition informs it.
Across All Programs
Automated reporting, internal copilots for service teams, and customer-facing dashboards are all within reach. The goal in each case is the same: customers should have the information they need without having to ask for it.
What Comes Next
The infrastructure is in place, and Reconext teams are actively applying it across programs. For clients, that means AI-assisted capabilities are no longer on the roadmap. They are available now and are being built into how we work.
If you want to understand how these capabilities apply to your program specifically, reach out to us.
