Chip level heat extraction for ultra dense AI and HPC delivering higher performance, lower energy use, and consistent thermals at scale
Desert Dragon provides Direct-to-Chip (DTC) as a managed service. The coolant is circulated directly over cold plates to remove heat at the source, unlocking densities and performance air cooling cannot support. The offering is turnkey—integrated plant CDUs, approved fluids, leak detection, and precision thermal controls are built into the facility for rapid rack roll‑out.
Operational integration & safeguards
Our AI tool – FinBladeAI continuously monitors workloads and thermal behavior, predicts demand shifts, and auto‑tunes setpoints in closed loop to maintain SLAs with minimum energy use. The result: stable training/inference throughput, lower energy per TFLOP, and reduced lifecycle OPEX through fewer thermal failures.

Design-dependent CDU classes support 65–85 kW capacity, maintaining stable clocks under sustained load while reducing IT fan power consumption. This enables predictable rack-level heat removal, allowing high-power GPU nodes to operate efficiently and sustain extended AI training workloads.
Tight ΔT control at both cold-plate and loop levels helps extend component lifespan while maintaining consistent time-to-train performance. Our SLO-driven thermal envelopes are aligned with training throughput and inference latency targets to ensure reliable and predictable AI cluster performance.
Facility-integrated heat rejection options, including dry coolers, plate-and-frame exchangers, and campus loop integration, are engineered for redundancy and seasonal efficiency. Fluid health telemetry, leak detection, pressure and flow analytics, and automated alarm routing feed directly into NOC and SOC runbooks to ensure continuous monitoring and rapid operational response.
Qualified installation and commissioning procedures are supported by a structured spare-parts strategy, incident response protocols, and periodic system validation. Concurrent maintenance practices ensure service continuity during component replacements, maintenance activities, and infrastructure upgrades.
Sensing and learning capabilities ingest loop metrics such as flow, pressure, and ΔT, along with rack sensors, server telemetry, and workload signals including queue depth and GPU utilization. The system continuously learns heat profiles for each topology, accounting for node type, rack composition, and workload patterns across time of day and seasonal conditions.
The system automatically tunes pump speeds, valve positions, and CDU setpoints within defined guardrails to maintain thermals within SLO bands. It also schedules preventive actions such as filter changes and fluid checks, and recommends maintenance windows based on predicted operational risk.
The system correlates anomalies, such as micro-leaks or flow restrictions, with workload events and triggers guided operational runbooks for rapid response. It also captures lessons learned from incidents and continuously updates operational policies to reduce the likelihood of repeat events.
Our service scope spans design, implementation, operations, and continuous optimization, including:
Implementation & Commissioning: Qualified procedures, flushing/charging, leak testing, and baseline capture. Acceptance testing with performance sign-off (ΔT, flow, and stability under defined load)
Run Operations (24/7): Real-time telemetry, alarms, trending, ticketing, and escalations via ITSM. FinBladeAI adaptive control and policy governance with human-in-the-loop approvals for higher-risk actions
Maintenance & Reliability: Preventive maintenance cadence, spares strategy, and concurrent service methods. Quarterly validation cycles (thermal drills, capacity headroom checks, and firmware interplay checks)
Reporting & Reviews: Executive dashboards with SLA/SLO attainment, MTTA/MTTR, ΔT stability, energy per rack, and capacity headroom. Monthly service reviews and a continuous-improvement backlog with prioritized remediation
AI Performance (Track): Adds model-aware steering (e.g., staging or pausing low-priority training jobs when thermal envelopes tighten) and “cost-per-experiment” analytics for client program managers
Mixed-Cooling Estate: Unified FinBladeAI control across DTC and immersion blocks, with shared reporting and common runbooks
Sovereign Analytics Pack: On-prem analytics warehousing (RBAC and audit trails), Arabic/English UI, and export-controlled data governance options
The following examples illustrate typical Service Level Objectives (SLOs) and operational transparency measures applied to liquid cooling environments. Actual targets and reporting metrics are tailored to each client contract and infrastructure configuration.
Thermal SLO: ≥ 99.9% of intervals within the ΔT target band at the cold plate
Leak Response: P1 isolation ≤ 5 minutes; recovery to a safe state ≤ 30 minutes
Performance SLO: 95th percentile time-to-train variance ≤ the agreed threshold under steady load
Client-Visible Dashboards: ΔT and setpoint history, CDU utilization, energy per rack, incident timeline, and change log with approvals
Deployment Notes: Qualification of server SKUs and cold-plate kits is required prior to service adoption. Actual kW per rack or CDU depends on IT design, ambient design points, and heat-rejection selection. OEM warranty alignment for IT equipment remains under client policy; Desert Dragon supports approved kits and processes
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Luke Good is a digital infrastructure professional and technical advisor at Evermore, where he supports the evaluation, development, and optimization of digital infrastructure investments.
With experience spanning data center operations, design, and customer-focused solution delivery, Luke specializes in bridging the gap between technical execution and capital deployment. He provides insight into data center design, operational performance, and alignment with customer-specific requirements, ensuring solutions are both technically robust and commercially viable.
In his role at Desert Dragon, Luke contributes to technical due diligence and investment strategy, helping to assess opportunities and guide the delivery of scalable, high-performance infrastructure platforms. With a strong foundation in critical facilities and hands-on operational environments, he brings a practical and informed perspective to strategic decision-making.
As Evermere’s Middle East Principal in the Digital Infrastructure Division, and with over 30 years of experience in the industry, Robert Karssiens is a digital infrastructure strategist and deal architect specialising in the development, delivery and monetisation of at scale data centre platforms. Working with his colleagues, Robert combines commercial execution, capital structuring, and technology positioning to deliver large-scale infrastructure outcomes across the Middle East.
Dr. Kholood AlNutaifi is a prominent Saudi regulatory and government affairs leader whose career spans more than two decades across national institutions, oversight, and public‑sector modernization. Her work with the Ministry of Health (MOH) included advising on strategic programs, crisis‑response operations during COVID-19 at the national level decision‑making frameworks, and inter‑agency coordination mechanisms—capabilities essential for guiding the deployment of emerging technologies within high‑governance environments.
As a highly respected Saudi woman leader, Dr. Kholood strengthens Desert Dragon’s commitment to diversity, inclusion, and alignment with the Kingdom’s Vision 2030 priorities. Her growing specialization in advanced digital systems and AI, combined with her extensive policy and regulatory experience, enables her to bridge the intersection of technology, governance, and national security considerations. Her presence institutionalizes credibility and supports the company’s mission to deploy advanced technologies responsibly, safely, and in full compliance with national development and regulatory expectations.
Tobias brings two decades of global leadership across capital markets, advanced technology, and large-scale infrastructure—experience directly aligned with Desert Dragon’s mission. In his role as the Board’s Advisor, he will help with global strategic insight, ensuring the company’s innovations align with the technological, economic, and geopolitical shifts defining the next decade. His presence strengthens Desert Dragon’s ability to build a future-ready platform with the scale, sophistication, and credibility required to lead technology transformation in KSA.
As CEO of Evermere, Tobias leads high-stakes investment and technology initiatives spanning critical global systems, including advanced computing, digital infrastructure, AI-driven platforms, and sovereign-grade economic architecture. His work advising sovereign funds, institutional investors, global financial partners, and technology operators gives him a unique perspective on how frontier technologies transition from prototype to scalable national capability.
What sets Tobias apart is his rare combination of founder-level operational expertise and institutional investment acumen. He has built and exited multiple technology ventures, giving him a hands-on understanding of system design, scalability, and technical execution. At the same time, he has structured and led complex, high-value transactions that shape national and regional infrastructure.
Farah Zeid leads Desert Dragon’s administration, focusing on workforce development, talent acquisition, and leadership development. Her mission is to build a high-performance team capable of supporting the Kingdom’s growing technology sector.
Mohammed AlDhabaan provides strategic leadership and long-term vision for Desert Dragon Data Centers. With extensive experience across government collaboration, infrastructure development, and strategic investment initiatives in the Kingdom, he guides the organization’s role in supporting Saudi Arabia’s digital transformation. His leadership ensures that Desert Dragon’s growth aligns with national priorities under Vision 2030 while fostering partnerships that accelerate technological innovation across the Kingdom.