CE - IC - Hardware Management for Liquid Cooling - Workstream (2026-03-18)

Why It Matters

Adopting LLM‑driven mock‑up generation can accelerate liquid‑cooling hardware development, but only if teams enforce prompt precision and schema validation to maintain compliance and reliability.

Key Takeaways

• LLM-generated Redfish mockups accelerate liquid‑cooling hardware design process.
• Current mockup and interop profile backlog still pending release.
• Claude produced plausible rack, manifold, and CDU specifications.
• Generated schemas used outdated Redfish version, requiring manual correction.
• Prompt detail and profile inclusion improve LLM output fidelity.

Summary

The March 18 meeting revisited the ongoing hardware management workstream for liquid‑cooling systems, confirming that the series of update calls will continue and outlining the year’s focus on delivering mock‑ups and interoperability (interop) profiles for various cooling topologies. Participants reviewed the status of published assets, including a CDU mock‑up, a baseline liquid‑cooling service profile, and early versions of liquid‑gold server cold‑plate and immersion‑tank designs, while noting that several items remain in the pipeline, such as public immersion‑tank profiles and newer concepts like reservoir‑pumping units and liquid‑cooled bus bars.

The team highlighted a backlog of mock‑ups and interop profiles that have yet to be finalized, emphasizing the need for iterative feedback and alignment with the Open Compute Project (OCP) specifications. Detailed diagrams were presented for multiple immersion‑tank configurations—standalone, external CDU, redundant, and chassis‑enclosed—illustrating the complexity of the required documentation. The discussion also covered emerging topologies, including heat‑reuse loops, air‑assisted cooling side‑cars, and large‑scale heat‑pump integrations, underscoring the expanding scope of liquid‑cooling solutions.

A central experiment involved using the Claude LLM to auto‑generate a Redfish schema for an eight‑node liquid‑cooled rack with a CDU. The model produced a structurally sound mock‑up, correctly listing chassis sensors, manifolds, and CDU parameters, and even suggested redundant coolant connectors and leak‑detection sensors. However, it relied on an outdated Redfish version, misplaced some sensor locations, and stopped mid‑generation, indicating the need for precise prompts, explicit profile inclusion, and post‑generation validation.

The findings suggest that generative AI can markedly speed the creation of compliance‑ready hardware specifications, but successful deployment will require disciplined prompt engineering, version‑control of schemas, and rigorous review processes. Integrating LLM‑assisted drafting into the hardware design workflow could reduce engineering lead times and free resources for higher‑level design challenges, provided the output is vetted against current standards.

Original Description

Public call recording of CE - IC - Hardware Management for Liquid Cooling workstream.