Lambda at GTC 2026: an early preview

2026-03-10 · Source: The Lambda Deep Learning Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, short

Summary

Lambda is a Platinum sponsor at NVIDIA GTC 2026, showcasing its Superintelligence Cloud at Booth #1507. This infrastructure is designed for large-scale foundation model training and production inference, featuring high-density, liquid-cooled AI data centers. Lambda's Superclusters are engineered for NVIDIA Vera Rubin NVL72 and integrate NVIDIA Co-Packaged Optics (CPO) and Quantum-X InfiniBand for optimized networking fabrics. The company emphasizes rack-scale system design, foundational power density planning, and integrated liquid cooling. Live demonstrations will include LLM fine-tuning on NVIDIA Blackwell GPUs with Quantum-2 InfiniBand and an end-to-end deployment of infrastructure provisioning and model serving on Lambda's cloud platform. Maxx Garrison will lead a session on deploying Lambda's Bare Metal Instances with NVIDIA Vera Rubin NVL72 and GB300 NVL72.

Key takeaway

For CTOs and VPs of Engineering deploying large-scale foundation models, Lambda's Superintelligence Cloud offers dedicated, single-tenant AI supercomputing as a service. Your teams should consider this approach for deterministic performance, enhanced security, and direct co-engineering, especially when scaling with NVIDIA Vera Rubin NVL72 and GB300 NVL72 rack-scale systems. This infrastructure is built for next-generation accelerated computing.

Key insights

Rack-scale AI infrastructure requires integrated power, cooling, and networking for optimal performance and scalability.

Principles

• Liquid cooling is a baseline for high-density AI.
• Networking must be foundational, not bolt-on.
• Rack-scale systems demand facility-level readiness.

Method

Lambda designs and deploys single-tenant, purpose-built Superclusters with integrated power, liquid cooling, and NVIDIA CPO/InfiniBand networking for large-scale foundation model training and inference.

In practice

• Explore NVIDIA Vera Rubin NVL72 for rack-scale AI.
• Evaluate CPO switches for bandwidth bottlenecks.
• Prioritize integrated infrastructure design.

Topics

• AI Infrastructure
• Superclusters
• NVIDIA GPUs
• Liquid Cooling
• Foundation Models

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, MLOps Engineer, AI Architect

Related on AIssential

• Lambda at NVIDIA GTC 2026: building the Superintelligence Cloud — Lambda's Superintelligence Cloud integrates advanced NVIDIA hardware for scalable, high-performance AI workloads.
• Liquid-cooled AI systems expose the limits of traditional storage architecture — Hybrid air/liquid cooling for AI infrastructure is inefficient and creates thermal liabilities for storage.
• Inside the NVIDIA Rubin Platform: Six New Chips, One AI Supercomputer — NVIDIA's Rubin platform uses extreme co-design to optimize entire data centers for continuous, scalable AI intelligence production.
• NVIDIA GTC Studio with Insights from Vertiv — Digital twins and converged infrastructure are critical for scaling AI data centers efficiently and rapidly.
• Running AI Workloads on Rack-Scale Supercomputers: From Hardware to Topology-Aware Scheduling — NVIDIA Mission Control and related tools bridge hardware topology with scheduler abstractions for optimal AI/HPC workload placement.
• Saturate your Tensor Cores: Intel at NVIDIA GTC 2026 — Balanced CPU+GPU system design is crucial for maximizing GPU utilization and scaling AI workloads effectively.

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Lambda Deep Learning Blog.