# Lambda at GTC 2026: an early preview March 10, 2026• 5 min read ## **Join Lambda at NVIDIA GTC 2026** **Built for AI. Ready for Superintelligence.** Lambda is heading to NVIDIA GTC 2026 as a Platinum sponsor. At Booth #1507, we’re showcasing how the Superintelligence Cloud is built in the real world, from power and liquid cooling to rack-scale Superclusters engineered for [NVIDIA Vera Rubin NVL72](https://www.nvidia.com/en-us/data-center/vera-rubin-nvl72/). If you’re running large-scale foundation model training or production inference systems, this is where the architecture decisions get made. ## **Why Lambda is at GTC** GTC is where the AI ecosystem gathers to look ahead. Hardware is moving toward rack- and POD-scale. Networking is moving to co-packaged optics. And the teams deploying foundation models in production are being asked to do more than ever before, with higher density, lower latency, and greater reliability. We design, build, and deploy high-density, liquid-cooled AI data centers co-engineered across power, cooling, networking, and software. We deliver single-tenant Superclusters for organizations training large foundation models and operating production inference systems at scale. That is the Superintelligence Cloud. It’s not a generic capacity. It’s infrastructure, built with intent. ## **Infrastructure for large-scale frontier AI models** AI infrastructure is moving to rack-scale systems. That shift changes how clusters are designed and operated: - Power density planning becomes foundational - Liquid cooling is a baseline requirement for density - Fabric design becomes the difference between scaling cleanly or stalling under load Earlier this year, we announced our work around NVIDIA Co-Packaged Optics (CPO) and next-generation networking fabrics. In that announcement, we outlined how fabric efficiency compounds at the cluster scale and why networking must be engineered as part of the system rather than added later. [NVIDIA Quantum-X InfiniBand](https://www.nvidia.com/en-us/networking/products/infiniband/quantum-x800/) Photonics CPO switches are here. CPO switches eliminate the bandwidth bottleneck between racks and change the performance-per-watt calculus at the cluster scale. Our Superclusters are designed with this in mind from day one. Networking is not a bolt-on. It is foundational to sustained performance for distributed training of foundation models. ## **Superclusters for Superintelligence** Lambda Superclusters deliver a dedicated, single-tenant AI supercomputer as a service. Each deployment is purpose-built, shared-nothing, and engineered for large-scale foundation model training and production inference. For superintelligence labs and enterprise AI leaders, that means: - Deterministic performance - No noisy neighbors - Full control over security and compliance - Direct co-engineering with infrastructure experts These are not instances layered onto a shared cloud. They’re AI factories delivered as production-ready Superclusters. ## **What you’ll see at Booth #1507** We’re running two live demos that bring the Superintelligence Cloud to life: **Live LLM fine-tuning on NVIDIA Blackwell GPUs** Watch a real fine-tuning workload running on [NVIDIA Blackwell GPUs](https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/) connected via [NVIDIA Quantum-2 InfiniBand](https://www.nvidia.com/en-us/networking/quantum2/). A live dashboard displays training loss, throughput, GPU utilization, efficiency metrics, and system health. You'll see exactly where performance holds and where lesser infrastructure would stall. **End-to-end deployment on Lambda** See how quickly you can provision GPUs, deploy infrastructure, and serve a model on Lambda's cloud platform. From available compute to live inference, the full lifecycle is visible. One shows what Lambda can sustain. The other shows how fast you can move. Both matter in production. ## **Learn from our experts** At GTC, [Maxx Garrison](https://www.linkedin.com/in/maxxgarrison/) is leading a session on deploying Lambda's Bare Metal Instances with NVIDIA Vera Rubin NVL72 (coming soon) and [GB300 NVL72](https://www.nvidia.com/en-us/data-center/gb300-nvl72/), covering what rack-scale readiness actually requires. Rack-scale systems like NVIDIA Vera Rubin NVL72 require facility-level readiness, fabric alignment, and bare-metal provisioning that preserves system intent. When we say we’re ready, we mean: - Power density planning is complete - The liquid cooling strategy is engineered - The networking topology is aligned with rack-scale systems - Production deployment is designed from day one This is not theoretical compatibility. This is infrastructure built for the next generation of accelerated computing. Add Maxx's session to your agenda: > Deploy Lambda’s Bare Metal Instances with NVIDIA Vera Rubin NVL72 & GB300 NVL72 > [S82151](https://www.nvidia.com/gtc/session-catalog/sessions/gtc26-s82151/) ## **Explore resources before you arrive** Ahead of GTC, you can explore: - Video: [How Lambda builds GPU clusters for the age of superintelligence](https://www.youtube.com/watch?v=b5bHHMvFXoI) - Video: [How Lambda builds data centers that keep up with GPU generations](https://www.youtube.com/watch?v=5doJzo8a7kU) - Overview: [Lambda's platform](https://lambda.highspot.com/items/68ffb99077137018b697ddce) - Datasheet: [Lambda Superclusters](https://view-su2.highspot.com/viewer/4a750b212c77be4a460a5bbe390834cd) - Datasheet: [NVIDIA Vera Rubin NVL72 Supercluster](https://view-su2.highspot.com/viewer/7bcfe404cab45e44d2989b3bbd99d889) - Whitepaper: [Reproducible MFU optimization techniques](https://lambda.ai/llama-3-1-blackwell-mfu-benchmarks) Schedule an in-person meeting with our team at [lambda.ai/nvidia-gtc](https://lambda.ai/nvidia-gtc) We’ll see you in San Jose.