Lambda has closed its second institutional debt facility and its first $1 billion-plus GPU debt financing. The facility was oversubscribed and rated A (low) by Morningstar DBRS and Baa1 by Moody's. Proceeds will fund the purchase and development of GPU infrastructure supporting three committed customer deployments with two investment-grade offtakers across multiple data centers. Read the announcement:

Lambda is coming to Mayes County, Oklahoma. The new data center will deploy closed-loop cooling to reduce water use, and Lambda will pay 100% of its energy costs. It's expected to generate $500M in tax revenue over the next decade and create up to 1,000 construction jobs: We’re excited to join the Mayes County community and look forward to listening, learning, and working alongside local leaders and residents. We’re committed to being a good neighbor and long-term partner as this project moves forward. Read more in @tulsaworld:

Stephen Balaban (@stephenbalaban) has been through 5 pivots in 14 years. He started with facial recognition and AI filters before building the workstation and GPU compute business. The interview is a look at the long, non-linear path behind Lambda, and the decisions that shaped it along the way. Listen to the full conversation with @LambdaAPI’s CTO below on the @FoundersInArms podcast with @immad and @rajatsuri.

Ask a 3D vision-language model what's near the table and in front of the curtain, and it might guess "sewing machine." The right answer is a tray rack. CVP (UC San Diego + Lambda, WACV 2026) fixes this with a target-affinity token for task-relevant objects and an allocentric grid for global context. Against Video-3D-LLM: • SQA3D EM: 58.6 → 62.3 • Scan2Cap CIDEr: 83.8 → 90.5 • Better on all 5 benchmarks tested Full results across ScanQA, SQA3D, ScanRefer, Multi3DRefer, and Scan2Cap, plus how the central/peripheral split works:

GPU utilization increased from ~20% to 43% on a reservation of 96 NVIDIA H100 GPUs, while cutting queue starvation by 74%, with blocked jobs falling from roughly 10 per day to around 4. That’s the concrete result @SpreeAI saw after fixing their orchestration. When a unified diffusion model requires 80–100 GB of memory, you can’t simply throw workloads at a cluster and expect to use those GPUs efficiently. SPREEAI was dealing with workload fragmentation, ad-hoc submissions, and storage I/O blocking that left expensive GPUs idle. Working with Lambda’s ML engineering team, they implemented MLflow-based experiment orchestration with structured queuing and workload matching. They also connected Lambda’s Prometheus APIs to Grafana for real-time visibility into utilization gaps. The video testimonial covers how they diagnosed the bottlenecks and what the remediation looked like.

Build or buy AI? Wrong question. Lambda's @boborado is on the main stage at AI Infra Summit 2026 with @JLL CTO Yao Morin, @usbank EVP & Chief AI Officer Prashant Mehrotra, and @carrier Chief Data & AI Officer Arun Nandi, and the room's landing on the same answer: it's build AND buy. New term coined: valuemaxxing. Using value per task as a lens to govern resource allocation, while architecting around agility (easy model swaps) and availability (SLAs and model lifecycle).

Behind the scenes at AI Infra Summit 2026: @lambdaapi's Dave Ward, president of cloud services, shares how our @nvidia DSX MaxLPS testing let us run more nodes inside the same power budget, turning stranded capacity into usable compute.