Lambda

Use Cases

Large Language Model Training: State-of-the-art foundation model training (GPT-scale, reasoning models) using multi-GPU clusters with InfiniBand networking supporting distributed training across hundreds or thousands of GPUs.​

Fine-Tuning and Model Adaptation: Domain-specific LLM fine-tuning for legal, medical, financial, and code generation applications using flexible on-demand instances with PyTorch and HuggingFace integration.​

Vision and Multimodal Research: Computer vision, object detection, video understanding, and multimodal model development using Lambda GPU capacity with PyTorch, TensorFlow, and JAX support.​​

AI Research and Innovation: Academic researchers and AI labs conducting neural architecture search, optimization algorithm research, and novel training technique exploration at competitive costs.​

Production Inference Serving: Deployment of trained models as production inference services with sub-millisecond latency requirements or variable-traffic patterns requiring elastic scaling.​

Government and National Security AI: U.S. government entities and Department of Defense leverage Lambda infrastructure for classified AI workloads, frontier model research, and strategic AI capability development.​

Hyperscaler Infrastructure Partnerships: Microsoft Azure expansion and enterprise AI services powered by Lambda-hosted tens of thousands of GPUs through multibillion-dollar partnership agreement.​

Startup AI Product Development: Early-stage AI companies validate product concepts and develop AI services using startup-friendly pricing before scaling to hyperscaler platforms.

Customers & Markets

Lambda serves over 200,000 AI developers globally across diverse customer segments including AI researchers and PhDs at academic institutions (MIT, Stanford, Harvard, Caltech), frontier labs, and companies; enterprise AI teams at Fortune 500 companies building internal copilots and analytics; hyperscalers and cloud providers including Microsoft (multibillion-dollar partnership), Amazon Research, and Google; U.S. government and defense agencies supporting national security and strategic AI initiatives; AI startups building products with constrained hardware budgets; and system integrators and MLOps platforms embedding Lambda infrastructure for customers.​

High-profile customers and partners span Microsoft, Amazon Research, Google, Tencent, Intel, Kaiser Permanente, Disney, Los Alamos National Laboratory, and U.S. Department of Defense. Lambda operates 25,000+ GPUs positioned as a critical infrastructure provider for frontier AI workloads. The company’s market position strengthened significantly through its November 2025 Series E funding ($1.5+ billion at $4+ billion valuation), signaling investor confidence in Lambda’s role within the AI infrastructure stack as GPU demand continues exceeding supply from hyperscalers.​

Research, Partnerships & Innovations

Research Focus

Lambda prioritizes research on gigawatt-scale AI infrastructure for frontier model training and inference at unprecedented scale, energy efficiency and liquid cooling optimization to reduce operational costs and environmental impact, low-latency GPU interconnects enabling distributed training across thousands of GPUs with minimal communication overhead, model optimization and inference efficiency maximizing throughput and minimizing latency, and emerging GPU architectures ensuring infrastructure adapts to NVIDIA’s latest silicon generations (B200, GB300, GB400).​

Strategic Partnerships

Microsoft Partnership (November 2025): Multi-year, multibillion-dollar agreement for Lambda to provide AI infrastructure powered by tens of thousands of NVIDIA GPUs including GB300 NVL72 systems supporting Microsoft’s enterprise AI initiatives, Azure expansion, and Copilot infrastructure, representing an 8+ year relationship expansion.​

NVIDIA Partnership: Deep co-engineering partnership providing preferential access to latest GPU generations (B200, H200, GB300, GB400) and NVIDIA Exemplar Cloud certification, ensuring customers access newest architectures before general availability.​

Data Center Infrastructure: Strategic relationships with Colovore, Aligned Data Centers, ECL, and Prime Data Centers enabling rapid geographic expansion and capacity scaling.​

Enterprise and Research: Collaborations with Fortune 500 companies, national laboratories, and academic institutions (MIT, Stanford, Harvard, Caltech) for specialized AI workloads and frontier research.​

Product Innovations

Lambda Stack: Continuously-updated open-source deep learning software managing CUDA, PyTorch, TensorFlow, JAX across all infrastructure types with automated dependency resolution.​​

1-Click Clusters™: Automation enabling deployment of 16-2,000+ GPU clusters with InfiniBand, Kubernetes, and production-ready configuration in hours versus weeks of engineering.​

Hyperplane Hybrid Cloud: Seamless integration between on-premise and cloud GPU infrastructure enabling cross-environment workload orchestration without switching ecosystems.​

Lambda Chat: Open-source model hosting enabling inference on DeepSeek, Llama, and foundation models without vendor lock-in.​

NVIDIA Exemplar Cloud Certification: Industry-leading validation for AI infrastructure performance, reliability, and cost-effectiveness maintained for four consecutive years.​

Gigawatt-Scale AI Factories: Engineering and construction of proprietary AI supercomputing facilities starting with Kansas City, Missouri (24 megawatt capacity, expandable to 100+ megawatts, opening early 2026).​

Key People

Co-Founders

Stephen Balaban – Co-Founder and CEO: Computer Science and Economics graduate from University of Michigan; first engineering hire at Perceptio (machine learning startup acquired by Apple for on-device neural inference). Since co-founding Lambda in March 2012, Balaban has scaled the company to serve 200,000+ developers and raised $1.7+ billion in funding. His strategic vision centers on making AI computing ubiquitous and accessible through the philosophy “one person, one GPU,” positioning Lambda as essential infrastructure for the era of superintelligence.​​

Michael Balaban – Co-Founder and Chief Technology Officer: Mathematics and Computer Science degree from University of Michigan; previously Software Engineer at Nextdoor. Leads Lambda’s technology direction, ensuring product alignment with cutting-edge AI infrastructure requirements and maintaining competitive advantage in hardware and software engineering.​

Current Leadership

Leonard Speiser – Chief Operating Officer: Leads operational strategy and execution for Lambda’s scaling initiatives across research, engineering, sales, and partnerships.​

Heather Planishek – Chief Financial Officer: Oversees financial strategy, fundraising, and capital allocation for gigawatt-scale AI infrastructure expansion and construction.​

Robert Brooks IV – VP, Business Development and Revenue: Leads enterprise sales, customer partnerships, and major deals including Microsoft multibillion-dollar agreement.​

Paul Miltenberger – VP of Finance: Manages financial planning, budgeting, risk management, and financial governance.​

David Hall – VP of NVIDIA Solutions: Leads NVIDIA partnership strategy and GPU allocation optimization for customer benefit.​

Ariel Nissan – General Counsel: Oversees legal strategy, contracts, compliance, and risk management.​

Thomas Bordes – Head of Marketing: Directs marketing strategy and brand positioning to enterprise and developer audiences.