Co-Founder
Lambda
Lambda provides on-demand GPU cloud infrastructure for AI training and inference, offering access to NVIDIA B200, H100, and other accelerators through single-tenant clusters. The platform supports rapid scaling from individual instances to multi-node superclusters with high-bandwidth interconnects, backed by SOC 2 Type II compliance and pay-as-you-go pricing.
- Artificial Intelligence
Updated 28 days ago
Funding
Raised to date
$3.2BRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
$2.4B across 5 disclosed rounds
+22$1.5B · Series E
Announced Nov 2025
Disclosed cumulative: $2.4B
5/5
Reported total $3.2B
Incomplete round history. $765M of the $3.2B reported total isn’t matched to a known round.
RoundDateAmountValuationInvestors
Founders
Product
Problem
AI teams often face limited access to scalable, high-performance GPU infrastructure, leading to long provisioning cycles, under-utilized resources, and difficulty meeting the compute demands of large-scale model training and inference. Existing on-premise or generic cloud solutions can lack the latest NVIDIA hardware or the security guarantees required for mission-critical workloads.
Solution
Lambda offers an on-demand supercomputing platform that provisions private, single-tenant GPU clusters equipped with the latest NVIDIA GB300 NVL72, B200, and H200 accelerators. Users can launch instances through a web console or API with a single click, enabling rapid scaling from a single node to multi-node superclusters. The platform integrates high-bandwidth NVLink and InfiniBand interconnects to sustain data-parallel training at petaflop scale. All clusters are built with SOC 2 Type II compliance, encryption-in-transit, and role-based access controls to meet enterprise security standards. Compute usage is metered per GPU-hour, allowing teams to align costs directly with workload demand while avoiding upfront capital expenditure.
Target Audience
Primary customers are AI research labs, enterprise ML engineering teams, and startups developing large-scale deep learning models that require secure, high-throughput GPU compute for both training and inference.
Features
- 1-Click Clusters™ for instant provisioning of up to 64-GPU supernodes with automated networking and storage configuration
- Access to NVIDIA GB300 NVL72, B200, and H200 GPUs, delivering up to 2 PFLOPS of mixed-precision performance per node
- Private, single-tenant environments with SOC 2 Type II compliance, encrypted storage, and isolated VPC networking
- High-speed NVLink and InfiniBand fabric providing up to 200 Gbps inter-GPU bandwidth for distributed training
- RESTful API and CLI tools for programmatic instance lifecycle management and integration with CI/CD pipelines
- Pay-as-you-go billing model with per-GPU-hour metering and optional reserved capacity discounts
- Integrated monitoring dashboard with real-time utilization metrics, GPU health alerts, and cost analytics
- Support for popular ML frameworks (TensorFlow, PyTorch, JAX) and container orchestration via Docker and Kubernetes