Headquarters: San Jose, California
Founded and led by deep learning engineers, Lambda provides deep learning infrastructure including a GPU cloud service, on-prem servers, GPU clusters, GPU workstations, and GPU laptops to customers such as Intel, Microsoft, Google, Amazon Research, Tencent, Kaiser Permanente, MIT, Stanford, Harvard, Caltech, and the Department of Defense.
The rapid adoption of artificial intelligence (AI) in various industries has led to a high demand for graphics processing units (GPUs), crucial for AI software training and inference tasks. This surge in demand, coupled with production challenges from the pandemic and geopolitical conflicts, has resulted in a significant GPU shortage, impacting startups and enterprises that depend on these resources for AI development. Consequently, this has opened opportunities for alternative sources of GPU capacity, catering to the unmet needs in the market.
Lambda Labs addresses the GPU shortage problem by offering cloud services and specialized hardware tailored for AI and machine learning applications. Through its offerings, Lambda Labs enables organizations to access the necessary computational power for AI and ML development, overcoming the limitations posed by the global GPU shortage and high equipment costs.
Lambda’s platform provides access to Nvidia graphics cards that customers can use to train artificial intelligence models and perform inference. The startup’s GPU catalog includes, among others, Nvidia’s flagship H100 data center chip. The H100 features 80 billion transistors that allow it to run large language models up to 30 times faster than its predecessor.
Lambda's products include GPU Cloud, Echelon, Hyperplane, Scalar, NVIDIA DGX Systems, Vector One, and Tensorbook.
Lambda Labs' cloud product is a dedicated GPU cloud service tailored for deep learning. The company offers on-demand and reserved cloud GPUs for AI training and inference, with instances pre-configured for machine learning and pre-installed with popular ML frameworks such as Ubuntu, TensorFlow, PyTorch, NVIDIA CUDA, and NVIDIA cuDNN.
Lambda Echelon is a GPU cluster designed for AI. It comes with the compute, storage, network, power, and support users need to tackle large-scale deep-learning tasks. Echelon offers a turn-key solution to faster training, faster hyperparameter search, and faster inference.
Lambda Echelon clusters come with the new NVIDIA H100 Tensor Core GPUs and deliver unprecedented performance, scalability, and security for every workload. The NVIDIA H100 is an integral part of the NVIDIA data center platform.
Built for AI, HPC, and data analytics, the platform accelerates over 3,000 applications, and is available everywhere from data center to edge, delivering both dramatic performance gains and cost-saving opportunities.
The Lambda Hyperplane is a high-performance server designed for AI research and deep learning tasks. It is equipped with NVIDIA H100 or A100 GPUs and AMD EPYC 9004 series CPUs, offering unprecedented performance, scalability, and security for various workloads.
The Lambda Scalar is the ultimate GPU server for deep learning. Lambda Scalar servers comes with the new NVIDIA H100 Tensor Core GPUs and delivers unprecedented performance, scalability, and security for every workload.
The NVIDIA DGX systems combines the best of NVIDIA software, infrastructure, and expertise in a modern, unified AI development solution that spans from the cloud to on-premises data centers.
Lambda’s Vector One is a GPU desktop PC built for deep learning. The single GPU system is purpose-built for AI/ML projects, with liquid cooling designs, next-gen graphics for advanced AI/ML, and future-ready architecture.
Lambda, in collaboration with Razer, created the Lambda Tensorbook, the world's most powerful laptop designed for deep learning, available with Linux and Lambda’s deep learning software. The sleek laptop, coupled with the Lambda GPU Cloud, gives engineers all the software tools and compute performance they need to create, train, and test deep learning models locally.
Lambda Labs also provides colocation services that allow companies to keep their servers at its own data centers. If a customer-owned system experiences a malfunction, Lambda engineers can help fix it.
Lambda Labs offers a flexible on-demand pricing model, where customers are billed per second only for the time they actively use the platform. This approach allows users to choose their preferred GPU and only incur costs for hourly usage, aligning charges with actual consumption.
Lambda Labs' Reserved Cloud service provides a solution for users requiring consistent access to GPU clusters, ensuring availability when needed. This service is structured with flexible contract options, ranging from single to multi-year agreements, and features adaptable pricing that varies based on the selected GPU and the number of usage hours.
Lambda offers its hardware products at fixed prices, ensuring transparent and consistent pricing for customers.
According to The Information, Lamba Labs forecasted $250 million in revenue for 2023. That projected revenue is more than twice as high as Lambda’s revenue in 2022. For 2024, Lambda has projected revenue of close to $600 million.
In April 2023, for the third consecutive year, Lambda was chosen as an NVIDIA Partner Network (NPN) Solution Integration Partner of the Year in the Americas. NPN Partner of the Year awards honor and recognize companies for their impact on AI education and adoption through the use and distribution of NVIDIA accelerated computing.
In December 2023, Lambda Labs launched the Vector One. The new single-GPU desktop PC is built to tackle demanding AI/ML tasks, from fine-tuning Stable Diffusion to handling the complexities of Llama 2 7B. Lambda customers can now benefit from a more compact, quieter desktop PC at a price point of less than $5,500.