• GPU cloud company to award cloud credits to hundreds of researchers, powering the most GPU-dependent groundbreaking AI research
• Lambda continues company momentum as it aims to double the size of its research team in the next year, bolsters Lambda-led research output, and opens a new San Francisco Office

Lambda, the AI developer cloud, today announced a research grant to power AI research breakthroughs and researchers’ most GPU-dependent workloads. Lambda expects to sponsor hundreds of researchers in the coming year, who will receive up to $5,000 in Cloud Credits and mentoring from Lambda’s Chief Scientific Officer, Chuan Li. Lambda is also scaling its research program in 2025, actively hiring for GenAI and multimodal research, aiming to double the team within a year.

Research is the driving force of innovation in machine learning and AI, which is fundamentally restructuring and augmenting science, commerce, and industry. Through this research grant and the expansion of its own research team, Lambda is reaffirming their commitment to bringing compute power and AI cloud services to researchers across the world, and funding reliable research to help advance the broader machine learning community.

“I’m honored to play a role in aiding and amplifying the new wave of research groundwork required for the future of technology, made possible through our grant,” said Chuan Li. “Lambda’s mission is to make AI compute as ubiquitous as electricity and build the best AI compute platform in the world. This grant, alongside the expansion of our research team and Lambda-led research, brings us one step closer to that vision.”

Lambda’s Research Grant additionally expands our partnership with Nous Research, the creator of the very popular Open Source Hermes large language models series, or more recently Forge, a groundbreaking Reasoning engine. Nous’s ‘Researcher-in-Residence’ program will receive compute access through the program.

“Nous Research began in 2021 as an open-source research organization that was largely decentralized online. As a company today, Nous keeps the spirit of open-source alive through our commitment to continued research beyond our full-time team’s scope,” said Karan Malhotra, co-founder at Nous Research. “Our Researchers-in-Residence cohort hones in on experimental architectural research—a series of “what-ifs” and unorthodox methods that aim for revolutionary changes to model steerability, capabilities, and reasoning speed. Previously, our Researchers-in-Residence have yielded a synthetic data generation model, implementation of control vectors and prompt-tuning in llama.cpp, and high-engagement social agents. We are continuing this program as we grow, and implementing the fruits into our overall development cycle.”

Lambda’s recent growth is further supported by the opening of its first San Francisco office, as the company continues to solidify its presence within major technology hubs nationwide. The new office will serve as a hub for Lambda’s talent, notably their Machine Learning Research, Engineering and Product teams. This opening increases Lambda’s footprint by 50% in Silicon Valley, while offering new location and commute options to the best talent.

"Some of the best engineering talent is well concentrated in San Francisco. Adding a new location in the heart of the city helps us find and bring together the best engineers in a conveniently located workspace, where they can collaborate to build the best AI compute platform in the world," said Christian Kaiser, VP of Engineering at Lambda.

Applications for the grant are now open and will be accepted on a rolling basis. AI researchers can apply here: https://lambdalabs.com/research

About Lambda

Lambda was founded in 2012 by AI engineers with published research at the top machine learning conferences in the world. Our GPU cloud and on-prem hardware enables AI engineers to easily, securely and affordably build, test and deploy AI products at scale. Lambda’s mission is to accelerate human progress with ubiquitous and affordable access to computation. One person, one GPU.

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