RunPod, a leading cloud computing platform for AI and machine learning workloads, is excited to announce its partnership with vLLM, a top open-source inference engine. This partnership aims to push the boundaries of AI performance and reaffirm RunPod’s commitment to the open-source community.
vLLM, known for its innovative PagedAttention algorithm, offers unparalleled efficiency in running large language models. It is widely adopted as the default inference engine for open source large language models across public clouds, model providers, and AI powered products.
As part of this collaboration, RunPod provides compute resources for testing vLLM’s inference engine on various GPU models. The partnership also involves regular meetings to discuss AI engineers’ needs and ways to advance the field together.
“Our collaboration with vLLM represents a significant step forward in optimizing AI infrastructure,” said Zhen Lu, CEO at RunPod. “By supporting vLLM’s groundbreaking work, we’re not only enhancing AI performance but also reinforcing our dedication to fostering innovation in the open-source community.”
Also Read: Voxel51 Launches FiftyOne Open Source 1.0
The partnership builds on RunPod’s involvement with vLLM dating back to summer 2023. This long-term engagement underscores RunPod’s commitment to advancing AI technologies and supporting the development of efficient, high-performance tools for AI practitioners.
“vLLM’s PagedAttention algorithm is a game-changer in AI inference,” added Jean Michael Desrosiers, Head of Customer at RunPod. “It achieves near-optimal memory usage with less than 4% waste, significantly reducing the number of GPUs needed for the same output. This aligns perfectly with our mission to provide efficient, scalable AI infrastructure.”
RunPod‘s support of vLLM extends beyond technical resources. The collaboration aims to create a synergy between RunPod’s cloud computing expertise and vLLM’s innovative approach to AI inference, potentially leading to new breakthroughs in AI performance and accessibility.
Source: Businesswire
