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Aolani and FriendliAI Partner to Advance AI Inference at Scale

SINGAPORE - Media OutReach Newswire - 10 September 2026 - Aolani, a Singapore-founded neocloud powering AI growth, today announced a partnership to supply GPU cloud infrastructure to FriendliAI, the San Francisco-headquartered inference cloud for frontier AI to support the rapidly growing demand for inference services.



The global market for AI inferencing is expanding quickly as AI applications become part of everyday business workflows and organisations move from experimentation to deployment at scale. At the forefront of production-scale AI, FriendliAI serves this exact demand to help developers and enterprises deploy open-weight and custom AI models.

FriendliAI was founded by researchers who invented continuous batching, which is now a standard across AI inference serving. The company has built its inference stack end to end, from optimised GPU kernels to global distribution, so that production AI workloads run fast and reliably at scale. FriendliAI consistently ranks as one of the fastest inference providers on OpenRouter, with enterprise clients including LG, Kilo Code, and Liner running their production inference on the platform.

Efficient time-to-value and dependable compute are increasingly important to keep services responsive as usage grows. As access to reliable compute infrastructure becomes a strategic differentiator for companies scaling production workloads, more AI natives are turning to Asia for high-performance compute capacity, attracted by the region's expanding digital infrastructure, strategic connectivity, and growing AI ecosystem.

As one of the leading neoclouds offering purpose-built next-generation AI infrastructure, Aolani helps AI natives scale more efficiently. Aolani's infrastructure capabilities across orchestration, automation and lifecycle management actively supports FriendliAI's services. This partnership equips FriendliAI with the compute to serve the rapid customer demand, both across the globe and increasingly in Asia.

Nicholas Chia, Chief Executive Officer at Aolani said: "We're seeing inference needs grow faster than companies can find compute to support and service their customers. To narrow the supply and demand gap, we actively partner with companies like FriendliAI to deliver compute capacity on time, at scale, and to rigorous standards. We look forward to partnering with the FriendliAI team to grow its services to bring fast and reliable inference to developers worldwide."

Byung-Gon Chun, Founder and CEO of FriendliAI said: "We are seeing exponential growth in demand for our frontier AI inference services. Businesses need the freedom to choose the AI models that best suit their applications and the ability to run them efficiently in production. Our job is to deliver high-performance, reliable inference so developers can focus on building their AI applications. Aolani stood out as a trusted infrastructure partner that can help us scale at the pace our customers need. We look forward to working with Aolani to support our mission."

Media Contact
H/Advisors on behalf of Aolani
pr@aolanicloud.com
Hashtag: #Aolani #Neocloud #FriendliAI #AIinference #Inferencecloud



The issuer is solely responsible for the content of this announcement.

About Aolani

Where AI gets built in Asia. Founded in Singapore, Aolani is backed by compliant and purpose-built infrastructure to deliver the performance capabilities for next-generation AI. Aolani's AI factories enable organisations to build with confidence, scale ambitiously, and move at hyper-speed in the world's fastest-growing AI market.

For more information, visit www.aolanicloud.com and follow on LinkedIn.

About FriendliAI

FriendliAI is the inference cloud for frontier AI. Headquartered in San Francisco with a team in Seoul, FriendliAI runs open models in production at scale for AI-native startups and enterprises through its Model APIs, Dedicated Endpoints, and Bring Your Own GPU (BYOG) offering. The team built the full inference stack end to end, delivering the speed, reliability and efficiency that agentic AI workloads demand.

For more information, visit .

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