
Agentic AI Workflows Explained: Patterns, Infrastructure, and GPU Requirements
Agentic workflows plan, loop, and burst differently than a single model call — here's what that means for the infrastructure underneath.
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With the launch of AP-JP-1 in Fukushima, Runpod expands its Asia-Pacific footprint, improving latency, access, and compute availability across the region.

We're excited to announce the launch of AP-JP-1, Runpod's first data center in Japan—now live in Fukushima. This marks a major step forward in our global infrastructure strategy and opens the door to dramatically better performance for users across the Asia-Pacific region.
Until now, developers and organizations in Asia had to rely on Runpod's US- or EU-based regions, facing latency ranges of 150–200ms. With AP-JP-1, users in Japan, South Korea, and nearby countries can now expect latencies as low as 8-50ms, enabling lower-latency inference and smoother workflows across the board.
For Japanese institutional entities operating under stringent regulatory frameworks, AP-JP-1 delivers comprehensive compliance with national data sovereignty mandates—a critical consideration for sectors managing sensitive information assets such as finance, healthcare, and governmental organizations
We’re launching AP-JP-1 with NVIDIA H200s, our most powerful GPU offering, ideal for large model training, fine-tuning, and high-throughput inference.
AP-JP-1 is purpose-built for:
Whether you're running real-time inference, fine-tuning large models, or ensuring your infrastructure complies with local regulations, AP-JP-1 brings the power of Runpod closer to you.
Deploy your next workload in AP-JP-1 now via the Runpod console.
For questions or feedback, reach out to our team or join the conversation on Discord.
This strategic expansion exemplifies Runpod's core mission to democratizing access to high-performance AI infrastructure on a truly global scale
Author profile: Alyssa Mazzina
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