
Why AI is now an infrastructure problem
Part one of AI Infrastructure 101, a seven-part series.
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Runpod expands the AI Developer Cloud in India with the AP-IN-1 region, giving developers another location for training and inference workloads.
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We've definitely heard the concerns about how difficult it can be to get the specific GPU spec that you want on Runpod, and we've been hard at work securing new supply to make this easier. We want your development efforts to succeed, and this won't happen if we can't provide the hardware.
Towards this goal, we've added a data center in AP-IN-1 with over 1MW of power capacity, which will focus on providing H100 80GB HBM3s.
Additional regions are already in various stages of buildout, and we'll keep shipping these announcements as sites come online. The short version of our commitment: if the GPU you want is one people are actually asking for, our job is to make sure you can get it on Runpod.
If there's a specific GPU SKU or region you'd like to see prioritized, reach out through our Discord or support, or drop a note to our sales team. The feedback genuinely shapes what we build next.
AP-IN-1 is live in the Runpod console right now.

To deploy:
Thanks, as always, for building on Runpod, and keep the feedback coming, no matter what it is.
Author profile: Brendan McKeag
Blog Posts

Part one of AI Infrastructure 101, a seven-part series.
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The LTX-2.5 weights are out, with day-zero ComfyUI support. Here's what actually changed, and what you need to get generating on Runpod today.

A practical guide for accurately calculating the VRAM requirements for full-parameter model fine-tuning, explaining why standard inference-based rules of thumb are insufficient and offering equations to help users properly size their compute resources.