惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

WordPress大学
WordPress大学
B
Blog RSS Feed
The GitHub Blog
The GitHub Blog
爱范儿
爱范儿
博客园 - 司徒正美
J
Java Code Geeks
酷 壳 – CoolShell
酷 壳 – CoolShell
Engineering at Meta
Engineering at Meta
大猫的无限游戏
大猫的无限游戏
D
Docker
Blog — PlanetScale
Blog — PlanetScale
Recent Announcements
Recent Announcements
罗磊的独立博客
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - 聂微东
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
人人都是产品经理
人人都是产品经理
Stack Overflow Blog
Stack Overflow Blog
M
MIT News - Artificial intelligence
腾讯CDC
T
The Blog of Author Tim Ferriss
小众软件
小众软件
U
Unit 42
T
Tailwind CSS Blog

Runpod Blog.

DeepSeek V4 in the wild, and how to run it on Runpod Track GPU spend across your team with Cost Centers The GPU supply supercycle is here. Here’s what AI builders need to know. Community Spotlight: One-click AI image and video generation on Runpod with SwarmUI | Runpod Blog Community Spotlight: LoRA Pilot Data Prep to Inference Introducing the Runpod Assistant: Manage Your Cloud GPU Resources with Natural Language OpenAI's Parameter Golf: Train the Best Language Model That Fits in 16MB on Runpod LLM inference optimization: techniques that actually reduce latency and cost Pruna P-Video and Vidu Q3 public endpoints now available on Runpod Runpod brand spelling guide Quickstart - Runpod Documentation The AI market looks nothing like the narrative Training StyleGAN3 with Vision-Aided GAN on Runpod KoboldAI – The Other Roleplay Front End, And Why You May Want to Use It How to Connect Cursor to LLM Pods on Runpod for Seamless AI Dev Community Spotlight: How AnonAI Scaled Its Private Chatbot Platform with Runpod Prompt Scheduling with Disco Diffusion on Runpod Runpod's Latest Innovation: Dockerless CLI for Streamlined AI Development Run Your Own AI from Your iPhone Using Runpod Introducing Flash: Run GPU workloads on Runpod Serverless: No Docker required Use Claude Code with your own model on Runpod: No Anthropic account required Avoid Errors by Selecting the Proper Resources for Your Pod What hackers built on Runpod at TreeHacks 2026 Easily Back Up and Restore Your Pod with Cloud Sync + Backblaze B2 The Complete Guide to GPU Requirements for LLM Fine-Tuning AI Guides, Tutorials & GPU Infrastructure Insights | Runpod Your first Claude Code project within Runpod: a complete setup guide 10 billion Serverless requests and counting Building for resilience: Runpod’s response to the AWS us-east-1 outage How to Connect Google Colab to Runpod
New Runpod datacenter now live: AP-IN-1
Brendan McKeag · 2026-04-20 · via Runpod Blog.

New Runpod datacenter now live: AP-IN-1

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.

Deploy on AP-IN-1 today

AP-IN-1 is live in the Runpod console right now.

__wf_reserved_inherit

To deploy:

  1. Head to the Pods page in the Runpod console.
  2. Filter by H100 80GB HBM3 under GPU type.
  3. Select AP-IN-1 as your region.
  4. Pick your template or bring your own container, and deploy.

Thanks, as always, for building on Runpod, and keep the feedback coming, no matter what it is.

Author profile: Brendan McKeag

The Chips Got Faster. The Stack Didn't.

The Chips Got Faster. The Stack Didn't.

The bottleneck has moved.

All

Multi-Instance GPUs on Runpod: Stop Paying for Compute You Don't Need

Multi-Instance GPUs on Runpod: Stop Paying for Compute You Don't Need

With MIG, we can partition RTX 6000 Pro cards into isolated 24 GB instances. Here's when it makes sense for your workloads.

All

OpenAI Parameter Golf: what 1,100 researchers built in six weeks

OpenAI Parameter Golf: what 1,100 researchers built in six weeks

How 1,100 researchers beat OpenAI's own baseline with 16 megabytes and 10 minutes.

All

Build what’s next.

The most cost-effective platform for building, training, and scaling machine learning models—ready when you are.