Asia Is Buying GPUs by the Trainload: Korea’s 260,000-Blackwell Plan and Malaysia’s New Nvidia DC — Why DecentralGPT’s Distributed LLM Network Fits This Moment

Asia didn’t slow down over the weekend.

On November 3, South Korea revealed a plan to secure 260,000 of Nvidia’s newest Blackwell AI chips in a deal worth up to $10 billion — one of the boldest public steps we’ve seen from a national AI strategy in 2025. The goal is clear: train large-scale LLMs in Korea, keep talent and data in-region, and avoid depending entirely on US or Chinese clouds.

On the same day, YTL Power in Malaysia completed a new Nvidia-powered AI data center in Johor, again pointing in the same direction: Southeast Asia wants its own AI capacity, not hand-me-down compute.

Add to that Google Cloud’s latest numbers — AI is now one of Alphabet’s fastest-growing cloud businesses — and you get the bigger picture: AI is becoming regional, not only centralized.

This is exactly the world DecentralGPT was designed for.

What today’s news really means

1. AI is being localized.

When Korea orders 260,000 Blackwell GPUs, it’s not just about training flashy LLMs — it’s about latency, data residency, and having your own AI supply line. That’s the same set of problems we solve with decentralized, regional GPU nodes.

2. Central cloud won’t be enough.

Even Google says AI is stretching its infra, and smaller countries are building their own AI DCs to avoid bottlenecks. A decentralized network like DecentralGPT lets you spread inference across community-supplied GPUs instead of waiting for one hyperscaler to add your region.

3. LLMs are still growing, but context and reasoning are hard.

At the Agents4Science 2025 conference, researchers showed LLMs could co-author papers — but they also showed models still lose focus on long, complex tasks. That’s why routing to the right model, in the right region, at the right cost, actually matters more than yet another gigantic model.

Where DecentralGPT fits

1) Regional, decentralized inference

If Korea, Malaysia, Singapore, and the Gulf are all standing up Nvidia-based AI data centers, application teams will want to serve users from those regions — not from a single US-East endpoint. DecentralGPT already follows this philosophy: run LLMs on nodes close to users, keep latency down, and make AI usable for real products, not just demos.

2) Vendor-agnostic by default

Today’s news is very Nvidia-heavy — but we all know AMD, local accelerators, and even cloud-custom chips will show up in 2026. A real AI network can’t be locked to one GPU vendor. DecentralGPT’s stack is vendor-agnostic and model-agnostic, so we can run GPT-style models, Claude-style reasoning models, Qwen for APAC languages, and DeepSeek-style open models in the same network. That’s safer for builders when GPU pricing swings.

3) Web2 + Web3 in one place

Most of the new DCs in Asia will serve classic Web2 customers (telcos, gov, finance). But Web3 users want wallet login, token payment, and AI agents that can read on-chain data. DecentralGPT keeps both doors open — chat/app for normal users, token-based access and DGC payments for Web3. That lets us onboard traffic from centralized clouds and crypto ecosystems at the same time.

4) A flywheel for GPU miners and node operators

If Asia keeps buying GPUs and not all of them are 100% booked, they can be plugged into decentralized inference and earn DGC for serving LLM calls. That’s how we turn a regional AI build-out into a global AI network.

Try DecentralGPT (multi-model chat): https://www.degpt.ai/

Run a GPU / prepare to serve regional LLM traffic: https://www.decentralgpt.org/nodes/

Business / exchange / cloud-gaming / telco integration: contact@decentralgpt.org