OpenAI’s Custom Chip Plan and China’s Nvidia Hunger: Why Vendor-Agnostic, Regional Inference Favors DecentralGPT

What happened today (the short version)

• OpenAI is preparing mass production of an in-house AI chip with Broadcom as soon as next year—partly to reduce reliance on Nvidia and secure long-term compute. Financial Times

• Chinese tech giants still want Nvidia AI GPUs despite pressure to buy local alternatives; the H20 remains in demand and a China-specific Blackwell variant (B30A) is being eyed by buyers. ReutersTom's Hardware

Bottom line: the chip landscape is getting regional and multi-vendor. Centralized procurement is riskier; routing workloads by region, cost, and policy is now part of shipping AI.

Why this matters for builders (and buyers)

• Vendor lock is a business risk. Supply or policy shocks can hit uptime and unit economics. A plan that only works with one GPU family or one geography is fragile. Financial TimesReuters

• Latency is a product feature. Running inference near users (e.g., Asia vs. U.S.) is often more visible to customers than a 1–2% quality delta.

• Cost signals will stay noisy. If Chinese demand pulls on Nvidia’s China-specific chips (H20/B30A), pricing and availability elsewhere can wobble. ReutersTom's Hardware

How DecentralGPT fits the moment

DecentralGPT runs a decentralized LLM inference network across a distributed GPU backbone. Instead of pushing every call through one cloud in one region, workloads can be placed where they make sense:

• Vendor-agnostic by design: route across heterogeneous GPU providers.

• Regional routing: pick nearby capacity to cut latency and align with local policies.

• Predictable spend: distributed supply helps smooth local price spikes.

• B2C + B2B: DeGPT for users; a straightforward API for teams.

• Capacity growing: GPU mining (useful-work) is in development to add more regional nodes and reward operators in DGC.

The strategy is simple: meet demand where it is, not just where a single supplier ships.

Quick start (two tracks)

• Try it like a user: open DeGPT and compare response feel across times of day.

• Ship it like a team: get an API key, select a region, and route non-critical traffic first; expand once latency and cost look good.

Run your AI where your users—and policies—are.

Start with DeGPT: https://www.degpt.ai/.

Talk to us about region-aware inference for your stack: https://www.decentralgpt.org/.