Indosat introduced a commercial AI-driven radio access network (AI-RAN) rollout, featuring shared GPU clusters for both radio processing and AI workloads, developed in partnership with Nokia and NVIDIA. The deployment moved beyond lab tests after a live demonstration that ran the region’s first AI-driven 5G call and showed ultra-low latency control of a remote robot.
The operator validated a shared-hardware approach and built a Surabaya facility to train local talent, collaborating with universities to develop use cases in healthcare, education and agriculture. Indosat also joined an industry consortium with partners including SoftBank and T-Mobile US to share implementation lessons and development costs.
For operators and enterprise buyers, the rollout highlights a practical path to cut capex through hardware consolidation while unlocking edge AI services; the program also emphasizes skills development and cross-industry collaboration as critical to scalable network modernization.
Image Credit: Indosat, NVIDIA
Why This Trend Is Growing
- Shared GPU Network Convergence
- Shared GPU clusters at the radio edge reduce hardware duplication and open possibilities for multi-tenant service models that blend real-time radio processing with AI workloads.
- Edge AI Services Expansion
- Localized ultra-low-latency compute at cell sites supports new latency-sensitive applications such as remote robotics and real-time medical imaging inference.
- Consortium-led Standardization
- Industry consortiums pooling implementation lessons and costs foster interoperable deployment patterns that could accelerate standardized AI-RAN platforms.
Industries Being Reshaped
- Telecommunications Operators
- Operators consolidating baseband and AI processing onto shared hardware stand to reshape network economics and offer differentiated edge compute-as-a-service propositions.
- Healthcare Providers
- Hospitals and clinics leveraging edge AI in partnership with networks could access real-time diagnostics and remote procedure control with minimized latency impact.
- Agricultural Technology
- Farm-focused AI services running at regional edge sites enable high-frequency sensor analysis and autonomous equipment coordination that bypass cloud delays.
