Super Efficient AI
AI systems that are up to 100x more costs- and energy-efficient while delivering capabilities required by emerging and economically significant workloads.
The AI efficiency gap
Today’s global AI investments are optimized for performance, scale, and reliability, while energy consumption, emissions, hardware efficiency, and infrastructure stress are treated as secondary concerns. This misalignment hides the true cost of AI and locks systems into expensive, energy-intensive designs.
Current AI architectures leave significant room for efficiency-driven innovation.
SPARC’s Super-Efficient AI (SEAI) research leads this train of thought, reframing efficiency as a first-order design goal. Many economically important workloads, including planning, reasoning, and scientific simulation, can operate under relaxed latency and throughput requirements. This creates space for AI systems optimized for efficiency, distribution, and sustainability.
India is uniquely positioned to lead this shift, with a strong renewable energy base and a growing AI ecosystem. SEAI supports AI for science, strengthens national infrastructure, and positions India as a global source of efficient AI technologies and standards.
SEAI reframes efficiency as a core design principle, not a secondary optimization – making AI viable, affordable, and sustainable at national scale.
Research focus
Efficient AI hardware
Design low-power accelerators and memory-centric architectures that reduce energy use without relying on cutting-edge fabrication.
Sustainable data center infrastructure
Develop climate-adaptive, modular and decentralized data centers aligned with renewable energy availability.
Efficient models and AI systems
Create lightweight models, efficient training methods, and resilient serving systems that reduce compute and redundancy.
Measurement and transparency
Build benchmarks and tools to make AI energy use, emissions, and system efficiency visible and comparable.