AI BriefingFuriosaAI: The Future of AI Lies in Efficient Inference
Key point
As the AI industry shifts from training to inference, the importance of efficient inference computing is growing.
Details
The AI industry is currently undergoing a rapid shift centered on Inference Compute. As model efficiency increases, resource consumption paradoxically rises—a phenomenon known as the Jevons paradox—and computational demand at the inference stage is surging due to agentic AI, multimodal AI, RAG, and more.
Additionally, the massive power consumption of existing GPUs has become a major bottleneck hindering data center scalability. This trend signals the arrival of the 'era of inference,' shifting focus from Training to Inference for actual service operations.
To address these challenges, FuriosaAI is designing its RNGD chip with a focus on three core values:
- Performance: Securing high throughput and low latency, along with memory bandwidth capable of accommodating large-scale parameters
- Programmability: Providing versatility to handle diverse models and use cases through hardware-software co-design
- Power Efficiency: Achieving sustainable power efficiency suited for data center infrastructure
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