VENDORiQ: Google Ironwood: A driver for Cost Efficiency in AI?

Why it’s Important

Google’s unveiling of the Ironwood TPU directly addresses the burgeoning costs and computational demands of deploying sophisticated AI models at scale. This focus on inference cost and efficiency is not unique to Google; it reflects a critical industry-wide challenge. Leading hardware provider NVIDIA continually emphasises performance-per-watt gains in its GPU architectures (like Blackwell) and promotes accelerated computing combined with software optimisation as key to sustainable AI scaling. Similarly, other major cloud providers are investing heavily in custom silicon to tackle these issues. AWS offers its Trainium chips optimised for cost-effective training and inference, and Inferentia chips for deep learning and generative AI inference, claiming significant cost reductions and energy savings compared to traditional GPUs for specific workloads. Microsoft is also developing its own silicon for Azure with the Maia AI accelerator’s software-led approach to data utilisation and power efficiency and the energy-efficient Arm-based Cobalt CPU, aiming to optimise performance and cost within its data centres while still partnering with NVIDIA and AMD.

Against this competitive backdrop, Ironwood’s explicit focus on inference performance and, crucially, power efficiency (claiming approx. 30x improvement over its 2018 Cloud TPU) underscores Google’s recognition that making AI economically viable in production is paramount. 

This aligns with the initial analysis that improving the cost-performance ratio is critical, especially as many AI services still operate at a loss. The substantial improvements in compute power, memory capacity, and interconnect bandwidth are clearly aimed at handling the next generation of complex models, such as large language models (LLMs), mixture-of-experts (MoEs), and multi-step ‘reasoning’ or ‘agentic’ AI systems, which Google terms ‘thinking models’.

For Google, Ironwood represents a vital component in maintaining competitiveness. By controlling the chip design and integrating it tightly with its cloud platform, Google aims to offer performance and efficiency advantages, particularly for its own AI models like Gemini, run within its ecosystem. 

If the claimed performance and efficiency gains hold true in real-world scenarios, Ironwood could significantly lower the barrier for organisations deploying large-scale AI, potentially making previously cost-prohibitive applications feasible on Google Cloud, positioning it strongly amidst fierce competition focused on the same cost and energy bottlenecks. More importantly, this type of advancement signals that we may be approaching the point where the current fees of generative AI become sustainable.

Who’s Impacted?

• AI Teams (Data Scientists, ML Engineers): Plan for more advanced AI applications that leverage reasoning models and for a sharp increase in volume of AI requests as industrial agentic AI takes hold. This, in turn, requires a close working relationship with cloud architects to select the most cost-effective platform for each AI application.
• Cloud Architects: Evaluate the costs of strategic AI initiatives being hosted on different cloud platforms, comparing the costs of running inference as distinct from training or running a proof of concept (PoC).  

Next Steps

• Monitor Benchmarks: Look for independent benchmarks and real-world performance data as Ironwood becomes available on Google Cloud to validate the claimed performance and efficiency gains against alternative solutions (e.g., NVIDIA GPUs, AWS Trainium/Inferentia, Azure Maia).
• Engage with Cloud Providers: For organisations heavily invested in or considering cloud platforms for large AI projects, discuss potential access, pricing models, and integration best practices for optimised silicon (Ironwood on GCP, Trainium/Inferentia on AWS, Maia on Azure) with respective representatives.
• Contract Flexibility: Looking to the horizon, maintain operational options to flexibly switch platforms without hold-up or financial penalty.