Google's latest chip innovation marks a significant shift in the company's approach to artificial intelligence computing, focusing on the growing demands of AI inference rather than just training. The tech giant has recognized that as AI models evolve toward reasoning capabilities, the computational costs are increasingly shifting from development to deployment.
The Shift to Inference Computing
At its Google Cloud Next 25 event, Google unveiled Ironwood, its 7th-generation Tensor Processing Unit (TPU). Unlike previous generations that were positioned primarily for AI training workloads, Ironwood represents a strategic pivot toward inference - the process of making predictions from trained AI models in response to user requests. This shift acknowledges an economic inflection point in artificial intelligence, where the industry is moving from experimental research projects toward practical, widespread implementation of AI models by businesses.
Technical Advancements
Ironwood delivers impressive technical improvements over its predecessor, the 6th-generation Trillium TPU. Google claims the new chip achieves twice the performance per watt, delivering 29.3 trillion floating-point operations per second. Memory capacity has been dramatically increased to 192GB of high-bandwidth memory (HBM) per chip - six times more than Trillium. Additionally, memory bandwidth has been boosted 4.5 times to reach 7.2 terabits per second, enabling much greater data movement both within the chip and between systems.
Ironwood TPU Specifications vs. Previous Generation (Trillium)
| Feature | Ironwood (7th Gen) | Trillium (6th Gen) | Improvement |
|---|---|---|---|
| Performance per watt | 29.3 TFLOPS | ~14.65 TFLOPS | 2x |
| HBM Memory capacity | 192GB per chip | 32GB per chip | 6x |
| Memory bandwidth | 7.2 TBps | 1.6 TBps | 4.5x |
| Peak compute per chip | 4,614 TFLOPs | Not specified | - |
| Maximum scaling | 9,216 chips per pod | "hundreds of thousands" | - |
| Total compute at scale | 42.5 exaflops | Not specified | - |
Scaling Capabilities
Perhaps most impressive is Ironwood's scaling capability. The TPU can scale up to 9,216 chips per pod, delivering a staggering 42.5 exaflops of computing power. To put this in perspective, Google notes this is more than 24 times the compute power of El Capitan, currently the world's largest supercomputer. This massive scaling potential is further enhanced by Google's DeepMind-designed Pathways software stack, which allows developers to harness tens of thousands of Ironwood TPUs working in concert.
Economic Implications
The timing of Ironwood's release is particularly significant given the escalating costs of AI infrastructure. Wall Street analysts have increasingly focused on the enormous expenses associated with building and deploying AI systems, especially as models like Google's Gemini move toward reasoning capabilities that dramatically increase computational demands. By developing its own high-performance inference chips, Google may be able to reduce its dependence on vendors like Nvidia, AMD, and Intel, potentially saving billions in infrastructure costs.
Market Positioning
While Google has developed TPUs for over a decade through six previous generations, the explicit positioning of Ironwood as an inference-first chip represents a departure from past approaches. Previously, Google had described TPUs as necessary investments for cutting-edge research but not alternatives to chips from established vendors. The inference market is considered high-volume in the chip world, as it must meet the needs of thousands or millions of customers requiring day-to-day predictions from trained neural networks.
Software Developments
Alongside the hardware announcement, Google also revealed it's making its Pathways software available to the public through Pathways on Cloud. This software distributes AI computing workloads across different computers, potentially allowing customers to achieve greater efficiency and utilization of their AI resources.
The Future of AI Computing
With Ironwood, Google is positioning itself at the forefront of what it sees as the next generation of AI computing - moving from responsive models that simply present information to proactive systems capable of interpretation and inference. As Amin Vahdat, VP/GM of ML, Systems & Cloud AI at Google, stated, Ironwood is purpose-built to power thinking, inferential AI models at scale, signaling Google's vision for more sophisticated AI applications that can reason through complex problems rather than simply responding to prompts.