At its recent Cloud Next conference, Google introduced Ironwood, the seventh generation in its Tensor Processing Unit (TPU) lineup.
Highlights
Unlike previous iterations, Ironwood is the company’s first TPU designed exclusively for inference—the process of running AI models post-training. This design marks a strategic shift in Google’s AI hardware focus as demand for low-latency, high-efficiency inference grows.
Cluster Configurations Targeting Scale and Flexibility
Ironwood will be deployed later this year for Google Cloud customers, available in two primary cluster sizes:
- 256-chip configuration for medium-scale workloads
- 9,216-chip configuration for high-scale, production-level AI services
These setups aim to address a variety of cloud deployment needs, from development environments to full-scale enterprise applications.
Performance and Hardware Specifications
Each Ironwood chip delivers 4,614 teraflops (TFLOPs) of peak compute performance, as per Google’s internal testing. It features:
- 192GB of dedicated memory
- Bandwidth speeds up to 7.4 terabits per second
These specs are intended to support resource-intensive AI inference tasks such as:
- Real-time recommendation engines
- Ranking systems
- Generative AI applications requiring rapid response
SparseCore and Power Efficiency
Ironwood introduces a new core component called SparseCore, which is optimized for data-heavy workloads. It is particularly suited for applications that demand rapid processing of personalized content—like product suggestions or social media feed generation.
The architecture has also been refined to minimize on-chip data movement, helping to reduce latency and improve power efficiency across inference tasks.
Performance Leap from Trillium TPU
Ironwood demonstrates a tenfold performance increase over Google’s previous-generation Trillium TPU, highlighting a significant jump in hardware capabilities. This advancement aligns with the increasing complexity and resource demands of contemporary AI workloads.
Focus on Energy Efficiency
Google reports that Ironwood achieves twice the performance per watt compared to Trillium, emphasizing energy efficiency alongside performance. This focus aligns with ongoing efforts to reduce the environmental impact of large-scale data center operations.
Expanding Cloud Infrastructure
Ironwood’s launch occurs within a broader trend of cloud providers investing in custom AI hardware. Google’s new TPU joins a growing field of proprietary chips from major players:
- Amazon’s Trainium and Inferentia (AWS)
- Microsoft’s Cobalt 100 (Azure)
These moves reflect a wider shift toward in-house silicon development aimed at improving integration, performance, and cost control within cloud platforms.
Google also plans to integrate Ironwood into its AI Hypercomputer architecture, a modular supercomputing platform that underpins many of its AI services.
This integration is expected to enhance deployment flexibility and accelerate model execution for enterprise clients.
Collaboration with NVIDIA
In addition to its proprietary TPUs, Google has confirmed plans to support NVIDIA’s upcoming Vera Rubin accelerators within its cloud ecosystem.
This dual strategy allows customers to choose between custom Google silicon and third-party hardware based on workload demands, broadening the range of supported AI use cases.
Strategic Emphasis on the Inference Era
Amin Vahdat, VP at Google Cloud, described Ironwood as a key development in the “age of inference,” citing its combination of compute power, memory capacity, network performance, and operational reliability.
The TPU adds a new layer to Google’s chip strategy, aiming to make inference as scalable and efficient as model training—particularly relevant for businesses deploying AI in real-world environments.
