Google has unveiled its "7th-generation" custom AI accelerator, the Ironwood, which is the first in-house chip to be specifically designed for inference workloads.
Google's Latest Ironwood AI Chip Has Created a New Performance Benchmark, Targeting Inference Workloads
Announced at Google Cloud Next 25, Ironwood is the firm's most powerful and efficient accelerator to date. It comes with several improvements in generational capabilities, which make it an ideal candidate for inference workloads, an area that Google believes to be the next "phase of AI." The accelerator is set to be offered to Google Cloud customers, reportedly in two different configurations: a 256-chip configuration and a 9,216-chip configuration, which are to be chosen to depend upon the workload and inference power needed.
The next part is what makes Google's Ironwood a revolution for modern-day AI markets. It is claimed that under the 9,216-chip configuration, the firm achieves 24 times the computing power of the world’s largest supercomputer, El Capitan, achieving 42.5 Exaflops. Apart from this, Ironwood is said to run 2x higher in perf/watt relative to the previous-gen Trillium TPU, which shows that the scaling of performance with each generation has been tremendous. Here are some other interesting facts about Ironwood:
- Substantial increase in High Bandwidth Memory (HBM) capacity. Ironwood offers 192 GB per chip, 6x that of Trillium, which enables processing of larger models and datasets, reducing the need for frequent data transfers and improving performance.
- Dramatically improved HBM bandwidth, reaching 7.2 TBps per chip, 4.5x of Trillium’s. This high bandwidth ensures rapid data access, crucial for memory-intensive workloads common in modern AI.
- Enhanced Inter-Chip Interconnect (ICI) bandwidth. This has been increased to 1.2 Tbps bidirectional, 1.5x of Trillium’s, enabling faster communication between chips, facilitating efficient distributed training and inference at scale.
Ironwood and Google's achievements show how far custom in-house AI solutions have grown, and it is safe to say that this does challenge NVIDIA's monopoly over the markets, which Jensen already knows. Such performance figures clearly indicate that there's always room to grow, and with solutions popping up from the likes of Microsoft with Maia 100, and Amazon with Graviton chips, it is clear that firms have recognized the opportunities presented by in-house solutions.
About the author: Muhammad Zuhair is a hardware and technology reporter for Wccftech, specializing in the semiconductor industry and the complex interplay between technology, manufacturing, and geopolitics. His coverage focuses on the corporate strategies and technological roadmaps of industry giants like TSMC, NVIDIA, Samsung, and Intel. Zuhair's expertise lies in deconstructing complex topics such as fabrication nodes (e.g., 2nm process), the economic impact of policies like the CHIPS Act, and the strategic development of AI infrastructure from NVIDIA, AMD and Intel.
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