Google launches Ironwood AI chip for cloud computing

Google has announced Ironwood, its seventh-generation TPU AI accelerator chip, at the Cloud Next conference this week.

The chip is designed for running AI models and is expected to be available to Google Cloud customers later this year.

Ironwood comes in 256-chip and 9,216-chip clusters, offering up to 4,614 TFLOPs and 192GB RAM per chip with 7.4 Tbps bandwidth.

It features an upgraded SparseCore to boost tasks like recommendations by reducing latency and improving energy efficiency.

The chip will be integrated with Google’s AI Hypercomputer as part of its broader cloud infrastructure strategy.

While Google has not provided a specific launch date for Ironwood, it has confirmed that the chip will debut later this year.

🔗 Source: TechCrunch

🧠 Food for thought

1️⃣ Google’s decade-long custom chip strategy reaches maturity

Google’s TPU journey represents one of the longest-running custom silicon projects in AI, spanning nearly a decade since internal development began in 2015 1.

The evolution from the first-generation TPU to Ironwood demonstrates Google’s methodical approach to chip development, with each generation addressing specific limitations of the previous one—from the first TPU’s inference-only capabilities to later versions supporting both training and inference 2.

This seventh-generation TPU marks a significant milestone as Google’s first chip specifically optimized for running AI models rather than training them, reflecting how the industry is shifting focus from developing models to deploying them at scale 3.

The performance leap is substantial. Ironwood’s 9,216-chip configuration delivers 42.5 exaflops of computational power, theoretically surpassing the world’s largest supercomputers in AI-specific tasks 4.

This progression illustrates how specialized AI hardware development typically requires longer timelines than software development but can ultimately deliver substantial competitive advantages through purpose-built architectures.

2️⃣ The high-stakes race for AI inference dominance

Ironwood’s focus on inference rather than training highlights an important market shift. While training gets attention, inference (running models) represents the bulk of actual compute usage and cost in production AI systems 5.