In the high-stakes race to dominate AI infrastructure, Google just rolled out a game-changer: Ironwood, its seventh-generation Tensor Processing Unit (TPU). Built for the AI age, Ironwood delivers more than 4x the performance of its predecessor, and it’s positioned to challenge Nvidia’s grip on AI compute head-on.
But this is not just another chip launch. Ironwood signals a deeper shift in how AI will be built, scaled, and delivered by tech giants who now want to own everything from software to silicon.
Let’s see what makes Google Ironwood such a big deal, how it compares to competitors like Nvidia, AMD, and Intel, and what this means for the future of AI infrastructure.
What Is Google Ironwood?
Ironwood is Google’s latest custom-built AI chip, a TPU (Tensor Processing Unit), purpose-engineered for machine learning and deep learning tasks. Where general-purpose GPUs like Nvidia’s H100 can do many things well, TPUs are laser-focused on neural network operations like matrix multiplication, making them ideal for:
- Training large language models (LLMs)
- Running real-time inference for chatbots and AI agents
- Powering cloud-scale applications across industries
The Ironwood TPU can scale up to 9,216 chips in a single pod, allowing seamless parallelism and minimizing communication bottlenecks, a common limitation in traditional GPU clusters. Despite its efficiency, it is cooled via liquid cooling to keep the temperatures down.
While Google has not disclosed raw TFLOPS yet, insiders suggest Ironwood outpaces TPU v4 (used in PaLM and Bard) by at least 4x, putting it squarely in the performance tier of Nvidia’s H100.
Where will Ironwood be used?
Ironwood is built for production-scale AI, not just lab experiments. Here are some real-world applications it’s expected to accelerate:
Large Language Models (LLMs)
Ironwood is designed to train and serve trillion-parameter models like Claude, Gemini, or open-source LLMs at unprecedented speed and cost efficiency.
Real-Time AI agents
It can support the inferencing needs of AI assistants, search agents, chatbots, and recommendation engines — delivering responses in milliseconds.
Healthcare & life sciences
In genomics, drug discovery, and imaging diagnostics, Ironwood’s scale makes it a powerful accelerator for model training and simulation tasks.
Scientific research & climate modeling
Whether simulating hurricane behavior or predicting molecular dynamics, Ironwood offers a level of throughput that makes complex modeling practical.
Enterprise AI via Google Cloud
Businesses using Vertex AI will be able to deploy models directly on Ironwood infrastructure, optimizing performance and cost under a single, vertically integrated platform.
Ironwood vs Nvidia H100
The most direct comparison, and the one Google intends, is with Nvidia’s H100 GPU, which currently dominates AI model training.
| Feature | Google Ironwood | Nvidia H100 |
|---|---|---|
| Type | TPU (AI-specific chip) | GPU (general-purpose) |
| Architecture | 7th-gen TPU (2025) | Hopper (2022) |
| Peak Performance (FP8) | ~600–700 TFLOPS (est.) | ~700 TFLOPS |
| Max Pod Size | 9,216 TPUs | 256 GPUs per DGX pod |
| Target Use Case | LLMs, inference, cloud AI | LLMs, HPC, AI research |
| Power Efficiency | Highly optimized | Higher draw at scale |
| Ecosystem Integration | Deep into Google Cloud stack | Widely supported across clouds |
Key Point: Ironwood offers greater scale per pod, tight integration with Google Cloud, and improved energy efficiency, making it a highly competitive alternative, especially for companies already in Google’s ecosystem.
How about AMD and Intel?
Ironwood’s main competitor is Nvidia, but AMD and Intel are also building dedicated AI hardware. Here’s how they stack up:
AMD MI300X
- AMD’s top AI chip, launched late 2023
- Designed for inference and HPC workloads
- Offers up to 192GB of HBM3 memory, great for massive models
- Runs on ROCm, AMD’s answer to CUDA, but adoption is still growing
Intel Gaudi 2 / Gaudi 3
- Developed by Intel’s Habana Labs
- Focused on cost-effective AI training and inference
- Gaudi 3 (launched 2024) is estimated to deliver 400+ TFLOPS FP8
- Primarily used in AWS and select enterprise clouds
| Feature | Ironwood | AMD MI300X | Intel Gaudi 3 |
|---|---|---|---|
| Launch Year | 2025 | 2023 | 2024 |
| Memory | TBD (likely HBM) | 192GB HBM3 | 128GB |
| Use Case Focus | LLMs, inference | HPC, AI inference | AI training / inference |
| Cloud Support | Google Cloud | Azure (limited) | AWS, OCI, GCP |
| Ecosystem Maturity | Very High | Moderate | Moderate |
Conclusion: AMD and Intel are gaining ground, but Ironwood and H100 remain the go-to chips for cutting-edge AI infrastructure today.
Google Ironwood’s market impact
Google’s ambitions are clear. It’s not just launching a chip, it is reinforcing its identity as a vertically integrated AI company.
Key highlights:
- Anthropic will use up to 1 million Ironwood TPUs to power Claude
- Google raised CapEx from $85B to $93B to support AI infra
- Q3 Cloud revenue reached $15.15 billion, up 34% YoY
- Ironwood will be fully available to customers in late 2025
Google’s CEO, Sundar Pichai, underscored the significance:
“We’re seeing a surge in demand for AI infrastructure… Ironwood is key to meeting that demand.”
This move also gives Google leverage as Nvidia GPUs remain in tight supply, offering customers a new high-performance option with potentially better cost-to-performance ratios.
Why this matters
Ironwood is more than a fast chip. It is a signal that Google wants to own the future of AI infrastructure, not rent it.
By controlling its own hardware, software, and cloud services, Google gains:
- Cost savings
- Faster iteration cycles
- More optimized performance
It also reduces its dependence on external chipmakers, a strategic move as AI models grow more complex and costly.
And for developers, startups, and enterprises? It means more choice, better performance per dollar, and access to hardware that’s purpose-built for the AI era. With its combination of scale, efficiency, and cloud-native integration, Ironwood is positioned to play a leading role in training the world’s largest models and running them in real time, from enterprise dashboards to voice assistants to scientific simulations.
