Google Cloud Ironwood TPUs and Axion VMs: The Next Generation of AI Infrastructure
The race to build high-performance AI infrastructure is accelerating, and Google Cloud has just made a major move. With the introduction of Ironwood TPUs (its seventh-generation Google Cloud TPU) and new Axion-based VMs, Google is doubling down on custom silicon to power the next wave of machine learning workloads.
Ironwood TPUs are purpose-built accelerators for large-scale AI training and inference, while Axion VMs bring highly efficient Arm-based compute to general-purpose cloud workloads. Together, they are designed for everyone from AI researchers and cloud architects to developers and business leaders who want serious cloud AI performance.
Why Ironwood TPUs and Axion VMs Matter Right Now
We’re entering what many call the age of inference — a world where large models don’t just generate outputs, but act as agents, interact with tools and systems, and run continuously inside products.
To support this, cloud infrastructure needs two critical ingredients:
- Specialized accelerators (like TPUs) to handle the heavy math in AI training and real-time inference.
- Efficient general-purpose compute (like Axion Arm CPUs) to power data pipelines, microservices, APIs, control logic, and orchestration around those models.
Google Cloud’s Ironwood TPUs and Axion-based VMs are designed exactly for this combination, with a system-level approach that tightly couples hardware, networks, and software.
Inside Ironwood TPUs: Google’s Most Powerful AI Accelerator Yet
Ironwood is Google Cloud’s seventh-generation Tensor Processing Unit (TPU), built from the ground up for modern deep learning. It’s engineered to train and serve massive models with extremely low latency, making it ideal for demanding AI and machine learning workloads.
Key Performance Highlights
- Up to 10× higher peak performance than TPU v5p.
- Over 4× performance per chip compared to TPU v6e.
- A single Ironwood pod can scale to 9,216 chips, delivering exascale compute for large model training and inference.
- Each chip is optimized for FP8 compute, ideal for large-scale AI workloads.
Memory & Bandwidth
- High-bandwidth memory per chip, dramatically larger than earlier TPU generations.
- Multi-terabyte per second memory bandwidth, reducing bottlenecks for large language models and multimodal architectures.
- A high-speed, custom interconnect topology to keep thousands of TPU chips communicating with low latency during distributed training.
Ironwood TPUs are also backed by advanced liquid cooling in Google’s data centers, enabling high performance and energy efficiency at massive scale. For customers, that translates into:
- Faster model training and iteration cycles.
- Lower cost for large-scale inference.
- The ability to tackle larger and more complex models than before.
In practice, Ironwood lets AI teams move from “we can’t afford to train this” to “we can ship this model into production.”
Real-World Use: From Foundation Models to Agentic AI
Ironwood TPUs are aimed squarely at workloads such as:
- Training and fine-tuning large language models (LLMs) and multimodal models.
- High-throughput, low-latency inference for chatbots, copilots, and AI agents.
- Complex recommendation systems and personalization engines.
- Enterprise AI workloads that require predictable performance at scale.
Leading AI companies and research teams can use Ironwood to shorten training times, experiment with bigger architectures, and support millions of users with responsive, always-on AI services.
Axion-Based VMs: Efficient Arm Compute for AI-First Cloud Workloads
While TPUs handle matrix-heavy AI computations, most of an AI system runs on CPUs: APIs, business logic, data engineering, microservices, feature stores, orchestration, and more. This is where Google’s new Axion platform comes in.
Axion is Google’s custom implementation of Arm® Neoverse™ CPU technology, designed for high performance and excellent price-efficiency. Axion-based VMs give customers a drop-in option to run general workloads more efficiently, without sacrificing speed.
N4A VMs (Preview): Price–Performance Optimized
The new N4A family of Axion VMs targets a wide range of cloud workloads:
- Microservices and containerized applications.
- Web backends and application servers.
- Data processing and analytics pipelines.
- CI/CD, build systems, and development environments.
From a business perspective, N4A is especially interesting because it offers:
- Up to ~2× better price–performance compared to equivalent x86-based VMs (depending on workload and configuration).
- Support for modern memory (e.g., DDR5) and strong networking bandwidth.
- Support for custom machine types and modern storage like Hyperdisk.
C4A Metal (Coming Soon): Bare-Metal Arm for Specialized Needs
For teams that require direct hardware access, Google is also introducing C4A Metal:
- Bare-metal Arm instances with access to the full CPU without a hypervisor layer.
- Ideal for performance-sensitive or license-constrained software stacks.
- Useful in scenarios such as Android build/test farms, automotive workloads, or other Arm-native environments.
This is especially valuable if you must run your stack on native Arm hardware for correctness, performance, or compliance reasons, but still want the elasticity and global reach of Google Cloud.
How Ironwood TPUs and Axion VMs Work Together
The real power of these announcements is in their combination. Google’s approach is to design the entire stack — accelerators, CPUs, network, storage, and software — as one integrated AI infrastructure platform.
A typical modern AI system on Google Cloud might look like this:
-
Ironwood TPUs for:
- Training large models and fine-tuning them on domain-specific data.
- Running high-throughput, low-latency inference for production traffic.
-
Axion VMs (N4A, C4A) for:
- Data ingestion, preprocessing, feature engineering, and analytics.
- Hosting APIs, backends, and microservices that orchestrate AI logic.
- Running agents, tools, and business workflows that call into models on TPUs.
This design mirrors how real AI products behave: only a fraction of the compute is in the model itself. The rest is in the ecosystem around the model — and that’s where Axion VMs shine with better price–performance and energy efficiency.
Example Architecture Pattern
Consider a company building an AI copilot for developers:
- Ironwood TPUs train and host the large language model that generates suggestions and explanations.
-
Axion N4A VMs run:
- API gateways and authentication services.
- Code parsing and static analysis tools.
- Usage tracking, analytics, and billing pipelines.
The result: blazing-fast inference, efficient infrastructure spend, and a scalable foundation for millions of users.
What This Means for AI Teams and Businesses
For AI researchers, developers, and cloud architects, Ironwood TPUs and Axion VMs unlock new possibilities:
- Bigger experiments, faster training: Ironwood’s performance lets you train larger models and iterate more quickly, which is crucial in frontier research and applied ML.
- Production-ready performance: The combination of high-throughput TPUs and efficient Axion VMs helps sustain demanding inference workloads without runaway costs.
- Optimized TCO (Total Cost of Ownership): Axion’s improved price–performance means you can move more of your “non-accelerator” workloads to a more efficient CPU platform.
- Future-proof infrastructure: Investing in custom silicon like TPUs and Axion positions your stack for the next generation of agentic AI, multimodal models, and real-time applications.
Getting Started with Ironwood TPUs and Axion VMs
If you’re already on Google Cloud, there are a few practical steps you can plan for:
- Audit your workloads: Identify which parts of your stack are AI training/inference and which are general-purpose compute. This helps you map TPUs vs. Axion VMs.
- Benchmark key workloads: Test representative training jobs and inference workflows on Ironwood TPUs when available. Compare them to existing GPU or TPU generations.
- Port services to Axion: Start with microservices and stateless workloads that run well on Arm. Many modern runtimes (Java, Go, Node.js, Python, etc.) already support Arm seamlessly.
- Design for scale: Think in terms of an AI platform: TPUs for heavy math, Axion for everything else, all connected via modern networking, load balancing, and observability.
For more technical details, you can refer to the official Google Cloud documentation and the original product announcement on the Google Cloud Blog.
Conclusion: A New Baseline for Cloud AI Performance
Google Cloud’s Ironwood TPUs and Axion-based VMs mark a significant leap forward in AI infrastructure. Ironwood TPUs deliver a massive boost in accelerator performance and energy efficiency for machine learning workloads, while Axion VMs give teams a powerful, cost-optimized CPU platform for the rest of their stack.
For organizations serious about cloud AI performance, this combination offers a flexible, future-ready foundation. Whether you’re training cutting-edge models, deploying AI agents at scale, or simply trying to reduce your infrastructure bill without sacrificing performance, Ironwood and Axion are technologies worth paying close attention to.
If you’re planning your next AI project on Google Cloud, consider this your new starting point: TPUs for the brains, Axion for the backbone — and a tightly integrated platform designed for the era of intelligent, always-on AI.
0 Comments