Research Finder
Find by Keyword
Vertical Integration: Google's AI Compute Edge?
Upcoming general availability of Ironwood TPUs and expanded Axion VMs highlights Google’s ongoing commitment to custom silicon and vertical stack optimization
Key Highlights:
Ironwood, Google’s 7th-gen TPU originally teased in April, aims to deliver substantial performance gains for high-volume AI inference.
The new Axion N4A VMs are architected to provide up to 2x better price-performance versus comparable current-generation x86 instances.
The introduction of the C4A metal bare metal instance is designed to support specialized, latency-critical Arm-native workloads.
Google is leveraging its proprietary AI Hypercomputer architecture to maximize efficiency across its hardware and software stack.
This dual announcement illustrates the necessity for both specialized AI acceleration and cost-optimized general-purpose compute.
The News
Google Cloud is announcing the general availability of its seventh-generation TPU (Tensor Processing Unit) called Ironwood, alongside expanding its Arm-based processor family with the N4A virtual machine and the C4A metal bare-metal instance, both in preview. This move solidifies the company’s focus on vertically integrated, custom silicon across the data center stack, adding to options addressing surging demand for AI inference efficiency alongside and cost-optimized general compute. These new offerings are presented as foundational components of Google Cloud’s AI Hypercomputer architecture.
find out more (https://cloud.google.com/blog/products/compute/ironwood-tpus-and-new-axion-based-vms-for-your-ai-workloads).
Analyst Take
This is a comprehensive infrastructure play that could position Google against not only Hyperscalers, but also present an evolving (if limited release) alternative to NVIDIA’s Blackwell. So look at this announcement as more than a refresh of the hyperscaler’s compute portfolio but as an evolving commitment to vertical integration, which is arguably the fundamental competitive differentiator in generative AI. The industry is at the inflection point where relying solely on commodity processors can’t work as the going-forward strategy for hyperscalers who must manage vast scale while simultaneously driving down energy consumption and broader total cost of ownership. Google’s strategy is clear: purpose-built hardware, co-designed software, thus offering superior performance per dollar and performance per watt. It's the same strategy others are using, but Google is one of the companies with the resources to make it work.
The core of this dual-release aims at the fundamental schism affecting cloud workloads: specialized acceleration for AI models, and efficient, flexible processors for everything else. ‘Everything else’ meaning the data prep, microservices, web serving, and orchestration that makes AI useful. Google is attacking both sides simultaneously with Ironwood and Axion. This duality serves customers building agentic workflows, requiring tight coordination between complex neural network inference (best suited for Ironwood) and the logic and orchestration layers (best suited for Axion).
What was Announced
The Ironwood TPU, Google’s AI accelerator now in its seventh generation, is purpose-built silicon addressing the intense demands of large-scale model training and high-volume, low-latency AI inference. According to the company, the chip will deliver a 10x peak performance improvement when compared to the preceding TPU v5p generation. It is also claimed to provide more than 4x better performance per chip for both training and inference workloads compared to the TPU v6e (Trillium). Architecturally, Ironwood is designed to scale up to 9,216 chips within a single superpod, interconnected by a breakthrough Inter-Chip Interconnect (ICI) networking fabric operating at 9.6 Tb/s. This will allow the system to access a staggering 1.77 Petabytes of shared High Bandwidth Memory, delivering massive computational domains for running the largest foundational models. Early market analysis suggests Ironwood’s inference performance profile positions it competitively against offerings like the Nvidia B200, leveraging a single primary compute die design that may confer a significant cost advantage for internal deployment. Furthermore, the hardware tightly integrates into the AI Hypercomputer platform, and therefore can deliver improved efficiency through software layers like the Cluster Director capabilities in Google Kubernetes Engine (for intelligent scheduling) and the GKE Inference Gateway (to reduce time-to-first-token latency by up to 96 percent.)
On the general-purpose side, Google is expanding its custom Arm-based Axion processor family. The new N4A virtual machine series is currently in preview and is engineered to serve as the most cost-effective N-series offering over previous Intel-based. N4A aims to deliver up to 2x better price-performance than comparable current-generation x86-based VMs. Early customer tests reporting compelling gains for workloads like video transcoding and data processing pipelines. These instances are built on the Arm Neoverse N3 compute core. The announced N4A machines will support up to 64 vCPUs, 512GB of DDR5 Memory, and 50 Gbps networking, alongside support for Custom Machine Types for granular configuration. At the same time, Google is announcing the forthcoming C4A metal, their first Arm-based bare metal instance, which is designed for specialized applications. C4A metal has the potential for delivering direct access to the physical server’s 96 vCPUs and 768GB of DDR5 memory with up to 100Gbps networking. That directly targets use cases such as automotive software development, which require architectural parity between cloud development environments and in-car physical silicon.
This combined announcement is a direct challenge to both Intel/AMD in the x86 general compute space and Nvidia in the high-end accelerator market. A substantial two-fer. Google is betting that its ability to co-design the full stack, silicon to software framework, gives it the edge over multi-party component aggregation. The performance gains claimed are substantial - with the key differentiating factor being the level of integration: everything from the Ironwood chip to the Jupiter data center network and the Titanium offload silicon is designed under one roof, providing efficiencies few rivals can match. This holistic approach is essential for scaling AI workloads sustainably - and is also a key reason why access to the technology outside Googles cloud offerings is not in the cards.
Looking Ahead
Based on what I see, proliferating custom silicon among hyperscalers continues the reshaping of the semiconductor industry, while also fundamentally redefining cloud competition. My perspective is that Google’s dual focus - Ironwood for AI maximization and Axion for TCO minimization - is a rational and necessary response to market dynamics. The key trend that I am going to be looking out for is the adoption rate of N4A. The custom Arm CPU race, initiated years ago by AWS with Graviton, is moving deeper into its competitive phase. Early benchmarks suggest Google’s Axion instances are highly competitive against AWS’s Graviton 4 offerings. N4A’s focus towards core workloads like Java applications and microservices, combined with its claimed price-performance advantages, is designed to compel broad migration away from traditional x86 workloads. This will be a slow burn, but the cost incentive is powerful.
When you look at the market as a whole, the announcement signals the final decline of the general-purpose cloud as we knew it. The future belongs to specialized compute. Specialized architectures and system-level optimization, driven by the need for power efficiency and performance density, are the defining characteristics of silicon in the middle of this decade. Going forward I am going to be closely monitoring how the company performs on building out the software ecosystem for Ironwood. While the technical specifications are impressive, the Achilles heel of the TPU program has always been developer portability compared to Nvidia’s entrenched CUDA ecosystem. The support for frameworks like vLLM is a pragmatic step, indicating a willingness to support the open-source AI community. HyperFRAME will be tracking how the company does in translating these hardware capabilities into developer velocity and sustained, competitive pricing in future quarters. Success rests not just on the 9.6 Tb/s ICI, but on making that speed easily consumable by the enterprise developer.
Stephen Sopko | Analyst-in-Residence – Semiconductors & Deep Tech
Stephen Sopko is an Analyst-in-Residence specializing in semiconductors and the deep technologies powering today’s innovation ecosystem. With decades of executive experience spanning Fortune 100, government, and startups, he provides actionable insights by connecting market trends and cutting-edge technologies to business outcomes.
Stephen’s expertise in analyzing the entire buyer’s journey, from technology acquisition to implementation, was refined during his tenure as co-founder and COO of Palisade Compliance, where he helped Fortune 500 clients optimize technology investments. His ability to identify opportunities at the intersection of semiconductors, emerging technologies, and enterprise needs makes him a sought-after advisor to stakeholders navigating complex decisions.