Google isn’t just joining the coveted AI chip race; it’s turning up the heat.
The Alphabet (GOOGL)-owned tech giant’s new Ironwood processor, its seventh-generation Tensor Processing Unit, is now available to a broader audience, a massive step in where the cloud wars are headed next.
Ironwood delivers nearly 10 times the throughput of TPU v5p and a whopping four times the performance of last year’s model, making it perhaps Google’s most powerful and efficient AI chip yet.
However, it isn’t just about raw speed; it’s about control. By designing its own silicon and pairing it seamlessly with its AI software, Google continues to cut costs while significantly boosting efficiency.
In a market that’s essentially defined by scale and silicon, Ironwood is Google’s newest weapon, and arguably its sharpest yet.
Google’s Ironwood puts custom silicon front and center
Google’s Ironwood TPU represents a full-on escalation in the powerful cloud arms race.
The latest generation of Google’s custom AI silicon delivers an incredible power leap, nearly 10 times the throughput of TPU v5p and four times the per-chip performance of last year’s version.
To put it plainly, it’s the fastest, smartest, and most efficient processor the tech giant has ever developed.
However, speed isn’t the only story.
Ironwood is designed with scale in mind, as thousands of chips could potentially link together into massive “superpods,” pushing data across a
9.6-terabit-per-second network while sharing nearly
1.8 petabytes of memory.
Additionally, liquid cooling and optical networking effectively keep the setup from overheating. Early users, including the likes of Anthropic, say Ironwood lets them do a lot more, faster, and for less.
Ironwood at a glance
- 10× the throughput of Google’s previous TPU generation
- 9,216-chip “superpod” network with 1.77 PB shared memory
- Advanced liquid cooling, along with optical switching for reliability
- Co-designed silicon and software for peak efficiency at lower costs
How Ironwood reshapes the cloud chip rivalry
Google is hogging all the spotlight with Ironwood, but it’s far from the only Big Tech play that’s betting big on custom silicon.
Across the cloud space, rivals continue developing their own potent chips, and in some cases, entire hardware ecosystems to gain speed, cut costs, and reduce dependency on Nvidia’s pricey GPUs.
So it’s a lot less about bragging rights and more about economics.
Though each company’s approach feels a little different, the goal is the same: to dominate the entire stack, from hardware to hyperscale.
How the other cloud chip players stack up:
- Amazon Web Services (AWS): Amazon’s popular cloud service utilizes Trainium (for training) and Inferentia (for inference) chips, offering major cost savings. AWS claims a nearly 50% cheaper model training than comparable GPU setups.
- Microsoft Azure: Under Project Athena, Microsoft’s Maia 100 accelerator is the tech giant’s ticket to silicon independence. It layers in-house hardware along with the potent Azure AI stack, cutting costs and reducing reliance on Nvidia.
- Meta: Not to be left behind, Meta has its own MTIA inference chip in production and is already testing out a training chip in powering its recommendation engines.
- Apple: The quiet pioneer. Its A-series and M-series chips show the payoff of vertical integration in efficiency, performance per watt, along with total control over its ecosystem.
Google boasts a full-stack edge, backed by cloud growth fueled by AI deals
Google’s raking in the moolah from its big bets on building custom chips, where it matters most.
Google Cloud sales are up an impressive 34% year over year last quarter, while operating margins have climbed 23.7%, up immensely from 17% a year earlier.
CEO Sundar Pichai credited the success to “substantial demand for our AI infrastructure products, including TPU-based solutions” as a key growth driver.
Also, the company isn’t shy about spending to keep that momentum alive, with capital expenditures or capex soaring to $91 billion for 2025, much of it being earmarked for AI-powered data centers.
That investment is already paying off in high-value wins.
Pichai says Google has signed over $1 billion in cloud contracts this year, more than in the previous two years combined, spearheaded by major partners such as Anthropic and Meta.
Google Cloud’s backlog hit $155 billion in Q3, up a superb 46% quarter-over-quarter, reflecting those eye-catching commitments.
Even Apple is negotiating a huge billion-dollar-a-year deal to use Google’s Gemini AI model to supercharge Siri.
So in many ways, Pichai’s pitch is simple: Google is “the only hyperscaler” offering a true full-stack AI platform, covering everything from custom chips like Ironwood to frontier models like Gemini.
