Every now and then, even if you’ve spent a lifetime in technology, something comes along that makes you feel like you’ve just crawled out of a cave clutching a sharpened stick. For me, the new Google Ironwood TPU was one of those moments – a specialised AI chip called a Tensor Processing Unit, designed specifically to crunch the enormous piles of numbers inside modern machine learning models.

How I Discovered I Was Suddenly Out of My Depth

I’ve spent most of my life around hardware and code. I started as an electronics engineer, working with RF, DSP, microprocessors, FPGAs and all the usual suspects. I’m comfortable with micro-architectures, instruction pipelines, I/O, and the maths behind correlation, pattern recognition and basic neural processing. These days I run a web business instead of designing boards, but the engineering mindset never really goes away.

So when I clicked on a video cheerfully titled something like “Google Ironwood TPU Explained”, I expected a pleasant technical update – a bit more parallelism here, a bit more memory there, the usual “now 30% faster!” story. Instead, I found myself realising, for the first time in a long time, that I was looking at a class of machine that simply didn’t exist when I last had my hands deep in hardware.

It wasn’t that the individual ideas were unfamiliar. Underneath the marketing, people were talking about matrix multiplies, systolic arrays, memory bandwidth, and power efficiency. All concepts I’ve met many times before in DSP and high-performance computing. But the scale of it – the number of processing units, the amount of on-package memory, the way thousands of these chips are lashed together into a single logical machine – was so far beyond the mental model I’d carried out of my engineering career that it genuinely stopped me in my tracks.

For a moment, I felt like a beginner again. Here I was, someone who once designed and debugged real hardware, now running a web and Remote Desktop Services business from Thornton, NSW, trying to wrap my head around a device that exists so companies like mine can use AI at a scale we’ll never directly see. I’m used to understanding the kit that underpins my work – servers, networks, virtual machines. The new Google Ironwood TPU made it clear that the ground has shifted under all of us.

That feeling – the sudden gap between “I know how computers work” and “I know how this works” – is why I’m writing this article. Not as a chip designer inside Google, but as an engineer-turned-business-owner trying to bridge the gap between traditional hardware knowledge and these new AI super-chips. Along the way, I’ll translate the jargon, connect Ironwood to the sort of services Sydney Business Web builds and manages, and, just as importantly, explain what any of this might mean for “Joe Average” who will never see a data centre, but will absolutely feel the impact of what runs inside it.

What on Earth Is a Tensor Processing Unit?

Before we get too excited about the new Google Ironwood TPU, it’s worth answering a very simple question: what is a Tensor Processing Unit in the first place?

In plain English, a Tensor Processing Unit (TPU) is a special kind of computer chip built to do one job incredibly well: crunch huge piles of numbers for AI and machine learning. Where a normal CPU is designed to do almost anything reasonably, and a GPU is designed to push lots of pixels and parallel maths, a TPU is designed from the ground up to run the maths inside neural networks.

If you strip away the jargon, most modern AI is just an enormous amount of fairly simple arithmetic – multiply, add, repeat. Those operations are applied over “tensors”, which is just a grand word for multi-dimensional arrays of numbers. You can think of a tensor as a stack of matrices: rows and columns, sometimes with extra dimensions on top. The TPU exists to chew through those matrices at terrifying speed.

A very rough way to think about it is like this:

• CPU (Central Processing Unit): The generalist. Great at running operating systems, handling logic, and doing a bit of everything. Lots of flexibility, less raw parallel maths.
• GPU (Graphics Processing Unit): The muscle. Originally built for graphics, but brilliant at doing the same operation on lots of data in parallel. That made GPUs the first big workhorse for AI training.
• TPU (Tensor Processing Unit): The specialist surgeon. A custom chip designed specifically for the matrix operations that power neural networks, with memory and data paths tuned so those operations never starve for data.

So when we talk about the new Google Ironwood TPU, we’re not talking about a slightly faster CPU or a “better GPU”. We’re talking about a chip whose entire reason for existence is to take the core AI workload – those tensor operations deep inside things like ChatGPT-style models, image recognition, recommendation systems and language translation – and run it as fast and as efficiently as physics, money and engineering will currently allow.

For Joe Average, that might still sound abstract, so here’s the practical punchline: every time you talk to an AI assistant, upload a photo that gets auto-tagged, use live translation, or get eerily good product recommendations, somewhere in the background there is a very large pile of linear algebra happening. TPUs are Google’s way of making that pile of maths cheap and fast enough to power products you can use on a modest monthly subscription instead of needing your own data centre.

In other words: a Tensor Processing Unit is what happens when someone looks at the maths behind AI, shrugs, and says, “Right, let’s build silicon that does only this, but on a planetary scale.”

From TPU v1 to Ironwood: Seven Generations in Ten Years

One of the things that struck me most about the new Google Ironwood TPU is how quickly it arrived. In my engineering days, major architectural shifts tended to span decades. With TPUs, Google has gone from the first internal chip to the seventh generation – Ironwood – in roughly ten years. That’s not an evolution so much as a sprint.

If you’re old enough to remember the early days of serious floating point processing, this isn’t the first time we’ve seen something like this. Back when CPUs struggled with floating point maths, along came the external floating point / maths coprocessor. You had your main CPU doing general work and a separate chip whose job was to chew through FP operations faster and more accurately. Eventually, process technology and integration caught up, the FPU moved onto the CPU die, and the separate coprocessor vanished from the board.

In a sense, TPUs are that idea coming back on a completely different scale. Once again, the general-purpose CPU isn’t efficient enough for a new class of workload – this time, the tensor maths inside AI models – so we build a dedicated accelerator. The difference is that instead of sitting in a socket next to your CPU, the TPU now lives in vast racks inside a data centre, connected by a custom high-speed fabric and presented to you over the cloud.

The story starts around 2015 with TPU v1. This first Tensor Processing Unit was built mainly to accelerate one job: running trained neural networks for things like language translation and image recognition inside Google’s own services. It focused on relatively low-precision integer maths, because that’s all the models really needed at the time, and it delivered a huge speed and efficiency bump compared with CPUs alone.

Then came TPU v2 and TPU v3, which most people outside Google first heard about when they were exposed as Cloud TPUs. These generations added support for floating-point formats designed for deep learning, along with much faster on-package memory (HBM) and bigger pods. In Google-speak, a pod is essentially a rack-scale cluster of TPU boards wired together so that, from the software’s point of view, it behaves like one enormous AI accelerator rather than a pile of separate chips.

With TPU v4, Google started to talk seriously about AI supercomputers. They scaled to thousands of chips in one pod, connected them with a very high-bandwidth fabric, and put serious effort into the cooling and power side. At this point we weren’t just talking about “fast chips” but about entire data centre-scale systems engineered around AI workloads.

The fifth and sixth generations – TPU v5e, v5p and then Trillium (v6e) – refined the idea. They covered a range of price–performance points (cheaper, more flexible TPUs for everyday workloads; high-end parts for large models), increased performance again, and improved memory bandwidth. This is where Google’s AI infrastructure really matured into what they now call their “AI Hypercomputer”: TPUs, specialised CPUs and networking all co-designed as one stack.

Which brings us to TPU v7, Ironwood. This is where the numbers become almost comical. Each chip delivers thousands of teraflops of low-precision AI compute, paired with 192 GB of ultra-fast HBM3e memory on-package and over 7 TB/s of bandwidth. Google then joins up to 9,216 of these chips into a single pod – really a full-blown AI supercomputer – with around 42.5 exaFLOPS of FP8 performance and about 1.77 petabytes of shared high-bandwidth memory available to the models running on it.

From my old-engineer perspective, it feels as if someone took the kind of maths accelerator or DSP we used to bolt onto a system, turned every knob to maximum, then replicated it thousands of times and wired the whole lot together as if it were one giant chip. That’s essentially what’s happened: the new Google Ironwood TPU is still “just” digital circuits doing huge numbers of multiplies and adds, plus the memory to hold the data and the connections to move it all around. But the scale is so far beyond what most of us ever worked with that it forces a rethink of what “a computer” even means.

All of this matters because the new Google Ironwood TPU isn’t just another benchmark figure in a press release. It’s the culmination of seven generations of iteration on a single idea: if AI is going to power search, translation, content generation, recommendations and countless other services for billions of people, we need hardware that is unapologetically built for the maths those systems run on – and we need a lot of it.

Ironwood in One Page: The Headline Numbers

So what makes the new Google Ironwood TPU so special, beyond the marketing? Let’s put the key numbers on the table, then translate them into something a normal human (or at least a busy business owner) can relate to.

At the level of a single chip, Ironwood delivers:

• Thousands of teraflops of AI compute – that’s trillions of operations per second, running low-precision maths that’s ideal for neural networks.
• 192 GB of HBM3e memory sitting right next to the chip, not out on a separate stick. This memory is incredibly fast and designed to feed data into the chip without creating bottlenecks.
• More than 7 terabytes per second of memory bandwidth. Imagine being able to read the entire contents of a high-end laptop’s RAM hundreds of times every second.

If we stopped there, Ironwood would already be an impressive bit of kit. But Google doesn’t use these chips one at a time. They wire them together into what they call a pod – a rack-scale machine made up of thousands of chips acting in concert. With Ironwood, a full pod can contain up to 9,216 TPUs working together.

When you add that up, you get headline figures around:

• About 42.5 exaFLOPS of FP8 AI performance – that’s 42.5 billion billion calculations per second in the low-precision format commonly used for modern AI models.
• Roughly 1.77 petabytes of high-bandwidth memory that models can use as if it were one giant pool, not thousands of little pockets.

Those numbers are so large they are basically meaningless on first reading, so here’s a gentler way to think about it. Picture a serious server in a data centre – the sort of machine that might run dozens of websites or virtual desktops. Now imagine not just one of those, but thousands, all stripped of everything except their mathematical muscles and welded together into a single, tightly coordinated calculator whose only job is to run AI models.

That’s what an Ironwood pod is. It’s not a chip you buy and plug into a box under your desk. It’s an industrial-scale AI engine that lives in Google’s data centres and is rented out, a fraction at a time, to whoever needs to train or run large models. Companies like mine, Sydney Business Web in Thornton NSW, will never “own” an Ironwood TPU, but we will absolutely use cloud services that run on top of it when we deploy AI-assisted tools alongside our Remote Desktop Services and web platforms for clients.

For Joe Average, the key point is this: chips like Ironwood are what make it realistic to have conversational AI, smart search, live translation, and other “clever” services available instantly when you click a button on your phone or inside a web app, without paying enterprise-software prices. The more efficient these AI super-chips become, the more ambitious and affordable the services built on top of them can be.

How Ironwood Actually Works (Without the Hype)

By now it’s clear that the new Google Ironwood TPU is very big and very fast, but that still leaves an obvious question: how does it actually work? Is this some completely new kind of computer, or is it just the same old ideas turned up to eleven?

The honest answer is: both.

At one level, Ironwood is still recognisably a computer in the classic sense. There is control logic, there are instructions, there is memory that holds data, and there are circuits that perform operations on that data. The spirit of the old von Neumann machine is still there: instructions and data flowing through a system that fetches, decodes and executes.

But if you zoom in on the parts that do the real work, the picture changes. In a traditional von Neumann machine, you have a relatively small number of very flexible arithmetic units, and you spend a lot of effort shuttling data back and forth between memory and those units. For most AI workloads, that becomes the bottleneck. You don’t run out of clever instructions – you run out of the ability to feed numbers into the maths engine fast enough.

The new Google Ironwood TPU attacks that problem head-on by rearranging the balance between “brains” and “muscle”. Instead of a few powerful, general-purpose cores, you get vast grids of simpler arithmetic units that are arranged so data can flow through them like water through a pipeline. Rather than asking one core to do many different things in sequence, Ironwood is built so many small units can perform the same kind of operation on huge blocks of data at once.

For the sort of maths you find in neural networks – basically repeated “multiply then add, multiply then add” across big matrices – this is ideal. The chip can keep tens of thousands of these tiny operations in flight at the same time, as long as the data keeps arriving in the right shape and at the right moment. That’s why so much of the Ironwood design revolves around its on-package memory and the connections between chips: feeding the beast.

Is the von Neumann Model Dead?

This is where the von Neumann question becomes interesting. Formally, the von Neumann model – a machine with memory plus a control unit that steps through instructions – is not “dead” at all. Somewhere on the board, there is still control logic orchestrating everything. What is changing is the assumption that one small cluster of cores will do all the heavy lifting.

For decades, we tried to make general-purpose CPUs do more work by turning the handle: higher clock speeds, bigger caches, more instruction-level cleverness, a few more cores. That approach ran into physical limits – power, heat, and the speed of moving data across a chip. GPUs cracked the next stage by saying, “Let’s have thousands of simple cores instead of a few very complex ones.” TPUs like Ironwood take that further by saying, “Let’s throw away generality as far as we reasonably can, and organise the hardware explicitly around the patterns of maths that neural networks use.”

So you can think of Ironwood as a hybrid world:

• The overall system still behaves like a big computer in the old sense – memory, instructions, scheduling, operating systems, compilers.
• The heart of the chip looks much more like a dataflow machine – data streaming through fixed structures designed for matrix operations, with the control logic mostly concerned with keeping the streams flowing.

From a philosophical point of view, that’s a quiet but important shift in the computing paradigm. Instead of asking, “How can I make one box do anything?” we’re now asking, “What specialised boxes do we need to do the really important things well enough and cheap enough at global scale?” The new Google Ironwood TPU is one very large, very specialised answer to that question for AI and machine learning.

For a business owner or a “Joe Average” user, the details of von Neumann versus dataflow might not matter day to day. But the consequences do. A world full of highly specialised chips means:

• AI services that feel instant and natural instead of slow and clunky.
• Costs that keep trending down as hardware becomes more efficient, making advanced tools affordable to smaller companies.
• New kinds of applications – from smarter search on your website, to AI-assisted workflows running inside Remote Desktop sessions – that simply weren’t practical when everything had to run on a handful of general-purpose cores.

At Sydney Business Web, when we look at the new Ironwood TPU, we don’t see a chip we’ll ever buy. We see the foundation for the next generation of cloud services we will hook into – from AI-powered support tools on our clients’ sites, to intelligent assistants running quietly in the background on their hosted Remote Desktop systems. The paradigm shift in hardware won’t show up on their invoices as “Ironwood usage”, but it will be there in the responsiveness, capabilities and pricing of the tools they rely on.

Training vs Inference: What Ironwood Is Really Built For

To understand why the new Google Ironwood TPU exists at all, you have to separate two phases of AI that often get muddled together in the public conversation: training and inference.

Training is the “go away and study” phase. This is where a model chews through mountains of data – text, images, audio, code – and gradually nudges billions or even trillions of internal weights into useful patterns. It’s like sending a bright but clueless student to boot camp with a library card and a stack of exams. Training runs can take days or weeks and use vast amounts of computing power. If they’re a bit slow, nobody outside the data centre notices.

Inference is the “answer the question now” phase. This is what happens when you type a prompt into an AI assistant, click “translate”, search your photos for “dog at the beach”, or hit an AI-powered feature inside a business app. The model is already trained; now it’s being asked to apply what it has learned to a specific input, right now, with a human waiting on the other end of a screen.

In the early days of large neural networks, most of the excitement was around training: “How big a model can we build?”, “How fast can we train it?”, “How good can we make it at translations or images?”. TPUs were a huge part of that story. But as these models have moved into everyday products, the real challenge has shifted. It’s no good having a brilliant model locked in a lab if you can’t serve its answers quickly and cheaply to millions of users.

That’s where the new Google Ironwood TPU really earns its keep. It can certainly be used for training, but it has been shaped very deliberately around high-volume, low-latency inference – the “answer my request right now” side of the equation. A single Ironwood pod can handle vast numbers of requests in parallel, keeping all those simple maths engines busy while still returning results fast enough to feel natural in an app or browser.

Remember the mental picture of a giant FFT machine – a fast Fourier transform (FFT) engine that crunches signals into their frequency components – or a systolic pipeline streaming data through many small stages? Training is like running that engine over the same mountain of data again and again until the model internalises the patterns. Inference is more like running many, many smaller jobs at once: thousands of users, each asking their own question, each requiring a different pathway through the model. Ironwood’s job is to keep the pipelines full for all of them without dropping the ball on latency or cost.

For “Joe Average”, this distinction might sound academic, but the consequences are very real:

• Faster, smoother AI tools: Chatbots that don’t pause and stutter, image generators that respond in seconds instead of minutes, live translation that feels almost like talking to a human interpreter.
• More complex features made practical: Things like AI-assisted coding, smart document search, or intelligent help inside business software become viable when the cost per query drops.
• Lower cost at the front end: The more efficiently companies like Google can run inference on Ironwood, the more they can bundle powerful AI into services that small and medium businesses can actually afford.

For a business like mine, Sydney Business Web in Thornton NSW, this is where Ironwood starts to matter. We’re not going to spin up our own model zoo and TPU pods, but we are very interested in reliable, affordable AI services we can wire into the systems we build – from websites to Remote Desktop environments. If a client wants AI-assisted support tools on their site, or background agents that help staff inside their hosted desktop sessions, those features ultimately depend on someone, somewhere, running inference at scale on hardware like Ironwood.

So while the headlines focus on exaFLOPS and petabytes, the practical story is simpler: training makes AI smart; inference makes AI useful. The new Google Ironwood TPU exists to ensure that second part can happen for millions of people at once, without the whole thing collapsing under its own weight.

Why Ironwood Matters to ‘Normal’ Humans (and Small Businesses)

By this point, it’s tempting to shrug and say, “All right, the new Google Ironwood TPU is a gigantic maths engine in a shed somewhere – so what?” If you never log into Google Cloud and you’re not designing chips for a living, why should you care? The answer is that Ironwood and its cousins are quietly shaping what’s possible in the software you use every day.

Let’s start with the ordinary user – the “Joe Average” who doesn’t care how many teraflops anything has, but does care whether things just work.

• Better conversations with machines: Large language models and chat-style assistants feel much more natural when they can run bigger, more capable models in less time. The new Google Ironwood TPU makes it cheaper and faster to host those models for millions of users at once.
• Richer voice and video tools: Live transcription, translation, noise removal and even “AI meeting summaries” all rely on heavy-duty neural networks. Faster, more efficient inference means these features can become standard in everyday apps instead of premium add-ons.
• Smarter search and recommendations: When you search your email, your photos, your files in the cloud – or even a shop’s product catalogue – AI can match meaning, not just keywords. That kind of semantic search and recommendation engine is exactly the workload chips like Ironwood are built for.
• More helpful copilots, less busywork: Code assistants, writing helpers, spreadsheet copilots and customer-service bots all live or die on speed and cost per query. If each request is expensive, nobody can afford to offer them broadly. Ironwood helps drive those costs down.

None of this will pop up on your screen as a message saying “Powered by the new Google Ironwood TPU”. You’ll just notice that things feel less clunky and more “magic” over time. Pages will suggest better answers. Tools will quietly automate steps you used to do by hand. Translation and transcription will feel good enough to trust in a live conversation.

For businesses, especially at the small and medium end of town, the story is similar but the stakes are higher. Powerful AI used to be the preserve of companies with deep pockets and in-house teams. As hardware like the new Google Ironwood TPU makes inference cheaper and more scalable, those same capabilities get packaged into cloud services that you can rent by the hour or by the feature.

• On websites: AI search that understands customer questions, content recommendations that make sense, and support bots that can handle the first line of enquiries 24/7 without annoying everyone.
• Inside business systems: Document search that actually finds things, automated drafting of routine emails and proposals, and smart helpers embedded into CRM or accounting tools.
• On Remote Desktops: For companies running most of their work inside hosted Windows desktops, AI can sit alongside staff all day – suggesting replies, summarising documents, flagging odd patterns – without needing powerful hardware in the office.

That’s exactly where a company like mine, Sydney Business Web in Thornton NSW, lives. We build and host websites, manage servers and provide Remote Desktop Services for clients who want solid, secure infrastructure without running their own data centre. We’re not about to install an Ironwood pod in the garage – but we are very interested in plugging our clients into AI-powered features that ride on top of it.

When a client asks, “Can we have a smarter search on our site?”, “Can our staff get automatic call notes and summaries inside their remote desktop?”, or “Can we triage support enquiries before a human ever sees them?”, the honest answer increasingly depends on what the cloud providers can do at scale. Hardware like the new Google Ironwood TPU is what turns those questions from expensive science projects into practical yes/no decisions for normal businesses.

In other words: Joe Average will meet Ironwood not as a chip, but as a slow drip of everyday miracles – better answers, faster tools, less friction. And small businesses will meet it in the form of services that let them punch above their weight, provided someone helps them pick and integrate the right ones.

A Caveman’s Conclusion: Staying Sane in the Age of ExaFLOPS

When I first watched a talk on the new Google Ironwood TPU, I genuinely felt like a caveman staring at a jet engine. I’ve spent a lifetime around electronics, code and networks, and yet here was a class of machine that simply didn’t exist when I last brandished a soldering iron in anger. It would be easy, at that point, to shrug and say “this is all beyond me now” and retreat into the familiar world of CPUs, RAM and the odd VPS.

But if you zoom out, this isn’t the first time we’ve seen a shift like this. In my generation, the arrival of affordable microprocessors and personal computers blew the doors open for small business. One box under a desk, plus a bit of memory and some code, was suddenly enough to build products, launch companies and create entirely new kinds of work. The hardware was important, but the real explosion happened in the services and systems built on top of it.

I suspect we’re seeing a similar pattern with AI and chips like Ironwood. Very few people will ever design a TPU; almost nobody will ever own an Ironwood pod. The value – and the jobs – are more likely to emerge one layer up, in the provision of services that use those pods:

• Designing and integrating AI-powered features into websites, business apps and internal tools.
• Adapting generic AI capabilities to specific industries – law, medicine, trades, hospitality, logistics.
• Providing the hosting, governance, security and support that make these tools safe to use in real businesses.

In other words, the centre of gravity has moved. In the microcomputer era, the magic was “I can own my own machine and write my own software.” In the TPU era, the magic is closer to “I can rent slivers of a planetary-scale machine and use them, indirectly, to solve real problems for real people.” That still leaves a great deal of room for human skill, judgement and craftsmanship – it just lives higher up the stack.

For me, as an engineer-turned-business-owner running Sydney Business Web in Thornton NSW, that’s actually reassuring. I don’t need to become an Ironwood architect. I need to understand enough to know what’s possible, what’s marketing fluff, and how to plug genuinely useful AI capabilities into the services we already provide: solid websites, managed hosting, and Remote Desktop environments where people actually do their work.

For Joe Average, the takeaway is simpler still. You don’t need to worship the hardware or memorise the acronyms. What matters is asking good, grounded questions: “Does this tool really help me?”, “Is my data safe?”, “What happens when it goes wrong?”, “Can I talk to a human if I need to?” The new Google Ironwood TPU will sit in the background, humming away in some distant data centre, while the rest of us focus on whether the things built on top of it genuinely improve our lives.

So yes, the gap between the world of transputers and the world of Ironwood is enormous. The numbers have gone from kilobytes to petabytes, from megaflops to exaflops. But the basic game is oddly familiar: new hardware appears, ambitious people build new layers of software and services on top, and small businesses find ways to turn that stack into something useful and human. In the end, the job isn’t to understand every transistor in the machine – it’s to use the machine well.

(For readers who’d like to dive deeper into some of the ideas mentioned here – Tensor Processing Units, fast Fourier transforms, and the history of parallel computing – I’ll add a short list of external reference links at the end of this article so you can explore at your own pace.)

This post is part of our continuing service of advanced technical solutions for developers and business owners.

Glossary of Terms

Tensor Processing Unit (TPU)

A specialised computer chip designed by Google to run the maths inside AI and machine learning models, especially the large matrix operations used in neural networks.

Google Ironwood TPU

The seventh-generation TPU (often called TPU v7), built for very large-scale AI workloads. It combines huge compute power, very fast memory and high-speed links into data-centre “pods”.

Tensor

A structured block of numbers – essentially a general term for scalars, vectors, matrices and higher-dimensional arrays. Modern AI models mostly operate on tensors.

Neural Network

A type of mathematical model loosely inspired by brain cells. It consists of layers of simple units (“neurons”) that transform input numbers into outputs, learning useful patterns from data during training.

Training (of an AI model)

The phase where an AI model learns from large amounts of data. Its internal parameters are adjusted over many passes so that it becomes good at tasks such as translation, image recognition or text generation.

Inference

The phase where a trained AI model is used to answer questions or perform tasks in real time – for example, replying to a chat query, translating speech, or generating an image.

Pod (TPU pod)

Google’s term for a rack-scale cluster of many TPU chips wired together so that, from the software’s point of view, they behave like one enormous AI accelerator.

HBM / HBM3e (High Bandwidth Memory)

A type of very fast memory mounted close to the processor on the same package. It provides much higher data throughput than normal DIMMs, which is vital for keeping large AI chips busy.

FLOPS / ExaFLOPS

FLOPS stands for “floating point operations per second” – a measure of how many maths operations a processor can perform. An exaFLOP is a billion billion (10¹⁸) operations per second.

FP8

A low-precision floating-point format using 8 bits. It is less accurate than traditional formats like FP32 but much faster and efficient for many AI workloads, allowing more operations per second.

Petabyte

A unit of digital storage equal to 1,000 terabytes (or roughly a million gigabytes). In the context of Ironwood, it refers to the total pool of high-bandwidth memory available across a full pod.

Systolic Pipeline / Systolic Array

A hardware structure where data flows through a regular grid of simple processing elements in waves, with each element doing a small piece of work. Ideal for repeated operations on large matrices.

Fast Fourier Transform (FFT)

An efficient algorithm for converting a signal from the time domain (how it changes over time) into the frequency domain (what frequencies it contains). Often implemented in dedicated hardware for speed.

von Neumann Architecture

The classic computer design model where instructions and data share a common memory, and a central processing unit fetches and executes those instructions step by step.

Remote Desktop Services

A way of running users’ desktops and applications on servers in a data centre, while they connect over the network. The heavy lifting happens on the server; the user sees a remote Windows (or other) desktop on their screen.

Further Reading

• Ironwood: The first Google TPU for the age of inference – Google’s overview of Ironwood as a TPU designed for the “age of inference”.
• Cloud Tensor Processing Units (TPUs) – Official Google Cloud page introducing TPUs, their use cases and basics.
• An in-depth look at Google’s first Tensor Processing Unit (TPU) – Historical and architectural background on the original TPU.
• Tensor Processing Unit – Wikipedia – Reference entry listing TPU generations, specs and history.
• Introducing Trillium, sixth-generation TPUs – Google Cloud blog post on TPU v6 “Trillium”, useful context before Ironwood.
• Systolic array – Wikipedia – Background on systolic arrays, the architectural idea behind many AI accelerators.
• Von Neumann architecture – Wikipedia – Classic computer architecture model and its limitations (including the von Neumann bottleneck).
• Guide to FFT Analysis (Fast Fourier Transform) – A practical, readable guide to FFTs and frequency-domain thinking.
• AI 101: A Guide to the Differences Between Training and Inference – Clear explanation of the training vs inference split in real-world AI systems.
• What is a neural network? – IBM – Gentle introduction to neural networks for readers who want a bit more grounding.