{"slug": "tpus-vs-gpus-how-google-s-tensor-processing-units-actually-work", "title": "TPUs vs GPUs: How Google's Tensor Processing Units Actually Work", "summary": "Google's Tensor Processing Units (TPUs) are specialized chips designed for neural network matrix multiplications, differing fundamentally from GPUs. Unlike GPUs, which evolved from graphics rendering, TPUs use a systolic array architecture that minimizes memory movement, addressing the memory bottleneck in large AI models. This design choice makes TPUs highly efficient for the predictable, repetitive multiply-add operations common in deep learning.", "body_md": "*Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product.*\n\nMachine learning engineers spend countless hours optimizing models, tweaking architectures, and squeezing performance out of hardware.\n\nYet many developers who train large models today have only a vague understanding of the machines doing the actual work.\n\nAsk a developer how a GPU works, and you'll usually hear something about \"lots of parallel cores.\"\n\nAsk how a TPU works, and the answer is often, \"Google made a chip for AI.\"\n\nBut the design differences are much more interesting than that.\n\nTPUs weren't built as faster GPUs. They were built around a different assumption: that neural networks spend most of their time performing enormous matrix multiplications. Once you accept that premise, the entire chip architecture changes.\n\nLet's explore how TPUs work, why Google built them, and where they outperform GPUs.\n\nAt a high level, modern neural networks are giant collections of matrix operations.\n\nConsider a simple transformer layer:\n\n```\noutput = X @ W\n```\n\nWhere:\n\n`X`\n\nis the input activation matrix`W`\n\nis the weight matrixUnder the hood, this becomes millions or billions of multiply-and-add operations.\n\nFor example:\n\n```\nA (4096 x 4096)\n×\nB (4096 x 4096)\n=\nC (4096 x 4096)\n```\n\nThis single operation contains over 68 billion multiply-accumulate computations.\n\nTraining and inference repeatedly execute these operations.\n\nThe hardware question becomes:\n\nWhat is the fastest possible machine for multiplying giant matrices?\n\nGPUs and TPUs answer this question differently.\n\nGPUs were never originally designed for machine learning.\n\nThey were built to render graphics.\n\nRendering a video game requires performing similar operations on millions of pixels simultaneously.\n\nThis naturally led GPU manufacturers to create architectures containing thousands of lightweight processing cores.\n\nA simplified GPU architecture looks like this:\n\n```\nCPU\n |\n | launches kernels\n |\nGPU\n ├── Thousands of parallel cores\n ├── Shared memory\n ├── Global memory\n └── Scheduling logic\n```\n\nThe key idea:\n\nThis approach works extremely well for deep learning because matrix multiplication can be broken into many independent tasks.\n\nThe result was almost accidental:\n\nThe hardware built for gaming turned out to be excellent for neural networks.\n\nAround 2013–2015, Google's infrastructure was serving billions of machine learning predictions every day.\n\nEngineers noticed something important.\n\nMany GPU features were rarely used during inference:\n\nThese features are valuable for a broad range of workloads.\n\nBut neural networks are highly predictable.\n\nMost of the work boils down to:\n\n```\nMultiply\nAdd\nMultiply\nAdd\nMultiply\nAdd\n```\n\nOver and over.\n\nGoogle asked a radical question:\n\nWhat if we remove everything that isn't useful for matrix multiplication?\n\nThe answer became the TPU.\n\nThe most important component inside a TPU is the systolic array.\n\nA systolic array is a grid of processing elements that pass data rhythmically through the chip.\n\nImagine a matrix multiplication:\n\n```\nA × B = C\n```\n\nInstead of sending data back and forth to memory repeatedly, the TPU streams values through a grid.\n\nA simplified example:\n\n```\nA →\n[PE][PE][PE]\n[PE][PE][PE]\n[PE][PE][PE]\n      ↓\n      B\n```\n\nEach Processing Element (PE):\n\nThe data \"flows\" through the chip like blood through arteries.\n\nThat's where the name systolic comes from.\n\nThis architecture dramatically reduces memory movement, which is often the true bottleneck in modern computing.\n\nMoving data frequently costs more energy and time than performing arithmetic.\n\nTPUs are designed around minimizing that movement.\n\nMany developers assume AI workloads are limited by compute.\n\nIn reality, large models are often limited by memory.\n\nConsider two scenarios.\n\nThe processor performs:\n\n```\n2 × 3\n```\n\nThis operation is extremely cheap.\n\nThe processor fetches:\n\n```\n2\n3\n```\n\nfrom distant memory before performing the multiplication.\n\nThe memory access can cost far more than the arithmetic.\n\nAs models scale, this becomes increasingly important.\n\nTPUs address this problem using:\n\nThe goal is simple:\n\nMove data as little as possible.\n\nThis is one reason TPUs achieve impressive performance-per-watt.\n\nOne TPU is powerful.\n\nA TPU Pod is where things become interesting.\n\nGoogle connects thousands of TPUs using specialized high-speed interconnects.\n\nConceptually:\n\n```\nTPU  TPU  TPU  TPU\n |    |    |    |\nTPU  TPU  TPU  TPU\n |    |    |    |\nTPU  TPU  TPU  TPU\n```\n\nThese chips behave almost like one giant distributed accelerator.\n\nLarge language models frequently require:\n\nTPU Pods were designed with these workloads in mind.\n\nThis is one reason many frontier-scale models have historically been trained on TPU infrastructure.\n\nThe networking architecture becomes nearly as important as the chips themselves.\n\nThe answer depends on the workload.\n\nAdvantages:\n\nAdvantages:\n\nThe tradeoff is flexibility.\n\nA GPU is a powerful general-purpose parallel computer.\n\nA TPU is a highly specialized neural network machine.\n\nThink of it like:\n\nThe assembly line wins if your workload matches its design.\n\nAs models continue growing, hardware architecture is becoming a first-class concern.\n\nTen years ago, most developers could treat hardware as a black box.\n\nToday:\n\nUnderstanding TPUs isn't just about learning another chip.\n\nIt's about understanding a broader trend:\n\nThe era of general-purpose computing is giving way to increasingly specialized hardware.\n\nTPUs are one example.\n\nAI accelerators from NVIDIA, AMD, Amazon, Microsoft, Cerebras, Groq, and many others are pushing the same idea further.\n\nThe future of AI may not belong to the fastest processor.\n\nIt may belong to the processor whose architecture most closely matches the mathematics of machine learning.\n\nGPUs helped ignite the deep learning revolution because they offered massive parallelism at scale. TPUs took the next step by asking a narrower question: if neural networks mostly perform matrix multiplication, why not build hardware specifically for that task?\n\nThe result was a radically different architecture centered around systolic arrays, data movement efficiency, and large-scale distributed training.\n\nAs AI systems continue growing, understanding these architectural choices becomes increasingly valuable—not just for hardware engineers, but for every developer building machine learning systems.\n\n**If you were training a large model today, would you prioritize the flexibility of GPUs or the specialization of TPUs—and why?**\n\n*AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.\n\ngit-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.*\n\nAny feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.\n\n| [🇩🇰 Dansk](https://github.com/HexmosTech/git-lrc/readme/README.da.md) | [🇪🇸 Español](https://github.com/HexmosTech/git-lrc/readme/README.es.md) | [🇮🇷 Farsi](https://github.com/HexmosTech/git-lrc/readme/README.fa.md) | [🇫🇮 Suomi](https://github.com/HexmosTech/git-lrc/readme/README.fi.md) | [🇯🇵 日本語](https://github.com/HexmosTech/git-lrc/readme/README.ja.md) | [🇳🇴 Norsk](https://github.com/HexmosTech/git-lrc/readme/README.nn.md) | [🇵🇹 Português](https://github.com/HexmosTech/git-lrc/readme/README.pt.md) | [🇷🇺 Русский](https://github.com/HexmosTech/git-lrc/readme/README.ru.md) | [🇦🇱 Shqip](https://github.com/HexmosTech/git-lrc/readme/README.sq.md) | [🇨🇳 中文](https://github.com/HexmosTech/git-lrc/readme/README.zh.md) | [🇮🇳 हिन्दी](https://github.com/HexmosTech/git-lrc/readme/README.hi.md) |\n\nGenAI today is a **race car without brakes**. It accelerates fast -- you describe something, and large blocks of code appear instantly. But AI agents *silently break things*: they remove logic, relax constraints, introduce expensive cloud calls, leak credentials, and change behavior -- without telling you. You often find out in production.\n\n** git-lrc is your braking system.** It hooks into\n\n`git commit`\n\nand runs an AI review on every diff In short, git-lrc helps **Prevent Outages, Breaches, and Technical Debt Before They Happen**\n\n**At a glance:** [10 risk categories](https://github.com/HexmosTech/git-lrc#what-git-lrc-checks-for) · [100+ failure patterns tracked](https://github.com/HexmosTech/git-lrc#what-git-lrc-checks-for) · every commit…", "url": "https://wpnews.pro/news/tpus-vs-gpus-how-google-s-tensor-processing-units-actually-work", "canonical_source": "https://dev.to/shrsv/tpus-vs-gpus-how-googles-tensor-processing-units-actually-work-c8i", "published_at": "2026-06-21 16:44:01+00:00", "updated_at": "2026-06-21 17:04:01.706805+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-infrastructure", "ai-chips"], "entities": ["Google", "Shrijith Venkatramana", "TPU", "GPU", "Tensor Processing Unit", "systolic array"], "alternates": {"html": "https://wpnews.pro/news/tpus-vs-gpus-how-google-s-tensor-processing-units-actually-work", "markdown": "https://wpnews.pro/news/tpus-vs-gpus-how-google-s-tensor-processing-units-actually-work.md", "text": "https://wpnews.pro/news/tpus-vs-gpus-how-google-s-tensor-processing-units-actually-work.txt", "jsonld": "https://wpnews.pro/news/tpus-vs-gpus-how-google-s-tensor-processing-units-actually-work.jsonld"}}