#tpus — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #tpus, aggregated by home.social.
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Planänderung für morgen:
Der #tpus trifft sich morgen, um 19 Uhr, im Biergarten Lister Turm. -
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Google controls ~25% of global AI compute, with ~3.8M TPUs and 1.3M GPUs
https://www.ft.com/content/2429f0f0-b685-4747-b425-bf8001a2e94c
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Google controls ~25% of global AI compute, with ~3.8M TPUs and 1.3M GPUs
https://www.ft.com/content/2429f0f0-b685-4747-b425-bf8001a2e94c
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🔥🤖 Google's latest flex: shoving #PyTorch into #TPUs like trying to fit a square peg in a round hole. Because obviously, #TensorFlow wasn't confusing enough already. 🙃🔍
https://developers.googleblog.com/torchtpu-running-pytorch-natively-on-tpus-at-google-scale/ #Google #TechNews #AI #HackerNews #ngated -
🔥🤖 Google's latest flex: shoving #PyTorch into #TPUs like trying to fit a square peg in a round hole. Because obviously, #TensorFlow wasn't confusing enough already. 🙃🔍
https://developers.googleblog.com/torchtpu-running-pytorch-natively-on-tpus-at-google-scale/ #Google #TechNews #AI #HackerNews #ngated -
TorchTPU: Running PyTorch Natively on TPUs at Google Scale
https://developers.googleblog.com/torchtpu-running-pytorch-natively-on-tpus-at-google-scale/
#HackerNews #TorchTPU #PyTorch #Google #TPUs #MachineLearning #AI
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TorchTPU: Running PyTorch Natively on TPUs at Google Scale
https://developers.googleblog.com/torchtpu-running-pytorch-natively-on-tpus-at-google-scale/
#HackerNews #TorchTPU #PyTorch #Google #TPUs #MachineLearning #AI
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"The market conversation is going to shift from the volume of tokens that you're generating to the utility of tokens and intelligence per dollar, intelligence per watt. So it's actually power efficiency and cost efficiency and value that you generate per token that matters a lot more." ~ Chirag Dekate, Gartner
#Google's new #TPUs assault AI's 'memory wall,' slash #AIinference latency and lower costs, setting up its enterprise cloud services to compete on price and power efficiency.
Check out this top news from #GoogleCloudNext, featuring details from an exclusive press preview event, comparative analysis with #NVIDIA 's #GPU systems, and the efficiency upshot for #enterpriseIT buyers: https://www.techtarget.com/searchitoperations/news/366642002/New-Google-TPUs-multiply-AI-infrastructure-efficiency
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"The market conversation is going to shift from the volume of tokens that you're generating to the utility of tokens and intelligence per dollar, intelligence per watt. So it's actually power efficiency and cost efficiency and value that you generate per token that matters a lot more." ~ Chirag Dekate, Gartner
#Google's new #TPUs assault AI's 'memory wall,' slash #AIinference latency and lower costs, setting up its enterprise cloud services to compete on price and power efficiency.
Check out this top news from #GoogleCloudNext, featuring details from an exclusive press preview event, comparative analysis with #NVIDIA 's #GPU systems, and the efficiency upshot for #enterpriseIT buyers: https://www.techtarget.com/searchitoperations/news/366642002/New-Google-TPUs-multiply-AI-infrastructure-efficiency
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🎉 Introducing the latest in #overpriced gibberish: Nanocode! For just $200, you too can partake in the mystical art of overcomplicating #JAX code on #TPUs. #GitHub #Copilot not included—just your own frustrated tears. 😂💸
https://github.com/salmanmohammadi/nanocode/discussions/1 #Nanocode #Frustration #HackerNews #ngated -
🎉 Introducing the latest in #overpriced gibberish: Nanocode! For just $200, you too can partake in the mystical art of overcomplicating #JAX code on #TPUs. #GitHub #Copilot not included—just your own frustrated tears. 😂💸
https://github.com/salmanmohammadi/nanocode/discussions/1 #Nanocode #Frustration #HackerNews #ngated -
Nanocode: The best Claude Code that $200 can buy in pure JAX on TPUs
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Nanocode: The best Claude Code that $200 can buy in pure JAX on TPUs
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Verdammt. Ich glaube, ich kriege Schnupfen. Wehe. Ich will Donnerstag zum #tpus !! (Und überhaupt)
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Verdammt. Ich glaube, ich kriege Schnupfen. Wehe. Ich will Donnerstag zum #tpus !! (Und überhaupt)
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Mein Wochenplan:
Heute: Lesung
Morgen: Lesung
Mittwoch: Konzert
Donnerstag: #tpus
Freitag: Nix
Sonnabend: Lesung -
#Nvidia secured a non-exclusive licensing agreement with #Groq, an #AIchip startup, for $20 billion. The deal aims to bring Groq’s CEO, #JonathanRoss, on board, along with their #inferencetechnology and #intellectualproperty. This move is seen as a strategic move to counter #Google’s success with #TPUs and maintain Nvidia’s dominance in the #AIchipmarket. https://spyglass.org/nvidia-groq-deal/?eicker.news #tech #media #news
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#Nvidia secured a non-exclusive licensing agreement with #Groq, an #AIchip startup, for $20 billion. The deal aims to bring Groq’s CEO, #JonathanRoss, on board, along with their #inferencetechnology and #intellectualproperty. This move is seen as a strategic move to counter #Google’s success with #TPUs and maintain Nvidia’s dominance in the #AIchipmarket. https://spyglass.org/nvidia-groq-deal/?eicker.news #tech #media #news
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Forget Nvidia: Alphabet Is the New Hot Chip Stock to Own, Apparently Alphabet (NASDAQ:GOOG) could be among the most-watched mega-cap tech stocks in the market right now. Between the company’s co...
#Investing #Alphabet #Alphabet #stock #chips #GPUs #Nvidia #semiconductors #TPUs #Alphabet #Alphabet
Origin | Interest | Match -
Touching the Elephant – TPUs
https://considerthebulldog.com/tte-tpu/
#HackerNews #Touching #the #Elephant #TPUs #TPU #Technology #AI #Hardware #Machine #Learning
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Touching the Elephant – TPUs
https://considerthebulldog.com/tte-tpu/
#HackerNews #Touching #the #Elephant #TPUs #TPU #Technology #AI #Hardware #Machine #Learning
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"If you go back a year or two, you might make the case that Nvidia had three moats relative to TPUs: superior performance, significantly more flexibility due to GPUs being more general purpose than TPUs, and CUDA and the associated developer ecosystem surrounding it. OpenAI, meanwhile, had the best model, extensive usage of their API, and the massive number of consumers using ChatGPT.
The question, then, is what happens if the first differentiator for each company goes away? That, in a nutshell, is the question that has been raised over the last two weeks: does Nvidia preserve its advantages if TPUs are as good as GPUs, and is OpenAI viable in the long run if they don’t have the unquestioned best model?
Nvidia’s flexibility advantage is a real thing; it’s not an accident that the fungibility of GPUs across workloads was focused on as a justification for increased capital expenditures by both Microsoft and Meta. TPUs are more specialized at the hardware level, and more difficult to program for at the software level; to that end, to the extent that customers care about flexibility, then Nvidia remains the obvious choice.
CUDA, meanwhile, has long been a critical source of Nvidia lock-in, both because of the low level access it gives developers, and also because there is a developer network effect: you’re just more likely to be able to hire low level engineers if your stack is on Nvidia. The challenge for Nvidia, however, is that the “big company” effect could play out with CUDA in the opposite way to the flexibility argument. While big companies like the hyperscalers have the diversity of workloads to benefit from the flexibility of GPUs, they also have the wherewithal to build an alternative software stack. That they did not do so for a long time is a function of it simply not being worth the time and troube..."
https://stratechery.com/2025/google-nvidia-and-openai/
#AI #GenerativeAI #Nvidia #Google #ChatGPT #OpenAI #LLMs #Chatbots #CUDA #GPUs #TPUs
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"If you go back a year or two, you might make the case that Nvidia had three moats relative to TPUs: superior performance, significantly more flexibility due to GPUs being more general purpose than TPUs, and CUDA and the associated developer ecosystem surrounding it. OpenAI, meanwhile, had the best model, extensive usage of their API, and the massive number of consumers using ChatGPT.
The question, then, is what happens if the first differentiator for each company goes away? That, in a nutshell, is the question that has been raised over the last two weeks: does Nvidia preserve its advantages if TPUs are as good as GPUs, and is OpenAI viable in the long run if they don’t have the unquestioned best model?
Nvidia’s flexibility advantage is a real thing; it’s not an accident that the fungibility of GPUs across workloads was focused on as a justification for increased capital expenditures by both Microsoft and Meta. TPUs are more specialized at the hardware level, and more difficult to program for at the software level; to that end, to the extent that customers care about flexibility, then Nvidia remains the obvious choice.
CUDA, meanwhile, has long been a critical source of Nvidia lock-in, both because of the low level access it gives developers, and also because there is a developer network effect: you’re just more likely to be able to hire low level engineers if your stack is on Nvidia. The challenge for Nvidia, however, is that the “big company” effect could play out with CUDA in the opposite way to the flexibility argument. While big companies like the hyperscalers have the diversity of workloads to benefit from the flexibility of GPUs, they also have the wherewithal to build an alternative software stack. That they did not do so for a long time is a function of it simply not being worth the time and troube..."
https://stratechery.com/2025/google-nvidia-and-openai/
#AI #GenerativeAI #Nvidia #Google #ChatGPT #OpenAI #LLMs #Chatbots #CUDA #GPUs #TPUs
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"In the blistering race for AI supremacy, Nvidia has long reigned as the undisputed king. Its GPUs powered the explosive growth of machine learning, turning abstract neural networks into reality and fueling an empire valued at trillions. But as the AI landscape evolves, cracks are appearing in Nvidia's armor. The shift from model training (Nvidia's stronghold) to inference, the real-time application of those models, is reshaping the market. And at the forefront of this revolution stands Google's Tensor Processing Units (TPUs), delivering unmatched efficiency and cost savings that could spell the end of Nvidia's monopoly.
By 2030, inference will consume 75% of AI compute, creating a $255 billion market growing at 19.2% annually. Yet most companies still optimize for training costs. This isn't just hype; it's economics. Training is a one-time sprint, but inference is an endless marathon. As companies like OpenAI grapple with skyrocketing inference bills (projected at $2.3 billion for 2024 alone, dwarfing the $150 million cost to train GPT-4), Google's TPUs emerge as the cost-effective powerhouse. In this in-depth analysis, we'll explore how TPUs are winning the inference war, backed by real-world migrations from industry leaders, and why this pivot signals Nvidia's impending decline."
https://www.ainewshub.org/post/ai-inference-costs-tpu-vs-gpu-2025
#AI #AIInference #GenerativeAI #Nvidia #Google #GPUs #TPUs #LLMs
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"In the blistering race for AI supremacy, Nvidia has long reigned as the undisputed king. Its GPUs powered the explosive growth of machine learning, turning abstract neural networks into reality and fueling an empire valued at trillions. But as the AI landscape evolves, cracks are appearing in Nvidia's armor. The shift from model training (Nvidia's stronghold) to inference, the real-time application of those models, is reshaping the market. And at the forefront of this revolution stands Google's Tensor Processing Units (TPUs), delivering unmatched efficiency and cost savings that could spell the end of Nvidia's monopoly.
By 2030, inference will consume 75% of AI compute, creating a $255 billion market growing at 19.2% annually. Yet most companies still optimize for training costs. This isn't just hype; it's economics. Training is a one-time sprint, but inference is an endless marathon. As companies like OpenAI grapple with skyrocketing inference bills (projected at $2.3 billion for 2024 alone, dwarfing the $150 million cost to train GPT-4), Google's TPUs emerge as the cost-effective powerhouse. In this in-depth analysis, we'll explore how TPUs are winning the inference war, backed by real-world migrations from industry leaders, and why this pivot signals Nvidia's impending decline."
https://www.ainewshub.org/post/ai-inference-costs-tpu-vs-gpu-2025
#AI #AIInference #GenerativeAI #Nvidia #Google #GPUs #TPUs #LLMs
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#Google is selling its #TPU #chips to external customers, challenging #Nvidia’s dominance in the #AIhardware market. #Anthropic, a major AI company, is a significant customer, purchasing over 1 million #TPUs. This move positions Google as a direct competitor to Nvidia, offering a differentiated #cloudprovider option with its #inhousesilicon design capabilities. https://newsletter.semianalysis.com/p/tpuv7-google-takes-a-swing-at-the?eicker.news #tech #media #news
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#Google is selling its #TPU #chips to external customers, challenging #Nvidia’s dominance in the #AIhardware market. #Anthropic, a major AI company, is a significant customer, purchasing over 1 million #TPUs. This move positions Google as a direct competitor to Nvidia, offering a differentiated #cloudprovider option with its #inhousesilicon design capabilities. https://newsletter.semianalysis.com/p/tpuv7-google-takes-a-swing-at-the?eicker.news #tech #media #news
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TPUs vs. GPUs and why Google is positioned to win AI race in the long term
https://www.uncoveralpha.com/p/the-chip-made-for-the-ai-inference
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TPUs vs. GPUs and why Google is positioned to win AI race in the long term
https://www.uncoveralpha.com/p/the-chip-made-for-the-ai-inference
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#Nvidia asserts its #GPUs are a generation ahead of #Google’s #AIchips, despite concerns about potential #competition. Nvidia highlights its chips’ flexibility and power compared to Google’s #ASIC chips, emphasising their ability to run every AI model. While Google’s #TPUs are gaining attention, Nvidia remains a dominant player in the #AIchipmarket. https://www.cnbc.com/2025/11/25/nvidia-says-its-gpus-are-a-generation-ahead-of-googles-ai-chips.html?eicker.news #tech #media #news
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#Nvidia asserts its #GPUs are a generation ahead of #Google’s #AIchips, despite concerns about potential #competition. Nvidia highlights its chips’ flexibility and power compared to Google’s #ASIC chips, emphasising their ability to run every AI model. While Google’s #TPUs are gaining attention, Nvidia remains a dominant player in the #AIchipmarket. https://www.cnbc.com/2025/11/25/nvidia-says-its-gpus-are-a-generation-ahead-of-googles-ai-chips.html?eicker.news #tech #media #news
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Start 2027 🛰️ Erste Test #Satelliten mit jeweils vier #TPUs sollen 2027 starten und das Konzept real erproben.
Herausforderungen ⚠️ #Strahlung im #All kann #Chips schädigen, doch erste Tests zeigen, dass die Hardware Missionen von fünf bis sechs Jahren verkraften könnte.
👉 https://eicker.TV ▹ #Technik #Medien #Politik #Wirtschaft ⏻ https://eicker.BE/ratung #Onlinestrategie und #Onlinemarketing von Gerrit Eicker aus #Münster im #Münsterland in #Westfalen
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#Google plans to launch #AIchips, known as Tensor Processing Units (#TPUs), into low-earth #orbit to power #datacentres with #solarenergy. The TPUs, attached to #satellites, will form a constellation for #highbandwidth #communication, potentially becoming economical by 2035. Challenges include radiation exposure and ensuring chip longevity. https://www.semafor.com/article/11/04/2025/google-wants-to-build-solar-powered-data-centers-in-space?eicker.news #tech #media #news
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#Google plans to launch #AIchips, known as Tensor Processing Units (#TPUs), into low-earth #orbit to power #datacentres with #solarenergy. The TPUs, attached to #satellites, will form a constellation for #highbandwidth #communication, potentially becoming economical by 2035. Challenges include radiation exposure and ensuring chip longevity. https://www.semafor.com/article/11/04/2025/google-wants-to-build-solar-powered-data-centers-in-space?eicker.news #tech #media #news
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Der #tpus trifft sich das nächste Mal am 30. Oktober im Harp. Nicht das jemand sagen kann, er hätte nichts gewusst.
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Eine weitere wichtige Frage bei diesem #tpus stellt @schwobimexil.bsky.social:
„Warum sind Schlüsseldienst und Schusterei immer zusammen?0 -
Der #tpus trifft sich das nächste Mal am 25. September, 19 Uhr, an wohlbekanntem Orte.
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🤔 So you want to *think* about GPUs? Good luck diving through 12 parts of pseudo-instructions that are all about #TPUs while pretending #GPUs matter. 🚀 Spoiler alert: They're basically fancy math rocks, but hey, at least they're not TPUs! ✨
https://jax-ml.github.io/scaling-book/gpus/ #technology #computing #mathrocks #hackernews #HackerNews #ngated -
In the thrilling saga of tech jargon excess, we're graced with a 13-part opus that finally teaches us how to think about GPUs—because apparently, we all need a PhD in rooflines and a minor in sharded matmuls to grasp the basics. 🙄 Spoiler alert: #NVIDIA GPUs exist and they're just as good as #TPUs, but someone had to write a book to tell us that. 📚🎉
https://jax-ml.github.io/scaling-book/gpus/ #techjargon #GPUeducation #techbooks #HackerNews #ngated -
How to scale your model: A systems view of LLMs on TPUs
https://jax-ml.github.io/scaling-book/
#ycombinator #scaling #jax #llms #transformers #tpus #google #deepmind #parallelism #pallas -
How to Scale Your Model: A Systems View of LLMs on TPUs
https://jax-ml.github.io/scaling-book/
#ycombinator #scaling #jax #llms #transformers #tpus #google #deepmind #parallelism #pallas -
Found the boxes misplaced inside another box of #TPUs, so here is a #deconstructed #Jolla #Mind2 represented by #Rock5B #RK3588 #NPU in #FireBreathing #HappyWeekend edition.
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Google Colab – Die Demokratisierung der KI Programmierung
#Colab #GoogleAI #MaschinellesLernen #KIProgrammierung #TPUs #NVIDIAGPUs #DemokratisierungDerKI #CodeAssistenz #HausaNLP #Programmierwerkzeuge
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Ultimately we can run #DeepLearning algorithms on tiny #embedded chips as well, but currently we are painstakingly emulating these novel #architectures on traditional #CPUs, #GPUs and #TPUs, which are very unsuitable for these workloads.
These domains are very different from one another on a very deep level, down to #processor architecture level.
