Google Cloud TPU Reviews and Details

Is Google Cloud TPU good?

Social recommendations and mentions

• I think Anthropic and OpenAI have found product-market fit

  I think the third company (likely Google) is going to make LLMs financially feasible with: - dedicated hardware (https://cloud.google.com/tpu) - optimized models (https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/). - Source: Hacker News / about 2 months ago

• Google Just Split Its TPU Into Two Chips. Here's What That Actually Signals About the Agentic Era.

  Previous TPU generations, including last year's Ironwood, were pitched as unified flagship chips. Google's internal experience running Gemini, its consumer AI products, and increasingly complex agent workloads apparently showed that a single architecture forces uncomfortable trade-offs. So they split the roadmap. - Source: dev.to / 3 months ago

• TPU Mythbusting: vendor lock-in

  Tensor Processing Units are a technology developed and owned by Google. While you can find GPUs in every cloud provider offer, the TPUs are currently only available through Google Cloud Platform. Situation when you invest in a technology or a service that is not available anywhere else is called vendor lock-in — it's something the sales people love, while customers try to avoid it. What does this look like for... - Source: dev.to / 3 months ago

• It's Time to Learn about Google TPUs in 2026

  Google's model is cloud-based. You can't buy a TPU to put in your server. Instead, Google keeps them in their own data centers and rents access exclusively through this. This allows Google to control the entire stack and they don't have to pay the "NVIDIA Tax". - Source: dev.to / 6 months ago

• Google Got Its Groove Back and Edged Ahead of OpenAI

  While I don't use Gemini, I'm betting they'll end up being the cheapest in the future because Google is developing the entire stack, instead of relying on GPUs. I think that puts them in a much better position than other companies like OpenAI. https://cloud.google.com/tpu. - Source: Hacker News / 6 months ago

• TinyTinyTPU: 2×2 systolic-array TPU-style matrix-multiply unit deployed on FPGA

  > TPUs aren't transformer ASICs. https://cloud.google.com/tpu > A TPU is an application-specific integrated circuit (ASIC) designed by Google for neural networks. - Source: Hacker News / 7 months ago

• TinyTinyTPU: 2×2 systolic-array TPU-style matrix-multiply unit deployed on FPGA

  Gemini is likely the most widely used gen AI model in the world considering search, Android integration, and countless other integrations into the Google ecosystem. Gemini runs on their custom TPU chips. So I would say a large portion of inference is already using ASIC. https://cloud.google.com/tpu. - Source: Hacker News / 7 months ago

• TPUs vs. GPUs and why Google is positioned to win AI race in the long term

  Google does not sell them, but you can rent them: https://cloud.google.com/tpu As you note, they'll set the margins to benefit themselves, but you can still eke out some benefit. Also, you can buy Edge TPUs, but as the name says these are for edge AI inference and useless for any heavy lifting workloads like training or LLMs.... - Source: Hacker News / 8 months ago

• HipKittens: Fast and Furious AMD Kernels

  You can pay to use them https://cloud.google.com/tpu. - Source: Hacker News / 8 months ago

• Why AI infrastructure and multi-platform compute strategy matters now?

  First, multi-platform approaches reduce vendor lock-in because teams spread risk across clouds and vendors. For example, you can run TPU workloads on Google Cloud at https://cloud.google.com/tpu and Trainium on AWS at https://aws.amazon.com/machine-learning/trainium/. Meanwhile, NVIDIA H100 instances remain essential for many GPU-optimized models https://www.nvidia.com/en-us/data-center/h100/. - Source: dev.to / 9 months ago

• GCP Fundamentals: Cloud TPU API

  The Cloud TPU API provides a powerful and cost-effective way to accelerate your machine learning workloads. By leveraging Google’s custom-designed hardware and integrating seamlessly with the broader GCP ecosystem, you can unlock new levels of performance and innovation. Explore the official documentation and try a hands-on lab to experience the benefits of Cloud TPUs firsthand. https://cloud.google.com/tpu. - Source: dev.to / about 1 year ago

• AI Model Optimization on AWS Inferentia and Trainium

  Photo by julien Tromeur on Unsplash We are in a golden age of AI, with cutting-edge models disrupting industries and poised to transform life as we know it. Powering these advancements are increasingly powerful AI accelerators, such as NVIDIA H100 GPUs, Google Cloud TPUs, AWS's Trainium and Inferentia chips, and more. With the growing number of options comes the challenge of selecting the most optimal... - Source: dev.to / over 1 year ago

• Pathways Language Model (Palm): 540B Parameters for Breakthrough Perf

  According to https://cloud.google.com/tpu, each individual TPUv3 has 420 Teraflops, and TPUv4 is supposed to double that performance, so if that guess is correct, it should take a few seconds to do inference. Quite impressive really. - Source: Hacker News / over 4 years ago

• The AI Research SuperCluster

  You can also rent a cloud TPU-v4 pod (https://cloud.google.com/tpu) which 4096 TPUv-4 chips with fast interconnect, amounting to around 1.1 exaflops of compute. It won't be cheap though (excess of 20M$/year I believe). - Source: Hacker News / over 4 years ago

• Stadia's future includes running the backend of other streaming platforms, job listing reveals

  Actually, that's done with TPUs which are more efficient: https://cloud.google.com/tpu. Source: almost 5 years ago

• Nvidia CEO: Ethereum Is Going To Be Quite Valuable, Transactions Will Still Be A Lot Faster

  TPU training uses Google silicon and is thus a true deep learning alternative to Nvidia. Source: about 5 years ago

• Server Question

  The server choice really depends on how much CPU and RAM the requests take, how many users will be hitting the server, etc. You can start with a $5/month Digital Ocean server (or AWS or Google) and see if that works for you. Or you can outsource the server administration to Amazon or Google if you don't want to deal with it or need specialized tpu hardware. Source: over 5 years ago