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Google Cloud TPU VS Apple Core ML

Compare Google Cloud TPU VS Apple Core ML and see what are their differences

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Google Cloud TPU logo Google Cloud TPU

Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.

Apple Core ML logo Apple Core ML

Integrate a broad variety of ML model types into your app
  • Google Cloud TPU Landing page
    Landing page //
    2023-08-19
  • Apple Core ML Landing page
    Landing page //
    2023-06-13

Google Cloud TPU videos

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Apple Core ML videos

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IBM Watson & Apple Core ML Collaboration - What it means for app development

Category Popularity

0-100% (relative to Google Cloud TPU and Apple Core ML)

Google Cloud TPU

Apple Core ML

Data Science And Machine Learning
68 68%
Data Science And Machine Learning
32% 32
Developer Tools
0 0%
Developer Tools
100% 100
Data Dashboard
100 100%
Data Dashboard
0% 0
AI
0 0%
AI
100% 100

User comments

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Social recommendations and mentions

Apple Core ML might be a bit more popular than Google Cloud TPU. We know about 7 links to it since March 2021 and only 5 links to Google Cloud TPU. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Google Cloud TPU mentions (5)

  • 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 / about 2 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 2 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 3 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: almost 3 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: about 3 years ago

Apple Core ML mentions (7)

  • Ask HN: Where is Apple? They seem to be left out of the AI race?
    On the machine learning side of AI, they have CoreML. You can drag-and-drop images into Xcode to train an image classifier. And run the models on device, so if solar flares destroy the cell phone network and terrorists bomb all the data centers, your phone could still tell you if it's a hot dog or not. https://developer.apple.com/machine-learning/ https://developer.apple.com/machine-learning/core-ml/... - Source: Hacker News / 2 months ago
  • The Magnitude of the AI Bubble
    Apple has actually created ML chipsets, so AI can be executed natively, on-device. https://developer.apple.com/machine-learning/. - Source: Hacker News / 4 months ago
  • Does anyone else suspect that the official iOS ChatGPT app might be conducting some local inference / edge-computing? [Discussion]
    For your reference, Apple's pages for Machine Learning for Developers and for their research. The Apple Neural Engine was custom designed to work better with their proprietary machine learning programs -- and they've been opening up access to developers by extending support / compatibility for TensorFlow and PyTorch. They've also got CoreML, CreateML, and various APIs they are making to allow more use of their... Source: 12 months ago
  • Apple to occupy 90% of TSMC 3nm capacity in 2023
    > It’d be one thing if Apple actually worked on AI softwares a bit and made it readily available to developers. * Apple Silicon CPUs have a Neural Engine specifically made for fast ML-inference * Apple supports PyTorch (https://developer.apple.com/metal/pytorch/) * Apple has its own easily accessible machine-learning framework called Core-ML (https://developer.apple.com/machine-learning/) So it would be inaccurate... - Source: Hacker News / 12 months ago
  • The iPhone 13 is a pitch-perfect iPhone 12S
    This is the developer documentation where they advertise the APIs - https://developer.apple.com/machine-learning/. Source: over 2 years ago
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What are some alternatives?

When comparing Google Cloud TPU and Apple Core ML, you can also consider the following products

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Amazon Machine Learning - Machine learning made easy for developers of any skill level

machine-learning in Python - Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python.

TensorFlow Lite - Low-latency inference of on-device ML models

Amazon Forecast - Accurate time-series forecasting service, based on the same technology used at Amazon.com. No machine learning experience required.

Roboflow Universe - You no longer need to collect and label images or train a ML model to add computer vision to your project.

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