苹果最新的编程框架Xcode 16在最近的WWDC大会上发布，它包含了许多使用AI简化编程和应用程序集成的功能。然而，它也为Swift编程语言添加了新功能，例如Code Complete，可以预测和完成代码。另一个名为SwiftAssist的功能可以回答编码问题并帮助使用API。苹果的电脑现在使用自家研发的Apple Silicon，包括GPU、CPU和AI芯片。Mac之前依赖于来自AMD和Nvidia的x86芯片和GPU，但不再支持外部GPU。这将Mac开发人员局限在有限的环境中，无法编写AI应用程序。

🍎 **苹果的全新开发工具限制了HPC用户使用英特尔或英伟达的并行编程框架。** 苹果最新的编程框架Xcode 16在最近的WWDC大会上发布，其中包含许多使用AI简化编程和应用程序集成的功能。然而，它也为Swift编程语言添加了新功能，例如Code Complete，可以预测和完成代码。另一个名为SwiftAssist的功能可以回答编码问题并帮助使用API。苹果的电脑现在使用自家研发的Apple Silicon，包括GPU、CPU和AI芯片。Mac之前依赖于来自AMD和Nvidia的x86芯片和GPU，但不再支持外部GPU。这意味着Mac开发人员将被限制在一个有限的环境中，无法编写AI应用程序。

💻 **苹果鼓励开发人员将机器学习模型迁移到其CoreML格式，该格式利用了自家研发的CPU、GPU和神经处理器。** 苹果在WWDC上分享了其更广泛的AI计划，透露其已在谷歌的张量处理单元上训练了自己的LLM。苹果还建立了自己的私有计算云，该云将托管在谷歌的数据中心。苹果不会依赖于英伟达GPU进行云端AI，因为其AI战略侧重于能效，为此它拥有自己的算术技术。英伟达的GPU在更大规模的LLM上运行训练和推理，消耗更多能量。

🤖 **英特尔和英伟达正在积极应对MacOS支持的缺失。** 英特尔在其最新版本的OneAPI并行编程框架中取消了对MacOS的支持。英伟达多年来一直停止了对AI和HPC CUDA编程工具的MacOS支持。开发人员将不得不切换到Linux或Windows才能为英伟达GPU创建应用程序。CUDA为在英伟达硬件上运行的AI应用程序提供了必要的工具。与苹果一样，英伟达试图将客户锁定在其硬件和软件中。英伟达的开发工具打包在一个名为AI Enterprise的套件中，该套件并非免费。

🎮 **苹果拥有自己的游戏和AI框架Metal，该框架针对其GPU进行了优化。** 少数非常老的AMD和Nvidia GPU支持Metal，现在它仅限于苹果内部的GPU。然而，Mac开发人员将能够使用云端托管的英伟达GPU，这在今天通常是这种情况。云提供商通常为英伟达GPU提供工作环境，而无需与PC操作系统有任何关联。

🚀 **苹果的全新开发工具限制了HPC用户使用英特尔或英伟达的并行编程框架。** 苹果最新的编程框架Xcode 16在最近的WWDC大会上发布，其中包含许多使用AI简化编程和应用程序集成的功能。然而，它也为Swift编程语言添加了新功能，例如Code Complete，可以预测和完成代码。另一个名为SwiftAssist的功能可以回答编码问题并帮助使用API。苹果的电脑现在使用自家研发的Apple Silicon，包括GPU、CPU和AI芯片。Mac之前依赖于来自AMD和Nvidia的x86芯片和GPU，但不再支持外部GPU。这意味着Mac开发人员将被限制在一个有限的环境中，无法编写AI应用程序。

New Mac developer tools will leverage Apple’s homegrown chips, limiting HPC users’ ability to use parallel programming frameworks from Intel or Nvidia.

Apple’s latest programming framework, Xcode 16, was introduced at the recent WWDC conference and has a host of new features that use AI to make programming and application integration easier.

However, it also adds new capabilities to its Swift programming language with features like Code Complete, which can predict and complete code. Another feature called SwiftAssist answers coding questions and helps with APIs.

Apple’s PCs now use homegrown Apple Silicon, which includes GPUs, CPUs, and AI chips. Macs previously relied on x86 chips and GPUs from AMD and Nvidia but no longer support external GPUs. That puts Mac developers in a box by providing limited environments in which to write AI applications.

At WWDC, Apple encouraged developers to move machine-learning models to its CoreML format, which leverages homegrown CPUs, GPUs, and neural processors.

An open-source Python package called CoreML Tools converts PyTorch models to work with Apple’s AI hardware. Developers can also use JAX, TensorFlow, or MLX.

Intel and Nvidia aren’t wasting time with MacOS support. Intel pulled MacOS support for its latest 2024 version of the OneAPI parallel programming framework.

Apple shared its broader AI plans at WWDC, revealing that it had trained its LLMs on Google’s Tensor Processing Unit.

Apple has also established its own Private Compute Cloud, which will be hosted in Google’s data centers. Apple will not rely on Nvidia GPUs for AI in the cloud as its AI strategy is focused on power efficiency, for which it has its own arithmetic techniques. Nvidia’s GPUs run training and inference on larger LLMs that consume more power.

Nvidia discontinued MacOS support for its AI and HPC CUDA programming tools many years ago. Developers will have to switch to Linux or Windows to create applications for Nvidia GPUs.

CUDA provides the necessary tools for AI applications to run on Nvidia hardware. Like Apple, Nvidia is trying to lock down customers to its hardware and software. Nvidia’s development tools are packaged into a suite called AI Enterprise, which isn’t free.

Apple has its own gaming and AI framework called Metal, which is optimized for its GPUs. A handful of very old AMD and Nvidia GPUs support Metal, which is now exclusively relegated to Apple’s in-house GPU.

However, Mac developers will be able to work with Nvidia GPUs hosted in the cloud, which is mostly the case today. Cloud providers typically provide working environments for Nvidia GPUs without any ties to PC operating systems.