Apple Silicon and the Future of Computing

The best Mac you can buy for video editing and content creation is now whichever M1 or M2 Mac you can afford. They will all get it done.

A large percentage of visitors to this website come here for one reason. Whether by search or a link somewhere they have arrived at one specific page relating to running DaVinci Resolve on budget hardware.

Over the past few years, I’ve expanded into related topics such as how to set up proper color managed video monitoring on a budget, video storage, and the best value Thunderbolt eGPU solutions. Of course as technology changes I have to update or rewrite these articles, and in some cases particular solutions that were once relevant, may no longer be so.

The common foundation of my writing on all these topics is to present solutions and workarounds to problems faced by video creators that arise from hardware limitations.

The major limitations faced by those of us working with hardware on the budget friendly end of the computing spectrum include hitting the limits of a system’s processing power, storage read/write bandwidth, RAM, and GPU memory.

In some cases, the only consequence of hitting one or more of these limits is waiting longer for a video to render, or working with low resolution proxy media to ensure a responsive editing experience, then waiting to slowly render a finished project from the full resolution camera media. However, it often means some video files won’t play at all, and certain plugins or processes are completely unavailable without invoking a GPU out of memory error.

The reason for these limitations has its origin in the way computers have been designed since the very beginning of personal computing.

Processing Islands, Data Bridges and Traffic Congestion

Computers have always consisted of distinct (and discrete) components, each responsible for performing a specific task with regards to processing data. A CPU is the brain, RAM is the short-term high-performance memory, long term storage may be a hard drive or SSD, and a GPU is another type of processor suited to processing graphics data (or often other types of data with similar attributes). Each of these component’s interface with each other over some form of data bus, where data is routed and moved from one component to another.

The major advantage of this architecture is that components from different vendors can be combined and used together in a single system. A computer can be designed flexibly with the best combination of components for a specific task and price point. It also creates an environment for healthy commercial competition that fosters innovation. Components can easily swapped out for maintenance or upgrade to improve performance.

However, the disadvantage is that large amounts of data must be moved somewhat inefficiently between these components. This inefficiency has been the norm, and acceptable for as long as the tasks we expected our computers to perform fell below certain limits. As these limits are approached, the cost of higher performance components increases, often dramatically.

With the prevalence of media content creation and ever more sophisticated gaming, tasks that hit these limits are no longer niche, industrial, commercial, or professional at all for that matter. The need to process vast amounts of data incredibly quickly has become mainstream.

The result has been a huge amount of innovation in the development of ever more powerful discrete GPU’s over the past decade, and more of these massively parallel processing tasks (including crypto mining) rely largely on GPU performance.

However, the overall performance of these systems has largely become incremental and the limitations have remained.

A New Era of Integration, Efficiency and Performance

Building computer systems from discrete components has always been a necessity rather than a choice. At the performance level of desktop computers and servers, there is no single entity designing and manufacturing all the core components necessary to realize a more sophisticated level of integration… until now.

Chipsets for mobile devices, such as phones and tablets have enjoyed the power, speed and data processing efficiencies of a much higher level of integration for many years. It is no surprise that with the massive increases in transistor count and ever-increasing density of mobile processors, with ever more integration of functions once performed by discrete components, that this is the direction laptops, desktop, workstation and server hardware is headed.

One company has been at the absolute forefront of this evolution. Apple is in a unique position within the industry to pioneer the largest architectural change in personal computing since the birth of personal computing, all thanks to the iPhone.

Of course, Apple’s professional line-up of laptops and desktop computers have been missing the mark with their intended customers for many years. While they have certainly been good enough, and their users (myself included) are loyal to MacOS over the alternative, they have not outperformed an equivalent custom-built PC using similar components for substantially less cost.

It is clear that Apple recognized the inherent limitations of the Intel x86 architecture, not only in terms of processor, but the entire platform. The fundamental problem, one holding back not only Apple products, but every other computer on the market, was, is in fact, the entire architecture of the personal computer.

The solution was, and is, a tight integration between CPU, RAM, GPU, storage and sophisticated power management. How tight? So tight that much of it sits on the same slab of silicon. Built on so many generations of Apple’s highly successful SOC’s (System On A Chip) powering the iPhone and iPad.

After using the Apple M1 Pro powered Macbook Pro for more than six months for heavy video post production and photo editing, I can decidedly say that I will no longer recommend an Intel laptop to anyone for video editing or color grading.

Which Is The Best Mac for Video Editing & Content Creation

The best Mac you can buy for video editing and content creation is now whichever M1 or M2 Mac you can afford. Even the entry level M1 Macbook Air will run DaVinci Resolve, Final Cut, and the Adobe Suite with sufficient performance for 4K video editing, color grading and high resolution photography workflows.

My experience with two different 16″ Macbook Pro notebooks, both the M1 Pro with 16GB RAM and M1 Max with 32GB RAM have been almost identical in terms of noticeable performance when editing and color grading mobile video, and 4K-8K video from mirrorless cameras.

DaVinci Resolve on iPad

The power of Apple Silicon has come full circle. With Apple Silicon now powering the iPad, pro creators have a brand new tool for serious content creation on the move. Blackmagic Design have brought DaVinci Resolve to iPad.

Coming Q4 2022 here’s what Blackmagic Design have promised.

  • DaVinci Resolve for iPad will come in a free version and DaVinci Resolve Studio for iPad
  • DaVinci Resolve for iPad features support for cut and color pages
  • Optimized for MultiTouch technology and Apple Pencil
  • Supported on the iPad Pro with the M1 chip and M2 chip.
  • DaVinci Resolve delivers 4x faster Ultra HD ProRes render performance on the new iPad Pro with M2.
  • Send a clean feed grading monitor output to an Apple Studio Display, Pro Display XDR or an AirPlay compatible display
  • Open and create standard DaVinci Resolve project files which are compatible with the desktop version of DaVinci Resolve 18
  • Supported file formats include H.264, H.265, Apple ProRes and Blackmagic RAW
  • Media can be imported from the iPad Pro internal storage and Photos library, or externally connected iCloud and USB-C media disks

It is certainly an exciting time for video post production, and Apple are on the forefront of a revolution in computer architecture. It’s simply no longer the case that the most expensive, highest spec Mac is required, or that a Mac costs more than a PC with the same or higher performance.

The Mac is the best value computer for video post production that money can buy. 

Further Reading

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