Great efforts can bring about miracles. What can Apple do with the Mac Studio M3 Ultra that costs 100,000 yuan?

Apple will release Mac Studio with M4 Max chip and M3 Ultra chip on March 12.

As a mini desktop computer, the M3 Ultra chip version of Mac Studio now supports a high-end configuration, with an optional Apple M3 Ultra chip with a 32-core CPU, an 80-core GPU, and a 32-core neural network engine, 512GB of unified memory, and a luxurious configuration of a 16TB solid-state drive. The price is 108,749 yuan, not including peripherals and monitors. The M4 Mac mini next to it is 2999 yuan after subsidies, trembling.

So, if the M4 Mac mini is overkill for some people, what can the M3 Ultra Mac Studio do? The official answer is:

Reinventing the studio - This Mac Studio can connect to 8 Pro Display XDR monitors at the same time, presenting more than 160 million pixels;

Running large AI models - Its token generation performance using large language models with hundreds of billions of parameters is 16.9 times that of M1 Ultra.

M3 Ultra, this chip not only refreshes the performance ceiling of Mac, but also sets its sights on the forefront of AI development. With 512GB of unified memory and over 800GB/s bandwidth, can it become the desktop computing king in the era of large models?

Let’s first take a look at the basic specifications of the M3 Ultra.

The M3 Ultra uses Apple's innovative UltraFusion packaging architecture, integrating two M3 Max chips through more than 10,000 high-speed connection points to provide low-latency and high-bandwidth transmission capabilities. This allows the system to recognize the connected chips as the same complete chip, achieving surging performance while maintaining Apple's industry-leading energy efficiency. The M3 Ultra integrates a total of 184 billion transistors, taking the new Mac Studio's industry-leading performance to a new level.

The CPU has up to 32 cores (24 performance cores + 8 efficiency cores), which is a significant improvement over the M2 Ultra (20-core CPU); the GPU has up to 80 cores, and the graphics performance is about 20%-30% higher than the M2 Ultra (up to 76-core GPU).

Unified memory supports up to 512GB and memory bandwidth exceeds 800GB/s. The unified memory architecture of M3 Ultra integrates unparalleled high-bandwidth, low-latency memory in personal computers. It surpasses the most advanced workstation graphics cards currently available, breaking the limits for professional workloads that require large amounts of video memory, such as 3D rendering, visual effects, and AI.

512GB unified memory is sufficient to load ultra-large-scale language models (LLMs), such as models with 600 billion parameters, which is far more than traditional GPUs (such as the 32GB video memory of RTX 5090). Although the bandwidth is high, the inference speed may not be superior to that of professional GPUs (such as H100's 3TB/s), which is suitable for development rather than large-scale deployment .

The M3 Ultra only takes a few seconds to load a 600 billion parameter LLM. Although the inference speed is not as fast as the H100, the efficiency is amazing when the single machine power consumption is only 300W. Compared with the high power consumption of NVIDIA GPU, the low heat and silent design of the M3 Ultra are more suitable for desktop environments. For independent developers, it is almost a perfect local AI workstation.

The heat dissipation problem caused by the extremely small body of the M4 Mac mini has been well solved by Apple through excellent heat dissipation design. Mac Studio also has excellent heat dissipation design to ensure the sustainability of high-performance operation and a quiet desktop working environment.

M3 Ultra brings support for Thunderbolt 5 ports to Mac Studio, with data transfer speeds up to 120 Gb/s, more than 2x faster than Thunderbolt 4. Each Thunderbolt 5 port is directly powered by a dedicated custom controller on the M3 Ultra chip. This allows each port on Mac Studio to be allocated its own dedicated bandwidth , making it an industry-leading Thunderbolt 5 solution.

Thunderbolt 5 can also connect multiple Mac Studio systems to work together to form a service cluster to achieve workflows that challenge the limits of content creation and computer science exploration.

As a match, Mac Studio (M3 Ultra) provides 6 full-speed Thunderbolt 5 ports (4 on the back and 2 on the front), compatible with USB4 120Gb/s, USB 3 10Gb/s, DisplayPort 2.1, and provides two additional USB 3 (USB-A) ports compatible with ordinary devices, with a maximum speed of 5Gb/s. It also comes standard with a 10Gb (10 Gigabit) Ethernet port and an SDXC card slot (UHS-II).

Of course, with the high-speed transmission of the Thunderbolt 5 interface, you can also cut off Apple's "golden storage" and downgrade from the optional 16TB to 1TB, which can save 34,500 yuan. It is also possible to expand the capacity externally through a Thunderbolt 5 high-speed hard drive enclosure.

The parallel video processing capabilities of the media processing engine in the M3 Ultra are greatly enhanced. The chip provides a dedicated hardware-accelerated H.264, HEVC and four ProRes codec engines, capable of playing up to 22 8K ProRes 422 video streams.

In the 8K video editing test, the M3 Ultra's 80-core GPU and large memory reduced the export time by 30%, and its multi-core performance improvement was particularly obvious compared to the M2 Ultra. However, in the face of ultra-high-load 3D rendering, bandwidth bottlenecks may still appear.

Finally, user experience and ecosystem are Apple's plus points.

MLX (Machine Learning X) is an open source machine learning framework designed by Apple for M series chips, aiming to make full use of the hardware features of chips such as M3 Ultra. Its seamless integration with macOS is first reflected in the deep optimization of the unified memory architecture and neural network engine (NPU).

The MLX framework is like an 'AI brain' tailored for the M3 Ultra, which maximizes the potential of 512GB of unified memory and 64-core NPU. In the macOS environment, loading a 130 billion parameter model only requires a few lines of code, and data does not need to be transferred across devices, which is much more efficient than the traditional GPU development process.

The integration of MLX with macOS is not only technical, but also simplifies the user experience, which is especially important for AI developers and professional users.

In macOS 15, MLX is pre-installed as a system-level tool. Users only need to install the Python package through pip to start development, without the need to configure complex CUDA or drivers. Compared with the cumbersome environment setup in NVIDIA GPU development, MLX's "install-and-use" greatly reduces the entry threshold.

MLX supports integration with Xcode, allowing developers to debug models and monitor performance in a familiar IDE. The hardware status of the M3 Ultra (such as NPU usage and memory usage) can also be displayed in real time through the macOS Activity Monitor, making the development process more intuitive.

The ecological characteristics of macOS make it easy to embed MLX models into professional software such as Final Cut Pro and Logic Pro. For example, an MLX-based audio enhancement model can run directly in Logic Pro, using the M3 Ultra's NPU to process audio streams in real time.

For users who are not familiar with GPU programming, the charm of MLX lies in its simplicity. Installing MLX on macOS is like installing an app. With the debugging tools of Xcode, you can run a local LLM in a few minutes. Even in Logic Pro, you can use it to optimize audio tracks in real time. The convenience of ecological collaboration is amazing.

Imagine an independent developer who uses MLX to fine-tune a 7-billion-parameter conversational model on the M3 Ultra. The 512GB memory easily accommodates the data, the NPU accelerates the training, and the macOS terminal provides real-time progress feedback. The whole process is as smooth as silk. Compared with the high cost and complex configuration of the cloud, this localized experience is undoubtedly a new benchmark for large model development.

Of course, MLX is not perfect. Its deep binding to the Apple ecosystem means that cross-platform migration is a challenge, and compared with the old frameworks, its community resources need to be enriched. But for macOS users, these flaws are insignificant in the face of a seamless experience.