NVIDIA GPU or Apple Silicon for Machine Learning? The Real Tradeoffs
You have a budget and a model that you want to train, and over somewhere on the internet two groups are shouting past one another; one asserts that a Mac Studio with 192GB of memory made all the difference, while the other maintains that nothing has any impact on CUDA and that you are wasting your time looking at anything else.
Each of them is correct, yet neither one includes the aspects that would assist you in making your decision. The true situation lies in the space between the raw benchmark charts and the actual day-to-day workflow, and it is in that gap that the majority of buying advice fails.
The article examines the genuine trade-offs associated with each platform for machine learning; it looks at the strengths, as well as the sharp drawbacks that are seldom included in the initial promotional material. By the end you will be able to decide which machine suits the kind of work you are doing and what compromises you have to make in either case.
The short answer
When it comes to heavy training, large models, and any situation in which you plan to scale, buying an NVIDIA GPU is the more reliable option. It has the advantage in terms of raw computing power, benefits from mature software support, and offers effective multi-GPU scaling, as well as matching the setup used in the cloud.
Apple Silicon is able to accommodate on-device inference, as well as small and medium-sized training tasks, and models that have high memory requirements since they can make use of its large unified memory pool.
The key issue is simple: are you training at scale or are you building and running models locally?
Comparing them where it counts
Raw compute
Begin with the figures, since the gap is real. The RTX 4090 provides about 82.6 TFLOPS of FP32 computing power. The M2 Ultra, which is Apple’s most powerful chip, achieves around 27 TFLOPS. That is roughly one-third of the throughput.
The gap becomes more significant at the levels of precision that are important for training. The 4090 achieves around 166 TFLOPS in FP16 via its Tensor Cores, whereas Apple’s GPU performs FP16 at a rate close to that of its FP32 performance. When moving on to datacentre chips the gap increases once more, the A100 reaching 312 TFLOPS in TF32 and the H100 going well beyond.
As for pure mathematical operations per second, NVIDIA is not at all matched. That one fact is what leads most of the rest of this discussion.
Under the hood
The differences begin in the way the chips are manufactured. The RTX 4090 includes 16,384 CUDA cores together with 512 dedicated Tensor Cores which handle the matrix mathematics at the core of training, while the Apple M2 Ultra combines a 76-core GPU with a 32-core Neural Engine, its specialized inference component.
Image Credits: Amazon
Image Credits: Apple
They are further divided by precision support: NVIDIA graphics cards are capable of running FP32, TF32, FP16, BF16, and INT8 at high speed, while Apple’s GPU only supports FP32, FP16, and INT8, BF16 being available only on the CPU side and thus causing a step to be lost when using a training recipe designed for BF16 on a Mac.
It is completed by bandwidth: the A100 handles data at nearly 2 TB/s and the 4090 at about 1 TB/s, compared with 800 GB/s for the M2 Ultra. Although that figure is high for a single system it is still a little short of the amount that the discrete cards supply to their computing units.
Memory, where Apple flips the script
The situation is now the opposite: the RTX 4090 has 24GB of VRAM, while a high-end M2 Ultra provides up to 192GB of unified memory, shared between the CPU and the GPU, at a speed of 800GB/s.
The fact that the model fits within GPU memory is more important than the raw computing power difference for a particular task. When this is the case, the two systems compete with each other. In a test using a model of the same size as GPT-2, the M2 Ultra and the NVIDIA A6000 completed a training pass in almost the same amount of time, each taking about 2.7 to 2.8 seconds.
If you try to run the model beyond the memory capacity of the NVIDIA card, the situation alters. In that case, the GPU has to page data in and out or rely on offloading techniques, which causes a drop in performance. The Mac, on the other hand, just carries on since it never runs out of memory. When it comes to running very large models on a single machine, that extra space is indeed an advantage.
It’s here that the figure becomes practical: a 192GB unified memory pool is large enough to accommodate a large language model which would never fit on a 24GB card, so you can load and fine-tune it on a single quiet desktop rather than distributing it among several cards. For that particular but increasingly common application, Apple carries out something that no single consumer NVIDIA card is able to do.
The software that runs it
It is here that NVIDIA turns a competitive advantage into a strong barrier to entry. CUDA, cuDNN, and cuBLAS have been finely tuned over the years, and all the major frameworks set them as their default destination. PyTorch, TensorFlow, JAX, and ONNX are all able to activate an NVIDIA GPU without any trouble.
Apple has gained a lot of ground, with PyTorch now offering a Metal backend, TensorFlow having a Metal plugin, and JAX having one as well. All of the major frameworks are now compatible with Apple Silicon.
The problem is that the software has to be mature. Certain operations still revert to the CPU, and actual tests have demonstrated that PyTorch when using Apple’s Metal backend runs between two and five times more slowly than when it is used with CUDA.
There is also the Neural Engine, Apple’s specialised inference processor, which has a rating of nearly 31.6 TOPS on the M2 Ultra; it is efficient, but instead of integrating directly with PyTorch it hides behind Core ML, so most training workflows never make use of it.
Scaling and the cloud
NVIDIA was designed with the aim of enabling multiple cards to work together; by using NVLink, several cards can be linked, and libraries such as NCCL take care of communication between GPUs and between nodes. Distributed training is a problem that has already been solved and one that is well established.
Apple Silicon doesn’t provide a solution in this case: there is no support for multiple GPUs, no option for an external GPU, and no GPU access within Docker containers since Metal doesn’t pass through. Each Mac consists of a single chip, and scaling involves manually connecting several Macs together.
The cloud seals it. You can rent NVIDIA A100 and H100 instances by the hour from every major provider. No cloud offers Apple GPUs at all. If your local machine needs to mirror what production runs, that alone can make the decision for you.
Tooling and daily workflow
Every day the gaps in the workflow become apparent. NVIDIA integrates with the standard toolkit, allowing you to start GPU containers using nvidia-docker, monitor the load with nvidia-smi, and use the well-established Nsight tools to profile, together with most Python packages installing themselves as GPU-ready by default.
With a Mac the available path is more limited. Since Docker containers can’t access the Metal GPU, pipelines based on containers have to use the CPU. Most pip and conda builds on macOS stick to the CPU and require special installations just to be able to access the GPU.
The approach relies on Xcode Instruments rather than on the tools which most ML engineers are already familiar with; the ecosystem is also closed since it lacks open GPU drivers and has no equivalent to low-level libraries such as TensorRT or NCCL. While this does not prevent the work from being carried out, it does introduce some friction that a Linux and NVIDIA setup avoids.
Power and heat
Efficiency is Apple’s real achievement; during the same GPT-2 training pass the M2 Ultra consumed about 115 joules per iteration compared to the 212 used by the A6000. The Apple chips use only 15 to 50 watts, stay cool and remain very quiet.
High-end NVIDIA cards use hundreds of watts and therefore produce a lot of heat, requiring effective cooling and resulting in noticeable fan noise.
There’s a twist: in the same test, the 4090 achieved about 108 joules per iteration and thus outperformed the Mac in terms of efficiency per unit of work, even though it uses a lot of power overall. However, for a desktop computer that is always on or any battery-powered device, Apple is still clearly ahead.
What it feels like in real tasks
Numbers are more effective when they apply to a specific example. Consider image generation: a MacBook Pro equipped with an M1 Max takes about 10 seconds to produce a single 512 by 512 image using a diffusion model, while an RTX 4090 carries out the same task in about 1 second.
There’s a tenfold difference when it comes to a task that people carry out all the time, and this shows the entire situation in a condensed form. The Mac completes the job quietly and on battery power, while the NVIDIA card finishes it before you have had time to look up from the keyboard.
Smaller training jobs present a more favourable picture. A vision transformer completed in about four minutes on an M2 Mac as compared to eleven minutes on a CPU alone, which shows that the Mac’s GPU does provide a benefit for lighter tasks. A discrete NVIDIA card would reduce the time further.
The same thing can be said on a larger scale: small and medium-sized models can be trained at a reasonable speed on a Mac, which is sufficient for actual prototyping. However, when the batches become larger and the training runs last for hours or days, the difference in throughput becomes a gap that you experience every single day.
The sharp edges on each
Both platforms have their troubles, and the fair compromises apply in both directions.
On NVIDIA, a consumer 4090 caps at 24GB of VRAM, so a model that grows too large forces offloading or a smaller batch. Stepping up means datacenter cards near $30,000 or renting in the cloud. A PC is also a project you build, update, and maintain, on top of the power draw and heat above.
With Apple the value proposition is poor. An M2 Ultra machine is priced almost at $6,600, whereas a more powerful 4090 is available for about $1,600. The GPU is built into the chip, meaning that it can’t be upgraded later, and the cheapest fanless Macs reduce their speed when under continuous load. Together with the slower training and the fact that the software is still developing, the limitations are genuine.
Where NVIDIA wins
Choose NVIDIA if the task is heavy, if it is growing, or if it is heading towards production. Raw throughput is the main point. The chip has two to three times the computing power of Apple’s most powerful processor, and even more when you take into account the Tensor Cores that mixed-precision training depends on.
The software handles this. Since CUDA is the language used throughout the field, new models, little-known libraries, and the latest research code generally run well and most efficiently on NVIDIA. Rather than spending time fixing broken dependencies, you can focus on training. Deployment tools such as TensorRT provide additional inference speed, while the profilers show the bottlenecks in great detail.
There’s also the question of scale: add a second card, then a fourth, and then an entire node, and the tooling is included as well. Once you outgrow your desk, the same code can be run on rented A100 and H100 instances with only minor changes.
The platform that the benchmarks and the industry are based on is the one that is used for serious training of large CNNs, diffusion models, and language models.
Where Apple Silicon wins
Take Apple’s option when the task is local, memory-intensive, or power-sensitive. The major feature here is the huge block of unified memory. With a single machine you can hold models which would never fit on a 24GB card, there being no need for any offloading tricks.
The second reason is efficiency. These chips provide strong performance per watt, operate silently, and remain cool, making them suitable for a machine that runs all day on your desk or carries out inference continuously. They don’t need to be fitted with a space heater or have jet-engine fans.
The Neural Engine provides an advantage when it comes to inference. It is able to speed up quantized, Core ML models more than the GPU can, which is beneficial when you incorporate on-device features into Mac and iOS apps.
The entire package comes down to this: it’s a compact and well-made computer running a stable Unix environment, featuring tight integration with Core ML and requiring no assembly. When it comes to prototyping, fine-tuning both small and medium models, on-device inference, and demonstrations, the Mac is a pleasant and capable tool, even though it uses a relatively small amount of electricity.
The bottom line
Choose the right machine for the job and the decision becomes simple. When you are training large models, need mature software, or intend to scale up to more GPUs or move to the cloud, an NVIDIA GPU is by far the best choice. In return for that power you have to take on the power consumption, the heat generation, and the effort involved in building it.
If you want to use the machine locally, run memory-intensive models, or prefer a quiet, efficient, all-in-one unit, then Apple Silicon is a good option. The drawback, however, is that training is slower and there is a limit which cannot be increased.
For heavy ML work the answer leans NVIDIA. For local, efficient work, Apple has earned its seat.
