Which is faster: RTX4090 or M3 Max in machine learning?

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RTX4090 vs M3 Max – Machine Learning

RTX4090 vs M3 Max – Machine Learning – Which is Faster?

Machine learning has become an essential tool in various industries, from finance to healthcare to entertainment. As organizations continue to invest in machine learning algorithms and models, the need for high-performance computing hardware becomes increasingly important.

Two popular choices for machine learning workloads are the RTX4090 and M3 Max. Both are powerful GPUs that are designed to handle complex computations and advanced neural network training. But which one is faster when it comes to machine learning tasks?

RTX4090

The RTX4090 is a flagship GPU from NVIDIA, known for its exceptional performance in gaming and professional applications. It features a powerful architecture with advanced tensor cores that are optimized for machine learning workloads. With a high core count and large memory capacity, the RTX4090 is capable of handling large and complex models with ease.

M3 Max

The M3 Max is a high-performance GPU from a lesser-known manufacturer, but it has gained attention for its impressive performance in machine learning tasks. It features a custom architecture with dedicated cores for matrix operations, making it well-suited for deep learning and neural network training.

Performance Comparison

When it comes to raw performance, the RTX4090 and M3 Max are both capable of handling demanding machine learning workloads. However, in benchmark tests, the RTX4090 has shown to outperform the M3 Max in various tasks, including image recognition, natural language processing, and reinforcement learning.

The RTX4090’s advanced tensor cores and larger memory capacity give it an edge over the M3 Max when it comes to processing complex machine learning models. This makes it the preferred choice for organizations and researchers who require the highest level of performance for their work.

Conclusion

While the M3 Max is a capable GPU for machine learning tasks, the RTX4090’s superior performance and advanced features make it the faster choice for handling complex and demanding workloads. For organizations and professionals seeking the best performance for their machine learning projects, the RTX4090 is the recommended choice.

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@hardboiled7467
8 months ago

I can't even hear the fan noise over the sounds of raging apple fans… 😆

@briancase6180
8 months ago

Sure. Twice as much performance for floating-point calculations. But, what is the power dissipation? How well does the Nvidia GPU do when there's only battery power? The Apple GPU doesn't require wasteful memory-to-memory copies to get workloads from the CPU memory to GPU memory and back. And, if your ML model is quantized, the performance difference might not be so dramatic. More than likely, memory constraints on the Nvidia GPU will require a quantized model….

@MaxMustermann-vy7ur
8 months ago

Now do it unplugged!

@GShockWatchFan.
8 months ago

In the real world with all the variables considered, the Mac for serious applications will beat any pc anytime.

@RSV9
8 months ago

That's what I'm saying all the time 🤷‍♂️

@diyarabdullah9518
8 months ago

The biggest issue with windows laptops is that you can’t actually use them as a laptop😂

@sajjadsarkoobi
8 months ago

The only issue is regarding memory and bandwidth, so it is a trade off. Im training an object detection model that needs almost 30 GB of memory, in that case m3 max will help but slower which 30GB memory in 4090 just possible if we have two of it.
So for speed still can use Colab A100 that has 40GB memory and high TF. And use m3 max just for testing coding and running inference.

@jingzheshan
8 months ago

Lots of heat and lots of noise 😂

@zerogravityfallout4228
8 months ago

How about a desktop 4090? Would a 4080 super even make a difference since it's just a mobile 4090 with more wattage access

@tyestrains
8 months ago

But the PC is plugged into the wall! lol 😆

@AlexanderAk
8 months ago

Mobile rtx 4090 even is not close to descktop 4090

@anothernewsnetwork
8 months ago

Great video I have a question. Is there any way that you could get an external graphics card to run reliably on a Mac?

If so I'm wondering what the difference would be then

@DK-ox7ze
8 months ago

I have always wondered why nobody uses the NPU on Apple silicon? It's supposed to be much faster than the GPU for ml tasks. And pytorch supports that I believe if you do some config changes.

@Epicgamer_Mac
8 months ago

The Nvidia GPU’s always have higher teraflops than Apple’s. Doesn’t mean they’re faster, in many real world tasks the Mac can still crush the PC.

@julian_handpan
8 months ago

But what about the power consumption? 😌

@alejandromartinezramirez3312
8 months ago

What in terms of CPU and RAM?

@slaviboy
8 months ago

I can hear the fans from miles away :d

@RedDragon72q
8 months ago

Still short on GPU memory so small model training is about it.

@sasca854
8 months ago

Amazing computational performance, but unfortunately impeded by traditional discrete memory architecture. Apple's unified arch is definitely enticing in a lot of ways.

@danjietang8427
8 months ago

Can we try training a PyTorch model using both m3 max and 4090? Really curious to see how they perform