Windows host VS Mac Studio at the same price, which one is better in AI reasoning

Written by

Caleb Hayes

Updated on:July-02nd-2025

We chose two consoles priced at 30,000 yuan as comparison objects: HP Shadow Elf 11 360 water-cooled gaming console (29,000 yuan after JD.com subsidy) VS Mac Studio M4 Max (16+40 cores) 128G 2T (30,000 yuan after JD.com subsidy), the prices are similar. Let's take a look at their configurations:

HP Omen 11 360 Water-Cooled Gaming Desktop‌

‌Processor‌: Intel Ultra 9 285K (24 cores, 5.70 GHz turbo)‌

‌Graphics card‌: NVIDIA RTX 4090D (24GB GDDR6X video memory, supports DLSS 3.5/ray tracing)‌

‌Memory‌: 64GB DDR5 (Bandwidth estimated ≥80GB/s)‌

‌Storage‌: 2TB SSD + 2TB mechanical hard drive‌

‌Cooling‌: 360mm water cooling system‌

‌Apple Mac Studio M4 Max‌

‌Processor‌: M4 Max (16-core CPU + 40-core GPU, second-generation 3nm process)

‌Memory‌: 128GB unified memory (bandwidth 273GB/s)

‌Storage‌: 2TB SSD

‌Power consumption‌: Peak value <100W (fanless design, silent operation)

AI Reasoning Performance Analysis‌

‌1. Computing power and video memory/memory‌

‌RTX 4090D‌: Single-precision floating-point computing power is approximately ‌82 TFLOPS‌, video memory bandwidth ‌936GB/s‌, and 24GB of video memory can meet the full loading of small and medium-sized models (such as 10B parameters); but 30B+ parameter models need to be processed in batches, which is easy to cause delays‌.

‌M4 Max‌: GPU computing power is about ‌40 TFLOPS‌ (estimated), but 128GB unified memory can fully load 70B+ parameter models, avoiding video memory bottlenecks‌.

‌2. Energy efficiency and deployment cost‌

‌M4 Max‌: Low power consumption (<100W) is suitable for long-term inference tasks without the need for additional cooling investment‌.

‌RTX 4090D‌: Peak power consumption ≥ 450W‌, requires reliance on a water cooling system to maintain stability, and has high long-term operating costs.

‌3. Framework Adaptation‌

‌RTX 4090D‌: Supports mainstream frameworks such as CUDA and TensorRT, has wide compatibility, and is suitable for developers who need fast iteration‌.

‌M4 Max‌: Relies on Apple ecosystem tools such as MLX, with a high degree of optimization but limited flexibility (for example, some PyTorch functions need to be adapted)‌.

in conclusion

‌Small model reasoning (<30B parameters)‌:

RTX 4090D has obvious speed advantages thanks to its higher computing power and mature ecosystem.

‌Local deployment of large models (>50B parameters):

The M4 Max's 128GB unified memory avoids video memory limitations and provides higher overall throughput‌.

Energy efficiency and quietness requirements:

The M4 Max fanless design is more suitable for office/home environments‌.

‌Final Recommendation‌:

Mac Studio M4 Max is preferred: if you need to deploy ultra-large-scale models (such as 70B+) and require low power consumption and quietness.

Give priority to ‌OMNER 11‌: if you rely on the CUDA ecosystem, process small and medium-sized models, or have gaming/rendering needs‌.