GPU vs Apple Silicon: RTX 3090/4090/5090 TFLOPS vs M3/M4/M5 Max — LLM Inference Performance Comparison

GPU vs Apple Silicon: RTX 3090/4090/5090 vs M3/M4/M5 Max — LLM Inference Performance Comparison

Hardware Specifications and Peak TFLOPS

When running large language models locally, the choice of hardware significantly impacts performance. The table below compares key specifications of NVIDIA RTX GPUs and Apple M-series Max chips.

Prompt Processing (Filling) Performance at Different Context Sizes

Prompt processing, also known as prefilling, involves encoding a large input context. This phase is compute-bound. NVIDIA GPUs excel due to their high tensor core TFLOPS. The following table shows approximate time in seconds to process context sizes of 50k, 100k, 250k, and 500k tokens. Lower is better. Estimates assume a 13B parameter model using FP16.

Decoding Speed (Token Generation)

After prompt processing, the model generates tokens one by one. This is memory-bandwidth-bound. For models that fit within VRAM, NVIDIA GPUs are significantly faster. However, when a model exceeds VRAM, Apple's unified memory architecture maintains consistent speed without paging slowdowns.

Unified Memory Advantage: Single M5 Max vs Multiple RTX 3090s

Apple's unified memory allows a single laptop to hold models with up to 128 GB of parameters. To match this capacity with NVIDIA, multiple GPUs are required. For example, five RTX 3090s provide 120 GB total VRAM, but this comes with significant trade-offs.

Power Consumption, Heat, and Portability

The MacBook Pro M5 Max consumes only 30–60 W under typical LLM inference load, producing minimal heat and zero fan noise. In contrast, a single RTX 4090 draws up to 450 W, requiring robust cooling that generates noticeable noise. The multi-GPU option is even more demanding. For users who need to run large models on the go, the MacBook Pro is the only viable choice. For stationary setups where maximum tokens per second is the priority, NVIDIA remains unbeaten.

Conclusion

Choose NVIDIA RTX GPUs if your models fit within 24–32 GB of VRAM and you prioritize raw decoding speed and prompt processing throughput. Choose Apple M-series Max (especially M5 Max with 128 GB) if you need to run large models (70B+ parameters) locally, require portability and silence, or value power efficiency. The unified memory architecture of Apple gives a decisive advantage for large-scale local inference at the cost of lower peak performance.