CanItRun Logocanitrun.

NVIDIA RTX 5090 vs Apple M4 Ultra (192GB)

Side-by-side local AI comparison — VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 70 open-weight models.

Quick verdict

Apple M4 Ultra (192GB) wins for local AI inference. It has 160 GB more VRAM and -39% more memory bandwidth, runs 64 models natively (vs 47), and exclusively fits 17 models the other cannot. Note: NVIDIA RTX 5090 uses CUDA while Apple M4 Ultra (192GB) uses METAL — software ecosystem matters for your framework.

Specs comparison

SpecNVIDIA RTX 5090Apple M4 Ultra (192GB)
VRAM32 GB192 GB unified
Memory typeGDDR7LPDDR5X
Bandwidth1792 GB/s(+64%)1092 GB/s
CPU cores32 (24P + 8E)
ArchitectureBlackwellApple M4 Ultra
BackendCUDAMETAL
TierConsumerWorkstation
Released20252025
Models (native)4764

Estimated tokens per second

Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.

ModelNVIDIA RTX 5090Apple M4 Ultra (192GB)Delta
Llama 3.3 70B Instruct(70B)85.3 t/s(Q2_K)7.8 t/s(FP16)+994%
Qwen 3.6 27B(27B)88.5 t/s(Q6_K)20.2 t/s(FP16)+338%
Llama 3.1 8B Instruct(8B)112 t/s(FP16)68.3 t/s(FP16)+64%
Qwen 2.5 7B Instruct(7.6B)117.9 t/s(FP16)71.8 t/s(FP16)+64%

Delta is NVIDIA RTX 5090 relative to Apple M4 Ultra (192GB).

Only NVIDIA RTX 5090 can run(0)

No exclusive models — Apple M4 Ultra (192GB) can run everything NVIDIA RTX 5090 can.

Only Apple M4 Ultra (192GB) can run(17)

Both run natively(47)

These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.

Which should you choose?

Choose NVIDIA RTX 5090 if:
  • • Faster token generation is the priority
  • • You rely on CUDA-based tools (PyTorch, vLLM, Ollama)
Choose Apple M4 Ultra (192GB) if:
  • • You need to run larger models (>32 GB VRAM)
  • • You're on macOS and want native Metal acceleration (MLX, llama.cpp)
  • • Unified memory matters (CPU/GPU share the same pool — no data copy overhead)

Frequently asked questions

Which is better for local AI, the NVIDIA RTX 5090 or Apple M4 Ultra (192GB)?
For local AI inference, the Apple M4 Ultra (192GB) has the edge. It offers 192 GB VRAM (vs 32 GB) and 1092 GB/s bandwidth (vs 1792 GB/s), letting it run 64 models natively in VRAM vs 47 for its rival.
How much VRAM does the NVIDIA RTX 5090 have vs the Apple M4 Ultra (192GB)?
The NVIDIA RTX 5090 has 32 GB of GDDR7 at 1792 GB/s. The Apple M4 Ultra (192GB) has 192 GB of LPDDR5X at 1092 GB/s. The Apple M4 Ultra (192GB) has 160 GB more VRAM, allowing it to run 17 models the NVIDIA RTX 5090 cannot fit natively.
Can the NVIDIA RTX 5090 run Llama 3.3 70B?
Yes. The NVIDIA RTX 5090 runs Llama 3.3 70B natively at Q2_K quantization at approximately 85.3 tokens per second.
Can the Apple M4 Ultra (192GB) run Llama 3.3 70B?
Yes. The Apple M4 Ultra (192GB) runs Llama 3.3 70B natively at FP16 quantization at approximately 7.8 tokens per second.
What is the difference between the NVIDIA RTX 5090 and Apple M4 Ultra (192GB) for AI?
The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 5090 has 32 GB VRAM at 1792 GB/s (CUDA backend). The Apple M4 Ultra (192GB) has 192 GB VRAM at 1092 GB/s (METAL backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX 5090 runs 47 models natively vs 64 for the Apple M4 Ultra (192GB).
Full NVIDIA RTX 5090 page →Full Apple M4 Ultra (192GB) page →Check your hardware →