Apple Silicon vs Nvidia
The "Memory Wall" has officially been demolished. In this video, we put the Apple M4 Ultra through the ultimate AI stress test. While NVIDIA dominates in raw tokens-per-second for smaller models, the M4 Ultra’s 32-core Neural Engine and support for up to 512GB of Unified Memory have made it the only choice for researchers running 400B+ parameter models on a single desktop.
We break down the 2026 performance data:
The Neural Engine Leap: We benchmark the 32-core ANE (2x M4 Max) in Core ML and MLX. Discover why the M4 Ultra is delivering 3x the AI throughput of the M1 Ultra in real-world stable diffusion and whisper-transcription tasks.
Unified Memory vs. VRAM: We demonstrate "The Impossible Load"—running Llama 3.1 405B (Q4 quantization) entirely in memory. We compare this to the multi-GPU "headaches" required to achieve the same on Windows.
MLX Optimization: A look at how Apple’s MLX framework has matured in 2026, narrowing the gap with CUDA and allowing the M4 Ultra to achieve over 20 tokens/second on 70B models.
The Efficiency Paradox: Measuring the M4 Ultra’s 190W peak draw against an RTX 5090 system’s 700W+. Is the "silent performance" worth the "Apple Tax"?
Whether you're an AI researcher, a creative pro using Generative Fill, or a developer building local-first agents, this is the definitive benchmark guide for Apple's most powerful silicon to date.
Configure your AI-native Mac Studio at: 👉 https://kaizenapps.com