Comprehensive ML Inference Benchmark Reveals PyTorch Dominance on NVIDIA, But MLX Crushes on Apple Silicon
Key Takeaways
- ▸Apple MLX achieves 3-50x faster performance than PyTorch on Apple Silicon for various ML workloads, demonstrating the value of hardware-specific optimization
- ▸PyTorch dominates NVIDIA/CUDA but requires manual CUDA graph compilation and optimization—a cumbersome workflow compared to modern graphics APIs
- ▸Rust frameworks offer superior deployment characteristics (5-32MB binaries, static executables) but lag in numerical accuracy and correctness compared to PyTorch
Summary
A detailed local inference benchmark comparing PyTorch, llama.cpp (GGML), and Rust-based frameworks (Burn, Candle, Luminal, Meganeura) across multiple hardware platforms reveals significant performance variations depending on the underlying hardware. On Apple Silicon, Apple's MLX framework dramatically outperforms PyTorch by 3-50x depending on the workload, while llama.cpp surprisingly beats MLX on LLM inference. PyTorch maintains clear superiority on NVIDIA/CUDA systems but suffers from compilation requirements (CUDA graphs) and platform fragmentation issues.
The benchmark highlights critical gaps in cross-platform support: PyTorch's Triton optimizer lacks Windows support, Intel GPU acceleration via XPU failed to work, and AMD's ROCm stack shows significant room for improvement. Rust-based frameworks offer compelling deployment advantages with minimal binary sizes (5-32MB) and simpler distribution as static executables, but currently struggle with numerical accuracy parity compared to PyTorch. The findings underscore a broader tension in the ML inference landscape between development convenience and production deployment efficiency.
- Cross-platform PyTorch support is fragmented: Triton doesn't support Windows, Intel GPU backends failed, and AMD ROCm needs optimization
- ONNX Runtime shows competitive performance on AMD platforms, suggesting alternative inference engines may be more suitable for non-NVIDIA deployment
Editorial Opinion
This benchmark reveals a critical inflection point in ML inference: the PyTorch monolith is increasingly inefficient outside NVIDIA's ecosystem, while specialized frameworks like MLX and smaller Rust alternatives prove that domain-specific optimization delivers dramatic gains. The emergence of viable 5-32MB Rust-based alternatives challenges the assumption that heavy Python frameworks are necessary for production inference, though numerical correctness remains a blocker. As hardware diversity increases—Apple Silicon, AMD iGPUs, Intel Arc—the one-size-fits-all PyTorch approach appears increasingly untenable, favoring a future where practitioners select frameworks based on target hardware rather than development familiarity.
