🚀 8x Faster Than ONNX Runtime: Zero-Allocation AI Inference in Pure C#
The Myth: "C# is too slow for AI" For years, the narrative has been the same: if you want high-performance AI, you must use C++ or Python wrappers (like PyTorch/ONNX) that call into native kernels....
Source: dev.to
The Myth: "C# is too slow for AI" For years, the narrative has been the same: if you want high-performance AI, you must use C++ or Python wrappers (like PyTorch/ONNX) that call into native kernels. The common belief is that the Garbage Collector (GC) and the overhead of the "managed" environment make C# unsuitable for ultra-low latency inference. I decided to challenge that. By leveraging the latest features in .NET 10, AVX-512 instructions, and strict Zero-Allocation patterns, I built Overfit — an inference engine that outperforms ONNX Runtime by 800% in micro-inference tasks. 📊 The Results: 432 Nanoseconds The following benchmark compares Overfit against Microsoft.ML.OnnxRuntime. While ONNX is a powerhouse for large models, its overhead becomes a bottleneck for micro-inference. Environment: CPU: AMD Ryzen 9 9950X3D (Zen 5, AVX-512) Runtime: .NET 10.0 (X64 RyuJIT x86-64-v4) Task: Linear Layer Inference (784 -> 10 units) Method Mean Latency Allocated Ratio Overfit (ZeroAlloc) 432.0
