Unlock the Raw Power of .NET for AI and High-Frequency Computing
Are you hitting the performance wall in your C# applications? Do Garbage Collection pauses kill your real-time AI inference latency? In the age of Large Language Models, standard coding practices aren't enough. You need to go deeper.
High-Performance C# for AI is not a beginner's guide—it is a masterclass in squeezing every nanosecond of performance out of the .NET runtime. This volume bridges the gap between high-level application development and low-level systems programming, equipping you with the tools to build blazing-fast tokenizers, tensor processors, and inference engines.
In this volume, you will master:
- Zero-Allocation Architectures: Stop feeding the Garbage Collector. Learn to use Span<T>, Memory<T>, and ref structs to process massive data streams with zero heap allocations.
- SIMD & Vectorization: Unleash the full potential of your CPU. Implement hardware-accelerated math (AVX2/AVX-512) for operations like Cosine Similarity and Softmax, achieving 10x speedups over standard loops.
- Advanced Memory Management: Dive into the internals of the GC, the Large Object Heap (LOH), and learn how to use ArrayPool and stackalloc to manage memory manually and safely.
- Unsafe Code & Interop: Learn the art of using pointers, the fixed statement, and memory pinning to interface directly with native libraries and raw memory buffers.
- Scientific Profiling: Move beyond guesswork. Use BenchmarkDotNet and dotnet-trace to measure, diagnose, and prove your performance gains with statistical rigor.
Whether you are building a local LLM runner, a high-frequency trading bot, or a real-time data processing pipeline, this book provides the architectural patterns and low-level techniques required to compete with C++ and Rust.
Stop waiting for the Garbage Collector. Take control of your memory. Build the next generation of AI infrastructure in C#.
Check also the other books in this series
