A recent development in the .NET ecosystem shows that writing CUDA kernels in pure .NET is not only possible but also highly performant. TensorSharp, a pure .NET inference engine, has integrated the newly released GLM-5.3-Flash and Qwen3.8-Flash-Next models, achieving 2x decode performance compared to llama.cpp. This is a significant milestone for .NET developers who want to embed LLM inference directly into their applications without relying on native C++ libraries. The performance gain is attributed to hand-written CUDA kernels in .NET, which can be optimized for specific model architectures. For teams already invested in the .NET stack, this opens up new possibilities for building AI-powered products with tighter integration and potentially lower latency. The open-source nature of the models (MIT license for GLM-5.3-Flash) further lowers the barrier to adoption.
TensorSharp, a pure .NET inference engine, achieves 2x decode performance over llama.cpp for GLM-5.3-Flash, proving .NET's viability for high-performance LLM inference.