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One .NET 10 project shows that a local LLM inference engine can be written in C# and published as an approximately 15 MB Native AOT executable, without bundling Python or a conventional C++/CUDA runtime stack. That is a project-specific claim, not proof that local LLMs generally no longer need C++ or Python. The reported engine still uses native GPU interfaces, and its model files are separate, much larger downloads.
What “no C++ or Python” means in this project
In a DEV Community article, C# developer Ian Cowley describes Glacier.Inference, a .NET 10 engine that loads GGUF model files. The inference application is written in C# and published with Native AOT, rather than being a Python application or a C++ inference program packaged with its usual runtime stack.
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That distinction concerns the application and its deployment, not every component involved in inference. Cowley’s account describes calls to the CUDA Driver API through nvcuda.dll and a Direct3D 12 compute path. Those are native interfaces. The evidence supports a specific way to build an inference engine without bundling a conventional C++/CUDA runtime stack; it does not establish that all native dependencies disappear or that every model and GPU can use this approach.
The DEV article’s search result displays a September 13 publication date but does not show a year. Its implementation details and measurements are author-reported; independent code review or reproducibility evidence is not established here.
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What Native AOT changes at deployment
Microsoft Learn describes Native AOT as compiling an application to native code at publish time. A Native AOT app does not use a just-in-time compiler while it runs, and the result is a self-contained executable for a specific runtime environment. “Self-contained” does not mean one binary works across operating systems and processor architectures.
Native AOT also imposes compatibility requirements. Microsoft documents that publishing requires trimming and identifies limits that include dynamic loading—such as Assembly.LoadFile—and runtime code generation such as System.Reflection.Emit. Developers need to check library compatibility and address publish-time analysis warnings; code that depends on unsupported patterns may need changes or may not be suitable for this deployment model.
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What the reported 15 MB executable includes—and excludes
The approximately 15 MB figure is Cowley’s reported size for the Glacier.Inference executable, not a size guaranteed by .NET 10 or independently verified by Microsoft. It is not the size of a complete local LLM installation: the GGUF model is stored separately, and the article’s RTX 4060 example uses a 4.68 GB model.
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Native AOT can produce compact deployments, but a project’s final size depends on its application code, dependencies, runtime libraries, included symbols, and publishing choices. Microsoft’s size guidance describes a trade-off: optimization choices can favor smaller output or faster execution. A published size should therefore be treated as a property of that project’s build, target, and configuration, not a general .NET benchmark.
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What the article reports about performance
Cowley reports separate examples on different hardware, with different models and execution setups. They are useful as descriptions of the project, but not as a controlled comparison between GPUs or between models.
| Reported setup | Article-reported result | How to interpret it |
|---|---|---|
RTX 4060 laptop GPU; DeepSeek-R1-Distill-Qwen-7B-Q4_K_M.gguf, reported as a 4.68 GB 7B GGUF model |
41.92 tokens/second for serial generation; 72.5–104.8 tokens/second for speculative generation | Results reported by Ian Cowley’s DEV article. The two generation modes are distinct; the figures are not independently verified. |
AMD Radeon 890M integrated GPU; Qwen3-30B-A3B-Instruct-Q3_K_L.gguf |
21.68 tokens/second | Article-reported result for a different model and execution path; not directly comparable to the RTX 4060 example. |
| CPU comparison associated with the Radeon example | 0.89 tokens/second | Article-reported comparison using that article’s setup; it is not a general CPU-versus-GPU ratio. |
Model architecture and size, quantization, memory bandwidth, hardware, and execution path can all affect throughput. The reported numbers do not establish how another system will perform, nor do they provide a cold-start measurement or a general comparison with other inference engines.
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Why GPU-side token selection matters in the account
Cowley says the engine performs argmax token selection on the GPU rather than transferring the full logits array to the host for that step. The article describes this as reducing a transfer of roughly 608 KB to a 4-byte token ID and reports approximately 3.2 microseconds of sampling overhead.
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When this approach may—and may not—fit
It may fit when
- You want to build or deploy a .NET application that runs inference locally and can use a compatible GPU path.
- A self-contained, native executable is useful for your deployment environment, and the libraries you need pass Native AOT compatibility checks.
- You can package and manage model files separately from the application binary.
It may not fit when
- Your project relies on runtime code generation, dynamic assembly loading, or libraries that are incompatible with trimming or Native AOT.
- You need one executable that works unchanged across operating systems or processor architectures.
- You need performance or binary-size figures for your own workload: the article’s examples do not substitute for testing the same model, quantization, hardware, and execution path you plan to use.
The practical conclusion is narrower than the headline’s broad phrasing: C# and Native AOT can be used for a local inference engine without bundling Python or a conventional C++/CUDA runtime stack, as one reported project demonstrates. Whether that is a good fit depends on AOT compatibility, the target environment, GPU interfaces, and workload-specific testing.
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