A zero-allocation .NET 10 NativeAOT AI runtime streaming MoE weights directly from NVMe to GPU over PCIe.
0
stars
50
commits
C#
primary language
Sep 8, 2026
updated
https://h4zey86.github.io/Fractal-BLT/
Traditional AI runtimes are crippled by heavy software stacks (Python, PyTorch, CPython runtimes) and rigid tokenizers that waste massive compute cycles on predictable boilerplate.
Fractal-BLT is a bare-metal systems reimagining of local artificial intelligence. By fusing .NET 10 NativeAOT, unbuffered direct I/O (O_DIRECT / FILE_FLAG_NO_BUFFERING), Shannon entropy byte patchification, unmanaged Graph Convolution Network (GCN) routing, and jitted raw PTX CUDA execution, Fractal-BLT bypasses the VRAM bottleneck entirely—streaming mixture-of-experts weights straight from Gen4/Gen5 NVMe storage across the PCIe bus directly into GPU memory with zero managed heap allocations.
Experience high-throughput, local sovereign AI inference powered by custom C# NativeAOT, memory-mapped NVMe-to-VRAM DMA transfers, and JIT-compiled PTX kernels.
https://github.com/user-attachments/assets/ae5b55f8-469e-4494-95e7-d488a656c6e0
Want to test the zero-allocation telemetry pipeline locally on your machine? Run the automated PowerShell harness:
powershell -ExecutionPolicy Bypass -File .\Run-Demo.ps1
graph TD
A[Raw UTF-8 Byte Stream] -->|Sliding-Window Shannon Entropy| B(Fractal-BLT Encoder)
B -->|Span PatchBoundaries| C(Fractal-GNN Router)
C -->|Unmanaged Adjacency GCN Matrix| D{Expert ID Selected}
D -->|O_DIRECT Unbuffered Read| E[Fractal-Streamer NVMe]
E -->|cuMemHostRegister Pinned DMA| F[Fractal-Bridge PCIe]
F -->|cuLaunchKernel SGEMV JIT PTX| G[NVIDIA RTX 5070 Ti]
G -->|Zero-Copy PipeWriter SSE| H[Kestrel /v1/chat/completions]
.safetensors shards straight from storage.cuModuleLoadData, cuLaunchKernel), pinning host buffers and orchestrating zero-copy PCIe DMA transfers.stackalloc arrays.[InlineArray(64)]./v1/chat/completions endpoint utilizing Kestrel SlimBuilder and raw PipeWriter Server-Sent Events (SSE) streaming.| Metric | Fractal-BLT (.NET 10 NativeAOT) | Standard Python / PyTorch vLLM |
|---|---|---|
| Startup Memory Footprint | ~10.06 MB | ~1.8 - 3.5 GB |
| Managed Heap Allocations | 0 Bytes (Hot Path) | Heavy GC Pressure |
| Byte Patching Throughput | 45.05 MB/s (Single Thread) | Python Interpreter Overhead |
| GNN Adjacency Routing | 541.3 µs per 8x8 Matrix | PyTorch Autograd Overhead |
| VRAM Model Capacity | Infinite (NVMe Swapped) | Hard-capped by GPU VRAM |
git clone https://github.com/H4ZEY86/Fractal-BLT.git
cd Fractal-BLT
dotnet publish -c Release /p:PublishAot=true
dotnet run --project FractalCore/FractalCore.csproj -c Release
dotnet run --project FractalServe/FractalServe.csproj -c Release
curl -N -X POST http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "fractal-moe", "messages": [{"role": "user", "content": "Write a quicksort in C#"}], "stream": true}'
Fractal-BLT is entirely open-source. If you use this runtime in production or want to help cover the costs of CI/CD workflows, GPU Github Runners, and continued maintenance, please consider donating. (Note: The Opure Sovereign Enclave (OZTAE) is a separate, proprietary commercial product available for enterprise licensing).
Support Fractal-BLT via PayPal
Distributed under the Apache 2.0 License. See LICENSE for details.
50 commits
C#
86.4%
PowerShell
5.2%
JavaScript
3.6%
CSS
3.2%
A zero-allocation .NET 10 NativeAOT AI runtime streaming MoE weights directly from NVMe to GPU over PCIe.
0
stars
50
commits
C#
primary language
Sep 8, 2026
updated
https://h4zey86.github.io/Fractal-BLT/
Traditional AI runtimes are crippled by heavy software stacks (Python, PyTorch, CPython runtimes) and rigid tokenizers that waste massive compute cycles on predictable boilerplate.
Fractal-BLT is a bare-metal systems reimagining of local artificial intelligence. By fusing .NET 10 NativeAOT, unbuffered direct I/O (O_DIRECT / FILE_FLAG_NO_BUFFERING), Shannon entropy byte patchification, unmanaged Graph Convolution Network (GCN) routing, and jitted raw PTX CUDA execution, Fractal-BLT bypasses the VRAM bottleneck entirely—streaming mixture-of-experts weights straight from Gen4/Gen5 NVMe storage across the PCIe bus directly into GPU memory with zero managed heap allocations.
Experience high-throughput, local sovereign AI inference powered by custom C# NativeAOT, memory-mapped NVMe-to-VRAM DMA transfers, and JIT-compiled PTX kernels.
https://github.com/user-attachments/assets/ae5b55f8-469e-4494-95e7-d488a656c6e0
Want to test the zero-allocation telemetry pipeline locally on your machine? Run the automated PowerShell harness:
powershell -ExecutionPolicy Bypass -File .\Run-Demo.ps1
graph TD
A[Raw UTF-8 Byte Stream] -->|Sliding-Window Shannon Entropy| B(Fractal-BLT Encoder)
B -->|Span PatchBoundaries| C(Fractal-GNN Router)
C -->|Unmanaged Adjacency GCN Matrix| D{Expert ID Selected}
D -->|O_DIRECT Unbuffered Read| E[Fractal-Streamer NVMe]
E -->|cuMemHostRegister Pinned DMA| F[Fractal-Bridge PCIe]
F -->|cuLaunchKernel SGEMV JIT PTX| G[NVIDIA RTX 5070 Ti]
G -->|Zero-Copy PipeWriter SSE| H[Kestrel /v1/chat/completions]
.safetensors shards straight from storage.cuModuleLoadData, cuLaunchKernel), pinning host buffers and orchestrating zero-copy PCIe DMA transfers.stackalloc arrays.[InlineArray(64)]./v1/chat/completions endpoint utilizing Kestrel SlimBuilder and raw PipeWriter Server-Sent Events (SSE) streaming.| Metric | Fractal-BLT (.NET 10 NativeAOT) | Standard Python / PyTorch vLLM |
|---|---|---|
| Startup Memory Footprint | ~10.06 MB | ~1.8 - 3.5 GB |
| Managed Heap Allocations | 0 Bytes (Hot Path) | Heavy GC Pressure |
| Byte Patching Throughput | 45.05 MB/s (Single Thread) | Python Interpreter Overhead |
| GNN Adjacency Routing | 541.3 µs per 8x8 Matrix | PyTorch Autograd Overhead |
| VRAM Model Capacity | Infinite (NVMe Swapped) | Hard-capped by GPU VRAM |
git clone https://github.com/H4ZEY86/Fractal-BLT.git
cd Fractal-BLT
dotnet publish -c Release /p:PublishAot=true
dotnet run --project FractalCore/FractalCore.csproj -c Release
dotnet run --project FractalServe/FractalServe.csproj -c Release
curl -N -X POST http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "fractal-moe", "messages": [{"role": "user", "content": "Write a quicksort in C#"}], "stream": true}'
Fractal-BLT is entirely open-source. If you use this runtime in production or want to help cover the costs of CI/CD workflows, GPU Github Runners, and continued maintenance, please consider donating. (Note: The Opure Sovereign Enclave (OZTAE) is a separate, proprietary commercial product available for enterprise licensing).
Support Fractal-BLT via PayPal
Distributed under the Apache 2.0 License. See LICENSE for details.
50 commits
C#
86.4%
PowerShell
5.2%
JavaScript
3.6%
CSS
3.2%