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MBFSAITeam/cuda-runtime-bundle

MBFS CUDA Runtime Bundle Self-contained, ABI-matched CUDA execution stacks for running ONNX Runtime on Windows with the CUDA / TensorRT execution providers. One folder per CUDA major version: Stack Folder CUDA cuDNN ONNX Runtime EP When to use cu12 cuda_v12/ 12.x 9 CUDA + TensorRT 10 sm_75+ GPU on driver R527+ (CUDA 12) cu11 cuda_v11/ 11.x 8 CUDA + TensorRT 8.6 Pascal/Volta (sm_60–sm_70), or any GPU on a driver capped at CUDA 11.x Pick the stack matching your… See the full description on the dataset page: https://huggingface.co/datasets/MBFSAITeam/cuda-runtime-bundle.

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MBFS CUDA Runtime Bundle

Self-contained, ABI-matched CUDA execution stacks for running ONNX Runtime on Windows with the CUDA / TensorRT execution providers. One folder per CUDA major version:

Stack	Folder	CUDA	cuDNN	ONNX Runtime EP	When to use
cu12	`cuda_v12/`	12.x	9	CUDA + TensorRT 10	sm_75+ GPU on driver R527+ (CUDA 12)
cu11	`cuda_v11/`	11.x	8	CUDA + TensorRT 8.6	Pascal/Volta (sm60–sm70), or any GPU on a driver capped at CUDA 11.x

Pick the stack matching your GPU and driver: a newer GPU on an older driver must still use cu11 (loading cu12 binaries fails with cudaErrorNoKernelImageForDevice). The MBFS Sentinel build auto-detects this — scripts/build_windows.py clamps the stack to the lower of the GPU and driver ceilings (override with --gpu-stack).

TensorRT engine-build libraries live in a companion folder, `trt_v12/` (for the cu12 stack). These are the TensorRT 10 runtime + parser (nvinfer*, nvonnxparser) and the per-SM builder resources used when TensorRT compiles an engine. It is not a standalone CUDA stack — pair it with cuda_v12/ (whose onnxruntime_providers_tensorrt.dll is only the small ONNX Runtime ↔ TensorRT bridge). Unlike the CUDA folders, trt_v12/ is fetched selectively: a build downloads only the one builder resource matching the target GPU's compute capability (plus the always-included PTX fallback), not the whole ~2.8 GiB set. See [Contents (`trt_v12/`)](#contents-trt_v12).

⚠️ These are runtime redistributable libraries, not source. The CUDA, cuDNN, and TensorRT DLLs are © NVIDIA Corporation and remain under NVIDIA's licenses — see [License & redistribution](#license--redistribution) below.

Contents (`cuda_v12/`)

File	Component	Approx. size
`onnxruntime.dll`	ONNX Runtime 1.23.2 (combined CUDA + DirectML + TensorRT + CPU build)	15 MB
`onnxruntime_providers_cuda.dll`	ONNX Runtime CUDA execution provider	361 MB
`onnxruntime_providers_tensorrt.dll`	ONNX Runtime TensorRT execution provider	<1 MB
`onnxruntime_providers_shared.dll`	ONNX Runtime shared provider interface	<1 MB
`cublas64_12.dll`, `cublasLt64_12.dll`	CUDA 12 cuBLAS	96 / 451 MB
`cudart64_12.dll`	CUDA 12 runtime	<1 MB
`cufft64_11.dll`	CUDA 12 cuFFT	279 MB
`curand64_10.dll`, `cusparse64_12.dll`	CUDA 12 cuRAND / cuSPARSE	62 / 264 MB
`cudnn64_9.dll` + `cudnn_*64_9.dll` (7 files)	cuDNN 9	~1.4 GB

Total ≈ 2.3 GiB.

Contents (`cuda_v11/`)

File	Component	Approx. size
`onnxruntime.dll`	ONNX Runtime (CUDA + TensorRT + CPU build)	13 MB
`onnxruntime_providers_cuda.dll`	ONNX Runtime CUDA execution provider	223 MB
`onnxruntime_providers_tensorrt.dll`	ONNX Runtime TensorRT execution provider	<1 MB
`onnxruntime_providers_shared.dll`	ONNX Runtime shared provider interface	<1 MB
`cublas64_11.dll`, `cublasLt64_11.dll`	CUDA 11 cuBLAS	85 / 519 MB
`cudart64_110.dll`	CUDA 11 runtime	<1 MB
`cufft64_10.dll`	CUDA 11 cuFFT	267 MB
`curand64_10.dll`, `cusparse64_11.dll`	CUDA 11 cuRAND / cuSPARSE	62 / 265 MB
`cudnn64_8.dll` + `cudnn_{adv,cnn,ops}_{infer,train}64_8.dll` (6 files)	cuDNN 8	~1.0 GB

Total ≈ 2.4 GiB. cuDNN 8 keeps inference and training libraries split (*_infer / *_train), unlike cuDNN 9.

All DLLs in a folder come from a single matched build — the ONNX Runtime provider DLLs are ABI-locked to that folder's onnxruntime.dll, so each set must be used together (never mix DLLs across folders or with another ONNX Runtime release, or the CUDA EP fails to load).

Contents (`trt_v12/`)

TensorRT 10 for the cu12 stack, split into core (always needed) and per-SM builder resources (one per GPU architecture). A build pulls the core plus only the resource matching the target GPU's compute capability, plus the PTX fallback — typically ≈ 1.0–1.7 GiB instead of the full ≈ 2.8 GiB.

Core — always downloaded:

File	Component	Approx. size
`nvinfer_10.dll`	TensorRT 10 core inference runtime + builder	432 MB
`nvinfer_plugin_10.dll`	TensorRT 10 standard plugins	53 MB
`nvonnxparser_10.dll`	ONNX → TensorRT network parser	3 MB
`nvrtc64_120_0.dll`	NVRTC runtime compiler (CUDA 12)	44 MB

Per-SM builder resources — download only the one matching your GPU:

File	Architecture	Example GPUs	Approx. size
`nvinfer_builder_resource_ptx_10.dll`	PTX JIT fallback — always included	(any, forward-compat)	488 MB
`nvinfer_builder_resource_sm75_10.dll`	Turing	T4, RTX 20xx	157 MB
`nvinfer_builder_resource_sm80_10.dll`	Ampere	A100, A30	256 MB
`nvinfer_builder_resource_sm86_10.dll`	Ampere	A40, RTX 30xx	241 MB
`nvinfer_builder_resource_sm89_10.dll`	Ada	L4, L40, RTX 40xx	254 MB
`nvinfer_builder_resource_sm90_10.dll`	Hopper	H100, H200	661 MB
`nvinfer_builder_resource_sm120_10.dll`	Blackwell	B100/B200, RTX 50xx	377 MB

Full set ≈ 2.8 GiB (4 core + 7 resources). Example single-SM footprint — Ampere A30 (sm80): core (532 MB) + PTX (488 MB) + sm80 (256 MB) ≈ 1.2 GiB.

The builder resources are only needed when TensorRT compiles an engine (the first run for a given model/GPU). nvinfer_10.dll loads the matching nvinfer_builder_resource_sm<cc>_10.dll from the DLL search path at engine-build time; the MBFS Sentinel build stages them under dist/lib/trt_v12/ and adds that directory to the process PATH. Once an engine cache (.engine/.trt) exists, the resource for that SM is no longer read.

Requirements

—OS: Windows x64
—NVIDIA driver: cu12 needs R527+ (CUDA 12.x); cu11 needs R452+ (CUDA 11.x). The CUDA Toolkit does not need to be installed — these bundles ship the runtime.
—GPU: see the support notes below.

GPU support

`cu12`

cuda_v12/onnxruntime_providers_cuda.dll is compiled with cubins for the following architectures (no PTX is embedded, so there is no JIT forward-compatibility to newer archs):

Compute capability	Architecture	Example GPUs	Native CUDA
5.2	Maxwell	GTX 9xx	✅
6.0	Pascal	Tesla P100	✅
7.0	Volta	V100	✅
7.5	Turing	T4, RTX 20xx	✅
8.0	Ampere	A100, A30	✅
8.6	Ampere	A40, RTX 30xx	✅
8.9	Ada	L4, L40, RTX 40xx	✅
9.0a	Hopper	H100, H200	✅
10.x / 12.x	Blackwell	B100/B200, RTX 50xx	❌ not native

For the TensorRT path the per-SM builder resources in trt_v12/ cover sm75 / sm80 / sm86 / sm89 / sm90 / sm120; older archs (sm_52/60/70) fall back to the PTX builder resource (JIT). Pick the resource by compute capability: sm = major*10 + minor (e.g. 8.0 → sm80, 12.0 → sm120).

`cu11`

The cu11 stack is the fallback for machines that predate CUDA 12: NVIDIA Pascal (sm_60) → Volta (sm_70), and any otherwise-cu12-capable GPU running on a driver that only supports CUDA 11.x. Use it when nvidia-smi reports a CUDA version below 12.0, or for Pascal/Volta silicon. For newer GPUs on a current driver, prefer cu12.

On a GPU outside the supported range, or with a too-old driver, the host application should fall back to DirectML (DmlExecutionProvider, requires DirectML.dll — not included here) or CPU. These bundles cover the native CUDA path only.

Usage

The MBFS Sentinel build pulls the right stack automatically (build_windows.py --gpu-stack {cu12|cu11} --cuda-source hf), including the matching TensorRT builder resource (--tensorrt-source hf, default). To fetch a stack manually:

bash

# cu12 into ./dist/lib (lands at ./dist/lib/cuda_v12/)
hf download MBFSAITeam/cuda-runtime-bundle --repo-type dataset \
  --include "cuda_v12/*" --local-dir ./dist/lib

# cu11 into ./dist/lib (lands at ./dist/lib/cuda_v11/)
hf download MBFSAITeam/cuda-runtime-bundle --repo-type dataset \
  --include "cuda_v11/*" --local-dir ./dist/lib

For TensorRT, fetch the core + PTX + only your GPU's SM instead of the whole folder (example for an Ampere A30, sm80):

bash

hf download MBFSAITeam/cuda-runtime-bundle --repo-type dataset \
  --include "trt_v12/nvinfer_10.dll" \
  --include "trt_v12/nvinfer_plugin_10.dll" \
  --include "trt_v12/nvonnxparser_10.dll" \
  --include "trt_v12/nvrtc64_120_0.dll" \
  --include "trt_v12/nvinfer_builder_resource_ptx_10.dll" \
  --include "trt_v12/nvinfer_builder_resource_sm80_10.dll" \
  --local-dir ./dist/lib

For reproducible builds, pin a commit revision with --revision <sha> instead of main.

License & redistribution

This repository bundles components under different licenses:

—CUDA runtime (cudart, cublas, cublasLt, cufft, curand, cusparse, nvrtc) — © NVIDIA Corporation, redistributed under the NVIDIA CUDA Toolkit EULA.
—cuDNN (cudnn*.dll, both v8 and v9) — © NVIDIA Corporation, redistributed under the NVIDIA cuDNN Software License Agreement.
—TensorRT (nvinfer*.dll, nvonnxparser*.dll, and the nvinfer_builder_resource_* builder resources) — © NVIDIA Corporation, redistributed under the NVIDIA TensorRT Software License Agreement.

The NVIDIA libraries are redistributed unmodified as runtime dependencies, as permitted by the above agreements. NVIDIA, CUDA, cuDNN, and TensorRT are trademarks of NVIDIA Corporation. This repository is not affiliated with or endorsed by NVIDIA. By using these files you agree to the respective NVIDIA license terms.

MBFS CUDA Runtime Bundle

Contents (cuda_v12/)

Contents (cuda_v11/)

Contents (trt_v12/)