nvidia-arch 5.0.0

A lightweight tool for detecting and querying NVIDIA GPU architectures (SM/CC), and generating `-gencode` flags for CUDA builds

“ nvidia-arch removes the guesswork and keeps your CUDA builds future‑proof and reproducible. ”

✅ All info has been verified by the Explorer 🤖

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A lightweight tool for detecting and querying NVIDIA GPU architectures (SM/CC), and generating -gencode flags for CUDA builds; ideal for integrating into Python setup.py and custom CUDA workflows.

If you just want to see my note, see README.md.

💡 Why this exists

Working with CUDA toolchains is notoriously inconsistent across systems, CUDA versions, and GPU families. Different machines report different supported architectures, nvcc behaves differently depending on the installed CTK (CUDA Toolkit), and build scripts often end up hard‑coding SM versions that quickly become outdated.

This package solves that by providing:

• A single reliable source of truth for supported SM and compute capabilities
• Automatic detection of architectures from the installed CUDA Toolkit
• Clean overrides for building against specific CUDA versions
• Correct and reproducible generation of -gencode flags
• Consistent behavior across Linux, Windows, WSL, and CI environments

Key features:

• Detect installed CUDA Toolkit (CTK) and its include/lib paths
• Query supported SM/CC architectures for any CUDA version
• Generate correct -gencode flags for nvcc
• Handle PTX emission cleanly (+PTX suffix or highest‑SM policy)
• Filter architectures by GPU family (consumer, workstation, Jetson)
• Provide PyTorch‑style CC strings (7.5;8.6;8.9+PTX)
• Work reliably across heterogeneous environments (local, Docker, CI)

💽 Installation

Install from PyPI:

pip install nvidia-arch

Install from GitHub repo:

pip install git+https://github.com/rathaROG/nvidia-arch.git

🧪 Usage

For all details of all available functions: see core.py and arches.py.

Main highlights

Print a summary of supported architectures for each CUDA version

from nvidia_arch import print_summary
print_summary(min_sm=30)

CUDA  Arch (min..max)   Consumer/Workstation (cons)                Jetson (jets)
====================================================================================
11.0  3.0..8.0          3.0;3.5;5.0;5.2;6.0;6.1;7.0;7.5            3.2;5.3;6.2;7.2
11.1  3.5..8.6          3.5;5.0;5.2;6.0;6.1;7.0;7.5;8.6            5.3;6.2;7.2
11.2  3.5..8.6          3.5;5.0;5.2;6.0;6.1;7.0;7.5;8.6            5.3;6.2;7.2
11.3  3.5..8.6          3.5;5.0;5.2;6.0;6.1;7.0;7.5;8.6            5.3;6.2;7.2
11.4  3.5..8.7          3.5;5.0;5.2;6.0;6.1;7.0;7.5;8.6            5.3;6.2;7.2;8.7
11.5  3.5..8.7          3.5;5.0;5.2;6.0;6.1;7.0;7.5;8.6            5.3;6.2;7.2;8.7
11.6  3.5..8.7          3.5;5.0;5.2;6.0;6.1;7.0;7.5;8.6            5.3;6.2;7.2;8.7
11.7  3.5..8.7          3.5;5.0;5.2;6.0;6.1;7.0;7.5;8.6            5.3;6.2;7.2;8.7
11.8  3.5..9.0          3.5;5.0;5.2;6.0;6.1;7.0;7.5;8.6;8.9        5.3;6.2;7.2;8.7
12.0  5.0..9.0          5.0;5.2;6.0;6.1;7.0;7.5;8.6;8.9            5.3;6.2;7.2;8.7
12.1  5.0..9.0          5.0;5.2;6.0;6.1;7.0;7.5;8.6;8.9            5.3;6.2;7.2;8.7
12.2  5.0..9.0          5.0;5.2;6.0;6.1;7.0;7.5;8.6;8.9            5.3;6.2;7.2;8.7
12.3  5.0..9.0          5.0;5.2;6.0;6.1;7.0;7.5;8.6;8.9            5.3;6.2;7.2;8.7
12.4  5.0..9.0          5.0;5.2;6.0;6.1;7.0;7.5;8.6;8.9            5.3;6.2;7.2;8.7
12.5  5.0..9.0          5.0;5.2;6.0;6.1;7.0;7.5;8.6;8.9            5.3;6.2;7.2;8.7
12.6  5.0..9.0          5.0;5.2;6.0;6.1;7.0;7.5;8.6;8.9            5.3;6.2;7.2;8.7
12.8  5.0..12.0         5.0;5.2;6.0;6.1;7.0;7.5;8.6;8.9;12.0       5.3;6.2;7.2;8.7
12.9  5.0..12.1         5.0;5.2;6.0;6.1;7.0;7.5;8.6;8.9;12.0;12.1  5.3;6.2;7.2;8.7
13.0  7.5..12.1         7.5;8.6;8.9;12.0;12.1                      8.7;11.0
13.1  7.5..12.1         7.5;8.6;8.9;12.0;12.1                      8.7;11.0
13.2  7.5..12.1         7.5;8.6;8.9;12.0;12.1                      8.7;11.0
====================================================================================

* All NVIDIA Architectures:
  3.0;3.2;3.5;3.7;5.0;5.2;5.3;6.0;6.1;6.2;7.0;7.2;7.5;8.0;8.6;8.7;8.8;8.9;9.0;10.0;10.1;10.3;11.0;12.0;12.1

* Other Notes:
  1. Architecture(s) 8.8 is not officially supported in CUDA 11.8–12.9.
  2. Architecture(s) 10.1 is not officially supported in CUDA 13.0–13.2.
  3. Architecture(s) 10.3 is not officially supported in CUDA 12.8–12.8.
  4. Architecture(s) 11.0 is not officially supported in CUDA 12.8–12.9.

Detect CTK (CUDA Toolkit) in your environment

import json
from nvidia_arch import detect_ctk

cuda_info = detect_ctk()
print(json.dumps(cuda_info, indent=2))

{
  "version": "13.0",
  "root": "/usr/local/cuda",
  "include": {
    "root": "/usr/local/cuda/include",
    "cuda": "/usr/local/cuda/include/cccl/cuda",
    "cub": "/usr/local/cuda/include/cccl/cub",
    "thrust": "/usr/local/cuda/include/cccl/thrust"
  },
  "lib": "/usr/local/cuda/lib64"
}

Find all NVIDIA GPU(s) installed

import json
from nvidia_arch import find_gpu

gpu_info = find_gpu(extra_query_gpu='serial,temperature.gpu')
print(json.dumps(gpu_info, indent=2))

[
  {
    "name": "NVIDIA RTX A6000",
    "compute_cap": "8.6",
    "memory.total": "49140",
    "serial": "1234567891011",
    "temperature.gpu": "44"
  },
  {
    "name": "NVIDIA RTX A6000",
    "compute_cap": "8.6",
    "memory.total": "49140",
    "serial": "1234567891012",
    "temperature.gpu": "39"
  }
]

Get compute cap of the GPU(s) installed

from nvidia_arch import get_compute_cap
get_compute_cap(return_mode='cc_string', add_ptx=True)

'8.6;8.9+PTX'

Get supported SM architectures from installed CTK (CUDA Toolkit)

from nvidia_arch import get_architectures
get_architectures(cuda_ver=None, min_sm=75)

['75', '80', ...]

Get architectures for a specific CTK (CUDA Toolkit) version

from nvidia_arch import get_architectures
get_architectures(cuda_ver="13.0", min_sm=75)

['75', '80', '86', '87', '88', '89', '90', '100', '103', '110', '120', '121']

Get architectures and filter by GPU type (Consumer, Jetson, etc.)

Supported inputs for gpu_type:

• "all": All supported GPUs
• "cons": Only consumer/workstation GPUs
• "jets": Only Jetson/embedded GPUs
• "cons+jets": Only consumer/workstation + Jetson/embedded GPUs

from nvidia_arch import get_architectures
get_architectures(gpu_type="cons", cuda_ver="13.0", min_sm=75)

['75', '86', '89', '120', '121']

Get compute capabilities instead of SM codes

from nvidia_arch import get_architectures
get_architectures(gpu_type="cons", cuda_ver="13.0", min_sm=75, return_mode="cc_list")

['7.5', '8.6', '8.9', '12.0', '12.1']

Get PyTorch‑style architectures string with PTX

from nvidia_arch import get_architectures
get_architectures(gpu_type="cons+jets", cuda_ver="13.0", min_sm=75, return_mode="cc_string", add_ptx=True)

'7.5;8.6;8.7;8.9;11.0;12.0;12.1+PTX'

Validate a PyTorch‑style architectures string

from nvidia_arch import validate_cc_string
validate_cc_string(
    "6.1+PTX;Pascal;12.0;Lovelace",
    named_arches={"Pascal": "6.0;6.1+PTX", "Lovelace": "8.9+PTX"},
    force_highest_ptx=True,
    against_cuda_ver="12.8"
)

'6.0;6.1;8.9;12.0+PTX'

from nvidia_arch import validate_cc_string
validate_cc_string(
    "6.1+PTX;Pascal;12.0;Lovelace;13.5;0.9",
    named_arches={"Pascal": "6.0;6.1+PTX", "Lovelace": "8.9+PTX"},
    force_highest_ptx=True,
    against_cuda_ver="13.2"
)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\dev\exc\python\p311\Lib\site-packages\nvidia_arch\core.py", line 483, in validate_cc_string
    raise ValueError(f"Unknown architecture(s): {', '.join(unknown_arch)}. ")
ValueError: Unknown architecture(s): 0.9, 13.5+PTX.

Generate nvcc -gencode flags in Setup.py

from nvidia_arch import get_architectures, make_gencode_flags
arches = get_architectures(gpu_type="jets", cuda_ver="13.0", min_sm=75)
make_gencode_flags(arches, add_ptx=True)
# extra_compile_args["nvcc"] += make_gencode_flags(arches)

['-gencode=arch=compute_87,code=sm_87', '-gencode=arch=compute_110,code=[sm_110,compute_110]']

See a real example in BEVFusionx.

📝 License