nvidia-arch 7.2.0

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on the installed CTK (CUDA Toolkit), and build scripts often end up hard‑coding SM versions that quickly become outdated.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;10.1
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;10.1
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
13.3  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 13.0–13.3.
  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_gpus

gpu_info = find_gpus(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_caps
get_compute_caps(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_arches
get_arches(cuda_ver=None, min_sm=75)

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

Get architectures for a specific CTK (CUDA Toolkit) version

from nvidia_arch import get_arches
get_arches(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
• "dcen": Only datacenter GPUs
• "cons+jets": Only consumer/workstation + Jetson/embedded GPUs

from nvidia_arch import get_arches
get_arches(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_arches
get_arches(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_arches
get_arches(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'

Normalize architectures

from nvidia_arch import normalize_arches, get_arches

normalize_arches(['75', '86', '89+PTX'], return_mode='cc_string')
normalize_arches('7.5;8.6;8.9+PTX', exclude='8.6', return_mode='cc_string')

arches = get_arches(cuda_ver=12.8, return_mode='cc_string', add_ptx=True)
normalize_arches(arches, exclude='10.1', return_mode='cc_string')

'7.5;8.6;8.9+PTX'
'7.5;8.9+PTX'
'5.0;5.2;5.3;6.0;6.1;6.2;7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;12.0+PTX'

Validate a PyTorch‑style architectures string

from nvidia_arch import validate_arch_string
validate_arch_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_arch_string
validate_arch_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_arch_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_arches, make_gencode_flags
arches = get_arches(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.

Deprecation

The following legacy function names are deprecated and will be removed in version 10.0.0:

• find_gpu() → use find_gpus() instead
• get_compute_cap() → use get_compute_caps() instead
• get_architectures() → use get_arches() instead
• validate_cc_string() → use validate_arch_string() instead

You can continue using the old names until v10.0.0, but all new code and documentation now use the new, more Pythonic API. Importing or calling any deprecated function will issue a DeprecationWarning.

📝 License