CUDA Execute — Run CUDA/C++ code on remote GPUs via Modal.
A CLI tool for compiling and executing CUDA/C++ code on remote GPU infrastructure. Write kernels locally, run them in the cloud.
Develop locally, test remotely. Write and iterate on CUDA/C++ code on your local machine, then seamlessly run it on real GPU hardware in the cloud. Modal provides the sandboxed execution environment, so you can safely test code without worrying about breaking anything locally.
# Install directly from GitHub uv tool install git+https://github.com/zanussbaum/cuex # Verify installation cuex --helpcuex requires a Modal account (free tier available).
# Authenticate with Modal (one-time) modal setupcuex run <source_file> [options] [-- program_args...]Options:
| Flag | Description | Default |
|---|---|---|
--gpu, -g | GPU type: none, T4, L4, A10G, A100, H100 | Auto-detect from extension |
--timeout, -t | Max execution time in seconds | 300 |
--asm | Generate ASM/PTX/SASS output | false |
--data, -d | Glob pattern for data files to upload (repeatable) | - |
Auto-detection:
.cppfiles → CPU-only execution.cufiles → GPU execution (default: L4)
# Run CUDA code on default GPU (L4) cuex run my_kernel.cu # Run CUDA on A100 cuex run my_kernel.cu --gpu A100 # Run C++ on CPU only cuex run my_program.cpp # Get PTX/SASS output cuex run my_kernel.cu --asm # Upload data files with your kernel cuex run matmul.cu --data "*.bin" --data "config.json" # Pass arguments to the compiled program cuex run mandelbrot.cpp -- --width 1024 --height 768 # Check version cuex --version# Show code diff between last two runs cuex diff my_kernel.cu # Compare specific runs cuex diff my_kernel.cu -n 2 # Compare runs 2 and 3Compile and execute logs stream to stdout in real-time:
$ cuex run my_kernel.cu Source: my_kernel.cu GPU: L4 Timeout: 300s [compile] nvcc -O3 -o /tmp/program /tmp/my_kernel.cu [execute] /tmp/program [execute] Kernel executed in 0.042ms [execute] Result: 42 ✓ Job complete (exit code 0) Output saved to: ./cuex-out/my_kernel/L4/20250111_143052_a1b2c3d4/ Logs are saved to disk organized by file and GPU type:
./cuex-out/ └── <filename>/ └── <gpu_type>/ └── <job_id>/ ├── compile_log.txt ├── execute_log.txt ├── source.cu ├── asm_output.txt # If --asm used └── out/ # Program output files - Python 3.11+
- Modal account (free tier available)
MIT