Prototyping mode is currently in beta and exclusively available on Thunder Compute.

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Overview

Prototyping mode leverages CUDA-level optimizations to maximize GPU utilization, significantly reducing costs for AI/ML development workflows. While in beta, you may encounter compatibility issues with certain workloads. For maximum compatibility, you can start on Prototyping mode and easily switch to Production mode at any time.

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How it works

Thunder Compute’s prototyping mode applies optimizations at the CUDA level to maximize GPU utilization. This results in:

  • Higher GPU utilization: More efficient use of available compute resources
  • Lower costs: Pay less for the same computational power
  • Quick iteration: Ideal for development and experimentation

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Mode switching

We’ve designed Thunder Compute to make switching between modes simple:

  • Prototyping mode: Default mode with optimizations enabled for cost efficiency
  • Production mode: Disables optimizations for full compatibility at standard pricing

You can switch modes at any time based on your workload requirements. If you encounter issues in prototyping mode, simply switch to production mode for guaranteed compatibility.

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Mode-Specific Configuration

Prototyping mode uses the same infrastructure specifications as Production mode, with CUDA-level optimizations enabled for cost efficiency.

For detailed hardware specifications, networking, and pre-installed software, see the Technical Specifications documentation.

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Supported Software

The following software is tested and supported in prototyping mode. Exact version numbers for common pre-installed libraries are listed below.

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Prototyping Mode Compatibility

The following software has been specifically tested and validated for prototyping mode:

  • PyTorch: Fully supported (downgrading from pre-installed version may cause issues)
  • Notebooks: JupyterLab and Jupyter Notebooks work seamlessly
  • Model Serving: ComfyUI, Ollama, VLLM, and Unsloth are well-tested

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Experimental Support

The following workloads have limited testing in prototyping mode:

  • TensorFlow [experimental]
  • PyTorch Lightning [experimental]
  • JAX [experimental]
  • Custom CUDA Kernels [unpredictable behavior, particularly with errors and profiling]

If you encounter issues with experimental workloads, switch to Production mode for maximum compatibility.

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Unsupported Workloads

Currently, prototyping mode lacks official support for:

  • Graphics workloads (OpenGL, Vulkan, FFMPEG)
  • Certain low-level CUDA operations
  • Hardware-specific profiling tools

For unsupported workloads, use Production mode which provides full compatibility with all optimizations disabled.

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Benchmarking Considerations

When benchmarking in prototyping mode, note that:

  • Temperature, wattage, and utilization metrics may not be representative
  • Model performance (iterations/second) provides the most accurate comparison
  • For hardware benchmarking, use production mode instances

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Best Practices

  1. Start with prototyping mode for development and experimentation
  2. Test compatibility early with your specific workloads
  3. Switch to production mode if you encounter issues or need guaranteed compatibility
  4. Monitor performance using application-level metrics rather than hardware metrics

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Troubleshooting

If you experience compatibility issues:

  1. Check if your workload is in the experimental or unsupported categories
  2. Try switching to production mode
  3. Consult our troubleshooting guide
  4. Contact support with specific error messages

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