Thunder Compute offers two modes for running instances.Documentation Index
Fetch the complete documentation index at: https://www.thundercompute.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
| Feature | Prototyping | Production |
|---|---|---|
| Use case | R&D, experimentation, short-lived development | Long-running inference, batch training, production workloads |
| Cost | Lower | Higher |
| Compatibility | Most ML workloads | Full |
Prototyping Mode
Prototyping mode is optimized for R&D, experimentation, and short-lived development workloads. Use production mode for long-running inference services or batch training jobs.
Supported Workloads
- Research & Development
- Fine-tuning
- Training
- Small-scale inference
- Example software: PyTorch (fully supported; downgrading from the pre-installed version may cause issues), TensorFlow, JAX, Jupyter Notebooks, ComfyUI, Ollama, VLLM, Unsloth
Unsupported Workloads
- Long-running production inference: persistent inference servers, always-on APIs, or latency-sensitive serving
- Batch training: unattended production training jobs, scheduled training pipelines, or other long-running training workloads
- Graphics workloads: OpenGL, Vulkan, FFMPEG
- Hardware-specific profiling tools: tools that require direct hardware metrics or low-level device access
Production Mode
Production mode provisions a standard virtual machine with full CUDA compatibility and predictable performance.When to Choose Production
- Long-running training jobs
- Multi-GPU workloads (up to 8 GPUs)
- Graphics workloads (OpenGL, Vulkan, FFMPEG)
- Custom CUDA kernels
- Hardware profiling
Switching Between Modes
Modify existing instances to switch between prototyping and production mode. This also lets you change GPU type, vCPUs, and RAM. Storage can be expanded but not reduced.Learn More
- Technical Specifications: Hardware, networking, and storage details