RunPod

RunPod is used as an on-demand GPU compute platform to support ML training and experimentation where temporary access to high-performance hardware is required.

In this portfolio, its usage is primarily tied to academic and research work, rather than long-running production systems.

Key Capabilities

• On-Demand GPU Access
  Provides rapid access to a range of modern GPUs for training and inference workloads.

• Fast Provisioning Model
  Enables quick spin-up of compute resources without complex infrastructure setup.

• Cost-Aware Compute
  Supports short-lived, intensive workloads where dedicated cloud GPU clusters would be inefficient.

• Preconfigured ML Environments
  Offers ready-to-use environments suitable for experimentation and fine-tuning tasks.

• Flexible Execution Modes
  Supports both instance-based and serverless-style GPU execution.

Experience & Research Contribution

Used RunPod during Master’s-level research to support compute-intensive ML workloads that required temporary access to powerful GPUs.

Key contributions included:

• Running LLM fine-tuning and experimentation workflows
• Processing and analyzing astronomical data, including Chandra X-ray Observatory datasets
• Evaluating model behavior and performance under different compute configurations
• Managing short-lived training jobs in a cost-effective manner
• Comparing managed GPU platforms against self-managed alternatives for research use

RunPod served as a practical research enabler, providing flexible access to GPU resources without the operational overhead of maintaining persistent ML infrastructure.