What Groq Can’t Do (That RunPod Quietly Dominates)…

Author(s): R. Thompson (PhD)

Originally published on Towards AI.

What Groq Can’t Do (That RunPod Quietly Dominates)…

The large language model (LLM) revolution is in full swing, and one of the biggest determinants of success isn’t just which model you use — it’s where and how you run it. The battle is no longer just about model size or benchmark scores. Instead, it’s about control versus speed, customization versus convenience, and experimentation versus execution. In 2025, RunPod and Groq stand as two of the most important compute platforms for builders in the AI space.

The article explores the unique features of both RunPod and Groq in the context of AI computing. RunPod is highlighted for its flexibility and full control over the LLM lifecycle, enabling users to fine-tune and customize models effectively. In contrast, Groq is characterized by its speed and efficiency, offering rapid inference through specialized hardware. Practical use cases illustrate how developers and researchers can benefit from each platform depending on their needs, whether for real-time applications or extensive model training.

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Published via Towards AI