How does GPU memory fragmentation impact ChatGPT inference scalability?

GPU memory fragmentation reduces available contiguous memory, limiting batch size and long-context inference efficiency.

In large-scale ChatGPT deployments, GPU memory is used for model weights, activations, and KV cache. Over time, dynamic allocation and deallocation cause fragmentation, where free memory exists but is split into non-contiguous blocks.

This prevents allocation of large tensors required for long-context processing or large batches, reducing throughput and increasing latency.

To mitigate this, systems use memory pooling, pre-allocation strategies, and KV cache management techniques. Efficient memory scheduling is critical for maintaining stable inference performance.