How does memory management in transformer inference affect ChatGPT scalability?

Efficient memory management is critical for scaling ChatGPT due to KV cache growth and activation storage requirements.

Transformer inference is memory-intensive because it stores key-value (KV) caches for each token in the context window. As sequence length increases, KV cache grows linearly, consuming significant GPU memory.

Memory optimization techniques include KV cache paging, compression, offloading to CPU memory, and attention slicing. Without these optimizations, large-scale serving would be impossible due to GPU memory constraints.

This directly impacts scalability because memory becomes the limiting factor before compute in long-context scenarios.