How does KV-cache eviction strategy affect ChatGPT long-context stability and throughput?

KV-cache eviction controls which past token states are kept or dropped to manage GPU memory, directly impacting long-context quality and throughput.

In transformer inference, KV-cache stores key and value tensors for each token to avoid recomputation during attention. However, GPU memory is finite, so long conversations eventually exceed available memory.

KV-cache eviction strategies decide which cached states to remove. Common approaches include sliding window eviction (dropping oldest tokens), importance-based eviction (keeping semantically relevant tokens), and hybrid policies using summarization + caching.

Poor eviction can degrade reasoning continuity, while aggressive caching improves memory usage but risks losing critical context.