LLM Inference

Quantize KV Caches with Explicit Error Budgets

Autoregressive transformer inference retains key and value tensors from earlier tokens so each new token can attend to prior context without recomputing those projections. As context length and concurrent sequence count rise, this KV cache can become a substantial part of accelerator memory. KV cache quantization stores those tensors at reduced precision and reconstructs approximations when attention consumes them. The memory arithmetic is attractive, but the resulting error is not a generic model-weight perturbation. Quantized keys affect attention scores before the softmax, while quantized values affect the weighted sum after attention probabilities have been formed.