KV cache quantization: what FP8/INT8 K and V actually buy you, and where they break

1. 1Implement FP8/INT8 quantization on KV caches using libraries like TensorFlow or PyTorch
2. 2Measure the reduction in attention state size
3. 3Analyze the shift in logit distribution and its impact on model performance
4. 4Evaluate the effect on speculative decoding gains
5. 5Optimize model configuration to mitigate potential losses

Machine learning engineers and researchers benefit from understanding the trade-offs of KV cache quantization, as it impacts model accuracy and efficiency

💡 KV cache quantization can significantly reduce attention state size, but may also impact model accuracy due to changes in logit distribution

💡 FP8/INT8 KV cache quantization cuts attention state by 50%, but shifts logit distribution, quietly halving speculative decoding gains