KVTuner: Sensitivity-Aware Layer-Wise Mixed-Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference

Abstract: KV cache quantization can improve LLMs inference throughput and latency in long contexts and large batch-size scenarios while preserving LLMs effectiveness. However, current methods have three unsolved issues: overlooking layer-wise sensitivity to KV cache quantization, high overhead of online fine-grained decision-making, and low flexibility to different LLMs and constraints. Therefore, we theoretically analyze the inherent correlation of layer-wise transformer attention patterns to KV cache quantization errors and study why key cache is generally more important than value cache for quantization error reduction. We further propose a simple yet effective framework KVTuner to adaptively search for the optimal hardware-friendly layer-wise KV quantization precision pairs for coarse-grained KV cache with multi-objective optimization and directly utilize the offline searched configurations during online inference. To reduce the computational cost of offline calibration, we utilize the intra-layer KV precision pair pruning and inter-layer clustering to reduce the search space. Experimental results show that we can achieve nearly lossless 3.25-bit mixed precision KV cache quantization for LLMs like Llama-3.1-8B-Instruct and 4.0-bit for sensitive models like Qwen2.5-7B-Instruct on mathematical reasoning tasks. The maximum inference throughput can be improved by 21.25\% compared with KIVI-KV8 quantization over various context lengths. Our code and searched configurations are available at https://github.com/cmd2001/KVTuner.

• The paper introduces KVTuner, a sensitivity-aware approach that optimizes layer-wise mixed-precision KV cache quantization for efficient LLM inference.
• It employs multi-objective optimization to reduce the search space and calibrate precision settings, achieving improvements in throughput without compromising accuracy.
• Experimental evaluations on Llama-3.1-8B-Instruct and others show nearly lossless performance at 3.25-4.0 bit quantization, enhancing deployment flexibility.

KVTuner: Sensitivity-Aware Layer-Wise Mixed-Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference

Introduction

The paper "KVTuner: Sensitivity-Aware Layer-Wise Mixed-Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference" addresses a critical challenge in the field of LLMs: the memory and latency bottlenecks introduced by KV cache during inference. It introduces KVTuner, a framework designed to optimize KV cache quantization in a way that improves both the efficiency and the accuracy of LLM inference. The framework addresses the limitations of existing methods, such as overlooking layer-wise sensitivity and high overheads in decision-making, by introducing a more adaptive and hardware-friendly approach.

Methodology

KVTuner employs sensitivity-aware optimization techniques to tune layer-wise KV cache precision, prioritizing key cache precision to minimize quantization errors while balancing resource efficiency.

• Inherent Model Sensitivity: The research identifies that the sensitivity to KV cache quantization is a characteristic inherent to LLMs and independent of input prompts. This understanding enables the offline calibration of optimal KV cache quantization settings, reducing online computational overhead.

  Figure 1: The layer-wise KV cache quantization tuning framework KVTuner with two-stage search space pruning for efficient MOO search using the final memory and model accuracy.

• Multi-Objective Optimization: KVTuner implements multi-objective optimization (MOO) to search for Pareto-optimal layer-wise KV precision pairs considering memory usage and model accuracy constraints. It employs intra-layer pruning and inter-layer clustering to significantly reduce the search space, enhancing the efficiency of the tuning process.

Experimental Evaluation

The study rigorously evaluates KVTuner across several models, including Llama-3.1-8B-Instruct and Qwen2.5-7B-Instruct, demonstrating its effectiveness in nearly lossless quantization at reduced precision.

• Accuracy and Efficiency: The experimental results on mathematical reasoning tasks such as GSM8K show that KVTuner can achieve 3.25-bit and 4.0-bit mixed precision KV cache quantization with accuracy comparable to higher precision baselines. This results in a throughput improvement of up to 38.3% compared to traditional quantization techniques.
• Attention Patterns and Layer Sensitivity: The framework successfully correlates the layer-wise KV cache quantization sensitivities with attention patterns, finding that retrieval heads exhibit greater sensitivity to quantization errors compared to streaming heads.

  Figure 2: Pareto frontier of Llama-3.1-8B-Instruct with the per-token-asym KV quantization mode and without the proposed two-stage search space pruning on the first 200 GSM8k 4-shot prompts.

Key Observations and Implications

1. Importance of Key Cache Precision: The research highlights the critical role of key cache precision in maintaining LLM accuracy and proposes that a hardware-friendly configuration like K4V2 can achieve efficient KV cache compression without compromising performance.
2. Layer-Wise Quantization Adaptation: Layer-wise adaptation in precision allows for broader applicability and flexibility, addressing specific computational constraints while optimizing inference quality.
3. Deployment Flexibility: KVTuner's framework is adaptable for integration with diverse LLM deployment systems, enabling practical implementation in various industrial applications.

Conclusion

KVTuner offers a significant advancement in the efficient use of KV cache in LLMs, providing a practical solution that balances resource usage and model fidelity. By leveraging sensitivity-aware layer-wise mixed-precision quantization, KVTuner enhances inference throughput and reduces memory overhead, which is crucial for deploying scalable and effective AI systems. Future work may explore further reductions in search complexity and extend the framework to additional model architectures and quantization paradigms.