MemShare: Implementation and Performance Optimization Analysis of KV Cache Sharing Technology for Inference Models

MemShare is an open-source project addressing the memory bottleneck of inference models. By optimizing the PagedAttention architecture of vLLM with intra-request KV cache block sharing technology, it reduces memory usage by 30% to 50% and increases inference throughput by 20% to 40% without sacrificing model accuracy. This article will analyze its technical principles, performance benefits, and application value.

Inference models (e.g., DeepSeek-R1, OpenAI o-series) rely on long inference chains to improve accuracy, but KV cache memory consumption increases sharply. KV cache stores attention key-value pairs to avoid redundant computations, but memory usage becomes a bottleneck during long-chain generation. Traditional solutions (quantization, pagination) have issues like accuracy loss or complexity.

The core of MemShare is intra-request KV cache block sharing, extended based on vLLM: 1. Similarity detection uses lightweight LSH hashing to quickly locate candidate blocks; 2. Reference counting manages the lifecycle of shared blocks; 3. Adapts attention computation to ensure consistent output. Unlike cross-request sharing, it focuses on eliminating redundancy within a single request.

Experimental data shows that KV cache usage is reduced by 30-50% in long-chain inference tasks (depending on task redundancy); memory savings are converted into larger batch processing capacity, increasing throughput by 20-40%. Overheads like similarity detection are controlled at a low level through optimization, resulting in positive net benefits.

Applicable scenarios: Long-chain inference (mathematical proof, code generation), models with frequent self-correction, memory-constrained environments (consumer GPUs/edge devices). Limitations: Limited benefits for standard generation tasks, increased system complexity, need to balance accuracy and memory with similarity thresholds.

Comparison: Quantization (reduces accuracy), PagedAttention (allocation efficiency), speculative decoding (orthogonal optimization); MemShare does not compromise accuracy and can be combined with other technologies. Future directions: Cross-layer sharing, adaptive thresholds, collaborative design with model architectures.

MemShare provides tools for efficient deployment of inference models, improving memory efficiency and throughput without sacrificing accuracy, which is of great value to developers/researchers in resource-constrained environments. Underlying optimization technologies will become more important as the application of inference models becomes widespread.