KV cache optimization encompasses infrastructure improvements and algorithmic techniques designed to reduce memory consumption and improve computational efficiency during the inference phase of large language models (LLMs). The key-value cache, which stores intermediate computations for attention mechanisms, represents a significant bottleneck in inference performance, particularly as sequence lengths and batch sizes increase. Optimization approaches address both memory bandwidth constraints and storage requirements through disaggregation, compression, and scheduling techniques.

During transformer inference, the key-value (KV) cache stores previously computed key and value vectors for each token in the sequence, enabling efficient attention computation without recomputation. For a model with hidden dimension d and a sequence of length n, the KV cache grows linearly with sequence length, consuming memory proportional to 2 × n × d × batch_size × num_heads. This memory pressure becomes acute in long-context inference scenarios and high-throughput serving environments ¹⁾.

KV cache optimization strategies fall into several categories. Disaggregation approaches separate KV cache storage from computation, enabling independent scaling of memory and compute resources. Compression techniques reduce cache footprint through quantization, pruning, or low-rank decomposition. Scheduling algorithms manage cache allocation across concurrent requests, maximizing hardware utilization. Attention pattern exploitation leverages sparsity in attention matrices to skip irrelevant cache accesses ²⁾.

Disaggregation-style approaches, inspired by principles from disaggregated storage systems, decouple KV storage from the GPU computing units. This enables KV cache to reside in slower but larger memory pools—such as host CPU memory, NVMe, or specialized disaggregated storage—while maintaining high-bandwidth access through optimized I/O protocols. The MORI-IO KV Connector demonstrates this approach, achieving 2.5x higher goodput on single nodes by optimizing the memory hierarchy and reducing GPU memory pressure ³⁾.

This design pattern follows PD-disaggregation principles, where physical resources are logically separated from computation. Modern implementations use high-speed interconnects (NVMe over Fabrics, PCIe 5.0, or custom protocols) to bridge the latency gap between compute-attached GPU memory and disaggregated storage. Practical systems achieve near-GPU memory bandwidth through careful pipelining, prefetching, and asynchronous I/O orchestration ⁴⁾.

Quantization reduces cache memory footprint by storing keys and values with reduced precision. Techniques include 8-bit integer quantization, mixed-precision approaches, and learned quantization schemes. Research demonstrates that KV cache can tolerate significant quantization (down to 4-bit or lower) with minimal accuracy degradation, as attention computation is relatively robust to quantization noise ⁵⁾.

Token pruning selectively discards KV cache entries for tokens with minimal attention influence on future predictions. This requires careful token importance scoring—typically using attention weights or gradient-based salience metrics—to avoid degrading model quality. Low-rank decomposition represents KV matrices as products of lower-dimensional factors, exploiting inherent low-rank structure in attention patterns.

KV cache optimization directly impacts inference throughput (tokens/second), latency (time-to-first-token and time-per-token), and cost-effectiveness of LLM serving infrastructure. Systems employing disaggregation achieve higher effective batch sizes without GPU memory exhaustion, improving GPU utilization from typical 30-50% to 70-85% in production deployments. This translates to reduced inference cost per token and improved user experience through lower latency variance.

Commercial systems including vLLM, TensorRT-LLM, and other production inference engines increasingly incorporate KV cache optimization as a core component. The trade-offs between optimization aggressiveness and model quality drive architectural decisions—more aggressive compression reduces latency but risks quality degradation, while conservative approaches maintain accuracy at higher memory cost.

Effective KV cache optimization requires balancing multiple competing objectives: memory efficiency, computational latency, attention quality, and implementation complexity. Dynamic cache allocation across concurrent requests introduces scheduling challenges analogous to paging systems, with poor decisions incurring significant performance penalties. Speculative decoding and prefix caching add complexity to cache invalidation and sharing logic.

Variable sequence lengths across batches complicate efficient memory utilization, as cache allocation must accommodate worst-case lengths or employ dynamic reallocation. Attention sparsity patterns vary significantly across model architectures, domains, and input types, making universal optimization strategies difficult. Hardware-software co-design remains an open problem, as optimal cache management depends on specific interconnect bandwidth, GPU memory characteristics, and compute patterns.

• KV Cache Compression
• KVCache Management
• vLLM
• GPU Memory and Hardware Optimization
• Inference Optimization