InstCache: A Predictive Cache for LLM Serving

• URL: http://arxiv.org/abs/2411.13820v2
• Date: Mon, 14 Jul 2025 02:22:43 GMT
• Title: InstCache: A Predictive Cache for LLM Serving
• Authors: Longwei Zou, Yan Liu, Jiamu Kang, Tingfeng Liu, Jiangang Kong, Yangdong Deng,
• Abstract summary: Caching techniques offer opportunities to optimize the performance of Large Language Models inference engines.<n>High variability in the content and length of instructions make it rare for identical instructions to recur within a short time window.<n>We propose InstCache, a predictive caching mechanism for LLM serving systems.
• Score: 6.076957323090607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The revolutionary capabilities of Large Language Models (LLMs) are attracting rapidly growing popularity and leading to soaring user requests to inference serving systems. Caching techniques, which leverage data reuse to reduce computation, offer opportunities to optimize the performance of LLM inference engines. On the one hand, the low-level key-value (KV) cache working at the token level is widely adopted, albeit it incurs significant overhead as request volume grows. On the other hand, instruction-level caching, which stores full instruction-response pairs, is expected to play an increasingly crucial role. However, the high variability in the content and length of instructions make it rare for identical instructions to recur within a short time window, presenting challenges for effective caching instruction-response pairs. To address this challenge, we propose InstCache, a predictive caching mechanism for LLM serving systems. Leveraging the capability of LLMs, we can effectively reorder the representation space of instruction texts and develop a sufficient level of spatial locality. Such spatial locality enables us to predict potential instructions located in a compact region in the space, resulting in an effective caching system at runtime. Experimental results demonstrate that InstCache achieves a 2.3x higher hit rate compared to the upper bound of traditional caching mechanisms on WildChat dataset and reduces the time per output token of vLLM by up to 42.0% and 50.0% on LMSys and Moss datasets, respectively.

Related papers

• Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction [58.044803442346115]
  Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and parallel decoding but suffer from prohibitive computational complexity and memory overhead during inference.<n>We propose Sparse-dLLM, the first training-free framework integrating dynamic cache eviction with sparse attention via delayed bidirectional sparse caching.
  arXiv  Detail & Related papers  (2025-08-04T16:14:03Z)
• LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models [52.56008278458534]
  LaCache is a training-free method for efficient and accurate generative inference of Large Language Models.<n>LaCache enables LLMs to address both of the critical challenges in long-range modeling: robust long-range capabilities and continuous generation without running out-of-memory.
  arXiv  Detail & Related papers  (2025-07-14T19:09:57Z)
• A Generative Caching System for Large Language Models [1.2132389187658934]
  Caching has the potential to be of significant benefit for accessing large language models (LLMs) This paper presents a new caching system for improving user experiences with LLMs. A key feature we provide is generative caching, wherein multiple cached responses can be synthesized to provide answers to queries which have never been seen before.
  arXiv  Detail & Related papers  (2025-03-22T01:17:56Z)
• CSR:Achieving 1 Bit Key-Value Cache via Sparse Representation [63.65323577445951]
  We propose a novel approach called Cache Sparse Representation (CSR)<n>CSR transforms the dense Key-Value cache tensor into sparse indexes and weights, offering a more memory-efficient representation during LLM inference.<n>Our experiments demonstrate CSR achieves performance comparable to state-of-the-art KV cache quantization algorithms.
  arXiv  Detail & Related papers  (2024-12-16T13:01:53Z)
• XKV: Personalized KV Cache Memory Reduction for Long-Context LLM Inference [9.65524177141491]
  Large Language Model (LLM) inference generates output tokens one-by-one, leading to many redundant computations. KV-Cache framework makes a compromise between time and space complexities. Existing studies reduce memory consumption by evicting some of cached data that have less important impact on inference accuracy. We show that customizing the cache size for each layer in a personalized manner can yield a significant memory reduction.
  arXiv  Detail & Related papers  (2024-12-08T11:32:08Z)
• Compute Or Load KV Cache? Why Not Both? [6.982874528357836]
  Cake is a novel KV cache loader, which employs a bidirectional parallelized KV cache generation strategy. It simultaneously and dynamically loads saved KV cache from prefix cache locations and computes KV cache on local GPU. It offers up to 68.1% Time To First Token (TTFT) reduction compare with compute-only method and 94.6% TTFT reduction compare with I/O-only method.
  arXiv  Detail & Related papers  (2024-10-04T01:11:09Z)
• ThinK: Thinner Key Cache by Query-Driven Pruning [63.13363917871414]
  Large Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications. This paper focuses on the long-context scenario, addressing the inefficiencies in KV cache memory consumption during inference. We propose ThinK, a novel query-dependent KV cache pruning method designed to minimize attention weight loss while selectively pruning the least significant channels.
  arXiv  Detail & Related papers  (2024-07-30T17:59:08Z)
• Efficient Inference of Vision Instruction-Following Models with Elastic Cache [76.44955111634545]
  We introduce Elastic Cache, a novel strategy for efficient deployment of instruction-following large vision-language models. We propose an importance-driven cache merging strategy to prune redundancy caches. For instruction encoding, we utilize the frequency to evaluate the importance of caches. Results on a range of LVLMs demonstrate that Elastic Cache not only boosts efficiency but also notably outperforms existing pruning methods in language generation.
  arXiv  Detail & Related papers  (2024-07-25T15:29:05Z)
• PQCache: Product Quantization-based KVCache for Long Context LLM Inference [27.523568511043273]
  Key-Value Cache (KVCache) is a crucial component in Large Language Models (LLMs) Current methods selectively determine suitable keys and values for self-attention in LLMs to address the issue. We propose PQCache, which employs Product Quantization (PQ) to manage KVCache, maintaining model quality while ensuring low serving latency.
  arXiv  Detail & Related papers  (2024-07-01T13:05:42Z)
• Training-Free Exponential Context Extension via Cascading KV Cache [49.608367376911694]
  We introduce a novel mechanism that leverages cascading sub-cache buffers to selectively retain the most relevant tokens. Our method reduces prefill stage latency by a factor of 6.8 when compared to flash attention on 1M tokens.
  arXiv  Detail & Related papers  (2024-06-24T03:59:17Z)
• MiniCache: KV Cache Compression in Depth Dimension for Large Language Models [48.03117580340151]
  Key-Value ( KV) cache stores key-value states of previously generated tokens. The size of the KV cache grows linearly with sequence length, posing challenges for applications requiring long context input and extensive sequence generation. We present a simple yet effective approach, called MiniCache, to compress the KV cache across layers from a novel depth perspective.
  arXiv  Detail & Related papers  (2024-05-23T09:43:52Z)
• Efficient LLM Inference with Kcache [3.945956673130761]
  Large Language Models (LLMs) have had a profound impact on AI applications. KV Cache technology is one of the most widely used techniques in the industry. We propose a novel KCache technique to alleviate the memory bottleneck issue during the LLMs inference process.
  arXiv  Detail & Related papers  (2024-04-28T03:11:42Z)
• MeanCache: User-Centric Semantic Cache for Large Language Model Based Web Services [8.350378532274405]
  Caching is a natural solution to reduce inference costs on repeated queries. This paper introduces MeanCache, a user-centric semantic cache for LLM-based services. MeanCache identifies semantically similar queries to determine cache hit or miss.
  arXiv  Detail & Related papers  (2024-03-05T06:23:50Z)
• Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference [78.65321721142624]
  We focus on a memory bottleneck imposed by the key-value ( KV) cache. Existing KV cache methods approach this problem by pruning or evicting large swaths of relatively less important KV pairs. We propose LESS, a simple integration of a constant sized cache with eviction-based cache methods.
  arXiv  Detail & Related papers  (2024-02-14T18:54:56Z)
• KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache [67.9776980972508]
  We develop a tuning-free 2bit KV cache quantization algorithm named KIVI. KIVI can enable Llama, Falcon, and Mistral models to maintain almost the same quality while using $mathbf2.6times$ less peak memory.
  arXiv  Detail & Related papers  (2024-02-05T06:06:47Z)
• Accelerating Deep Learning Classification with Error-controlled Approximate-key Caching [72.50506500576746]
  We propose a novel caching paradigm, that we named approximate-key caching. While approximate cache hits alleviate DL inference workload and increase the system throughput, they however introduce an approximation error. We analytically model our caching system performance for classic LRU and ideal caches, we perform a trace-driven evaluation of the expected performance, and we compare the benefits of our proposed approach with the state-of-the-art similarity caching.
  arXiv  Detail & Related papers  (2021-12-13T13:49:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.