KV cache — short for key-value cache — is an inference optimization technique used by transformer-based language models that stores the intermediate attention computations from previously processed tokens so they do not need to be recalculated when generating each subsequent token. In a transformer model, every new token generated requires the model to compute attention relationships across the entire preceding sequence. Without a KV cache, this means reprocessing the full input from scratch for every token output — an operation that grows increasingly expensive as context length increases. With a KV cache, those computations are saved in memory and retrieved rather than recomputed, reducing the cost of inference on long sequences by orders of magnitude.
Think of it like a skilled translator working through a document. Without notes, they would need to re-read the entire document from the beginning every time they needed to render the next sentence — an approach that gets slower the longer the document gets. With notes, they write down key terms and context decisions as they go, and consult those notes rather than re-reading from scratch. The KV cache is those notes: stored work that makes each subsequent step faster rather than costlier.
For enterprise AI deployments, KV cache has direct and significant implications for inference cost and performance. The size of the KV cache determines how long a context an AI system can process efficiently, and KV cache memory consumption often exceeds model weight memory for long-context workloads — meaning that KV cache management, not model complexity, becomes the primary hardware constraint at scale. Understanding KV cache helps enterprise leaders evaluate AI infrastructure decisions, inference serving costs, and the realistic performance characteristics of long-context AI applications in production.
Imagine you ask a researcher to read a 100-page report and summarize each section as they go. If they have an eidetic memory and can hold everything they've already read in mind without re-reading, each new section takes only the time needed to process that new section. If they must re-read the entire report from the beginning each time, the tenth section takes ten times as long as the first, and the hundredth section takes one hundred times as long. The KV cache gives language models the equivalent of that working memory for the portions of the input they've already processed.
In transformer architecture, each attention layer computes three matrices from the input tokens: queries (Q), keys (K), and values (V). Attention determines how much each token should "attend" to every other token by comparing queries against keys, then using the result to weight the corresponding values. When generating a new token, the model only needs to compute the new token's query — but it still needs the full history of keys and values from all previous tokens to compute attention correctly. The KV cache stores these key and value matrices from prior tokens, so the model retrieves them from memory rather than recomputing them. Each new token adds its own key and value to the cache. The cache grows linearly with sequence length and, for large models, can consume tens to hundreds of gigabytes of GPU memory for long contexts. This creates a fundamental tradeoff: larger context windows require more cache memory, which reduces the number of concurrent requests that can be served from a given hardware configuration. Inference platforms address this through techniques including prefix caching (sharing KV cache across requests that share a common prompt prefix), quantized KV caches (reducing precision of cached values to save memory), and KV cache eviction policies (dropping less-recently-accessed entries when memory pressure is high).
In enterprise document processing — contract review, financial report analysis, regulatory compliance screening — KV caching is what makes it economically viable to process long documents at scale. When an AI system must reason over a 50-page contract, the KV cache stores the document's computed attention representations so that each follow-up question or analysis step can proceed efficiently without reprocessing the full document from scratch. Inference platforms that implement efficient KV cache management can process long documents at 10-100x lower cost per analysis step than systems without caching, which is the difference between a useful enterprise tool and one that is too expensive to operate at volume.
For agentic AI workflows where an agent operates over an extended conversation or task sequence — iterating on a code generation task, conducting a multi-step research workflow, or managing a customer service conversation across many turns — the KV cache grows across the session. Inference providers with prefix caching enabled can preserve the KV cache for shared portions of the system prompt and prior conversation history across requests within the same session, reducing latency for each subsequent agent step. Enterprise platforms building long-running agent workflows should evaluate inference providers' KV cache and prefix caching capabilities as a primary selection criterion, as the cost and latency differences between platforms with strong versus weak cache management are significant at production scale.
KV caching is not a new invention — it is a logical consequence of the transformer's attention mechanism, which computes key and value matrices from all tokens in the context window to determine each token's output. Since Vaswani et al.'s foundational "Attention Is All You Need" paper in 2017, KV caching has been a standard implementation detail of transformer inference: any efficient inference implementation naturally caches prior keys and values rather than recomputing them. What changed in 2023-2024 was not the concept but the scale and sophistication of cache management as context windows expanded from 4K to 128K tokens and beyond, and as inference at scale became a serious engineering and cost problem.
The emergence of commercial AI inference at scale prompted significant engineering investment in KV cache optimization. Prefix caching — sharing KV cache across requests with common prefixes — was popularized by inference frameworks including vLLM (2023) and became a standard feature of commercial inference APIs in 2024. Quantized KV caches (INT8/INT4 precision for cached values) became a standard memory reduction technique. Context window sizes have expanded from 4K to 1M+ tokens (Google Gemini 1.5 Pro, 2024), raising new challenges for KV cache memory management at extreme lengths. Speculative decoding techniques that use draft models to propose tokens also interact with KV cache design in novel ways. As of 2025, KV cache management is one of the central engineering problems in production LLM serving, with active research on sparse attention patterns, hierarchical caching, and offloading strategies that move cache to cheaper memory tiers (CPU RAM, NVMe storage) when GPU memory is insufficient.
KV cache is the mechanism that makes transformer-based language model inference fast enough to be practical: it stores the key and value attention matrices computed from prior tokens so they are retrieved rather than recomputed for each new token generated. Without KV caching, inference cost and latency would scale quadratically with context length, making long-document processing and extended agentic workflows economically inviable at scale. With KV caching — and advanced techniques like prefix caching and quantized caches — inference providers can offer competitive per-token pricing even for large context windows.
For enterprise leaders, KV cache is relevant at the infrastructure evaluation level: the quality of KV cache management in an inference platform directly determines serving cost, latency, and maximum throughput for long-context workloads. When evaluating AI inference vendors, providers, or on-premise serving platforms, enterprise architects should ask specifically about prefix caching support, maximum KV cache sizes, and cache eviction policies — as these implementation details translate directly into operating cost differences that compound significantly at scale. The cost gap between well-optimized and poorly-optimized KV cache management on a high-volume enterprise workload can easily exceed seven figures annually.
