Key-value cache stores attention key and value tensors already computed for earlier tokens during autoregressive inference. It prevents every decode step from recomputing all previous Transformer states, but its capacity and bandwidth frequently become the dominant LLM-serving constraint. At each layer, a new query attends to cached keys and values for allowed prior positions; the cache grows linearly with active sequence length even though attention work grows with the number of query-key interactions. A production definition states the base model and revision, tokenizer and vocabulary, context and output limits, numerical precision, data provenance, objective, trainable state, inference runtime, tool or retrieval boundary, evaluation population, latency and cost target, failure policy, and reproducibility artifacts. Similar labels can hide materially different implementations, so exact interfaces and assumptions belong in the contract. A useful capacity estimate is two times layer count times sequence length times KV-head count times head dimension times bytes per element, then multiplied by active sequences and adjusted for padding, allocator pages, metadata, prefix sharing, speculative branches, and pipeline/tensor sharding.

Architecture, representation, and operating mechanism. Prefill computes many tokens in parallel and populates cache; decode appends one or a few positions per sequence. Standard multi-head attention stores K/V per query head, grouped-query attention shares K/V within groups, and multi-query attention shares one or very few K/V heads. A serving scheduler allocates cache blocks, maps logical positions to physical pages, batches active sequences, reads relevant blocks into attention kernels, appends new states, forks or shares prefixes, reclaims completed blocks, and may evict or recompute under pressure. Paged attention removes large contiguous reservations, prefix caching shares common prompt blocks, sliding-window attention retains recent tokens, KV quantization uses lower-bit formats, cache compression prunes or merges states, and offload moves cold blocks to host memory or storage at latency cost. The complete stack includes input normalization, tokenization, embeddings, Transformer blocks, attention and KV state, output decoding, adapters or post-training weights, retrieval and tools where used, orchestration, policy controls, telemetry, and artifact storage. Data, control, and trust boundaries should remain visible instead of being collapsed into a single model call. Evaluation keeps task quality beside factuality, calibration, robustness, safety, subgroup behavior, context utilization, throughput, time to first token, inter-token latency, tail latency, memory, bandwidth, accelerator utilization, energy, and cost. Controlled comparisons hold prompts, sampling, data, model, hardware, concurrency, and judge protocol fixed and report uncertainty across repeated runs.

Implementation, serving infrastructure, and failure modes. Track cache at block granularity; separate allocated, reserved, used, and fragmented bytes; align layouts with fused attention kernels; support preemption and copy-on-write; validate rotary positions and sequence offsets; and isolate tenant data when pages are recycled. Decode repeatedly streams K/V through HBM and is often bandwidth bound. Larger HBM supports more concurrent context, wider bandwidth raises token rate, local SRAM tiles attention, and PCIe offload is much slower than device memory. GQA, MQA, FP8 or INT8 cache reduce bytes. Fragmentation rejects requests despite free bytes, stale blocks leak another sequence, position indices corrupt attention, quantization harms long-context retrieval, prefix hashes collide, offload thrashes, or a scheduler admits more context than bandwidth can serve. Implementation starts with a small explicit reference, typed schemas, deterministic fixtures, versioned prompts and templates, and traceable input-output examples. Production adds batching, streaming, mixed precision, compilation, caching, parallelism, retries, fallbacks, rate limits, redaction, isolation, and observability without changing semantics silently. Accelerators execute dense and sparse tensor kernels while HBM stores weights, activations, adapters, and KV state; CPUs tokenize and orchestrate; host memory, storage, PCIe, scale-up fabric, and scale-out networks move artifacts and requests. Batch, sequence length, vocabulary, precision, cache locality, communication, and power determine delivered rather than peak behavior. Typical failures include data leakage, template mismatch, tokenizer drift, train-serving skew, stale caches, unsupported operators, precision loss, memory fragmentation, prompt injection, malformed structured output, tool side effects, runaway loops, evaluation contamination, hidden retries, and average metrics that conceal catastrophic tails. A fluent answer is not evidence of correctness.

Evaluation, security, and lifecycle controls. Compare cached and full recomputation logits, prefill and incremental decode, variable lengths, left/right padding, beam forks, prefix reuse, eviction, cancellation, quantized formats, tensor parallelism, and memory-pressure recovery. Bytes per token and request, active tokens, fragmentation, block utilization, cache hit rate, HBM bandwidth, prefill and decode throughput, time to first token, inter-token latency, eviction, and quality by distance matter. KV state can contain sensitive prompt-derived representations; enforce tenant isolation, zeroization or safe reuse, retention limits, encrypted/offloaded storage controls, and audit of prefix sharing. Verification combines unit and property tests, reference parity, adversarial and edge-case prompts, schema validation, deterministic replay, offline benchmark suites, human review, safety red teaming, privacy and security tests, load and fault injection, long-context checks, shadow traffic, canary rollout, and rollback drills. Every result links to the exact model, data, tokenizer, configuration, code, and runtime. Collection, filtering, training or tuning, evaluation, registration, deployment, monitoring, incident response, refresh, rollback, retention, deletion, and retirement form one lifecycle. Model cards, data and prompt lineage, approvals, exceptions, dependencies, licenses, checkpoints, adapter versions, tool permissions, and evaluation evidence remain auditable. Owners define intended and prohibited use, access and tenant isolation, data minimization, consent or lawful basis, secret handling, human confirmation for consequential actions, rate and spend limits, abuse monitoring, appeal and escalation, retention, and incident responsibility. External model or framework behavior is treated as an untrusted dependency with pinned versions and compensating controls.

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Selection and practical application. Use GQA/MQA when model architecture permits, paged allocation for dynamic serving, lower-bit KV after long-context validation, prefix caching for repeated prompts, and offload only with explicit latency policy. Chat, code completion, agents, retrieval-augmented generation, long-document analysis, and multi-turn assistants rely on KV cache. The CFS KV-Cache Simulator at /kvcache exposes capacity and bandwidth tradeoffs directly. KV design interacts with attention architecture, context window, batch scheduler, quantization, HBM, parallelism, speculative decoding, prefix policy, admission control, and service-level objectives. The useful optimization boundary is the end-to-end application: user interface, model, tokenizer, context builder, cache, adapter, retriever, tools, runtime, accelerator, scheduler, network, policy, monitoring, and human workflow. Improving one component can move the bottleneck or weaken correctness, safety, isolation, and recoverability elsewhere. A production definition states the base model and revision, tokenizer and vocabulary, context and output limits, numerical precision, data provenance, objective, trainable state, inference runtime, tool or retrieval boundary, evaluation population, latency and cost target, failure policy, and reproducibility artifacts. Similar labels can hide materially different implementations, so exact interfaces and assumptions belong in the contract. Evaluation keeps task quality beside factuality, calibration, robustness, safety, subgroup behavior, context utilization, throughput, time to first token, inter-token latency, tail latency, memory, bandwidth, accelerator utilization, energy, and cost. Controlled comparisons hold prompts, sampling, data, model, hardware, concurrency, and judge protocol fixed and report uncertainty across repeated runs. CFS connects this topic to semiconductor architecture, implementation, verification, manufacturing, packaging, test, and deployed AI-system tradeoffs across the platform.