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The right KV-cache configuration is not a universal token count or a single “best” policy. Set a measured memory budget, choose a TTL that matches how quickly the underlying answer can become stale, and select eviction based on observed reuse. Then validate all three against the same representative traffic trace.
This guide explains how to size a cache for VRAM, host RAM, or external storage; distinguish sliding TTLs from absolute expiration; choose LRU, LFU, TTL-first, generation-based, or workload-aware eviction; and prevent fast but incorrect hits caused by model, prompt, tenant, or safety-policy changes.
What you are tuning
An LLM KV cache stores the intermediate key and value tensors created while processing a prompt prefix. When a later request presents the same reusable prefix, the runtime can restore those tensors instead of recomputing them. The benefit is lower prompt-processing latency and less compute, but every retained entry consumes memory and can become invalid when the model or the data behind the answer changes.
Three controls determine the trade-off:
- Size is the hard capacity available to cache data. It must leave room for model weights, activations, runtime buffers, and non-cache allocations.
- Time to live (TTL) defines how long an entry may be used before expiration. Depending on the implementation, a matching read can refresh the timer, creating a sliding window rather than a fixed expiry time.
- Eviction decides which entries leave first when the cache reaches its limit. Recency, frequency, age, generation, and request class can all be valid signals.
Tuning one control in isolation often produces a misleading result. A larger cache can increase hit rate while exhausting VRAM; a long TTL can preserve a high hit rate while serving obsolete prompt results; and an LRU policy can discard a small, stable hot set when a burst of one-time requests arrives.
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Establish a safe KV-cache size
Start with a hard memory budget
Choose the maximum cache allocation from the memory that remains after the model and runtime are reserved. Redis describes maxmemory as the ceiling for cache data and evicts entries until usage falls below that limit. The same principle applies to an in-process or GPU-resident cache: define a limit that the cache cannot exceed, rather than allowing it to consume every free byte.
Do not treat a vendor’s advertised token capacity as your budget. KV usage changes with model architecture, precision, layer count, sequence length, batch or concurrency, and implementation overhead. Measure the actual bytes allocated by your serving stack for representative prefixes.
Measure the variables that drive occupancy
Capture a trace containing the request classes you expect in production. At minimum, record:
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- KV bytes allocated per cached prefix and how that number changes with prefix length.
- Concurrent requests and the number of simultaneous prefixes retained.
- Prompt or prefix length distribution, including long-tail requests.
- Memory already consumed by weights, activations, kernels, runtime workspaces, and networking.
- Eviction events, reclaimed bytes, and whether reclaimed entries are requested again soon.
Use the trace to estimate a working budget rather than a theoretical maximum. Keep explicit headroom for transient allocations and traffic spikes. A cache that fits only at average concurrency can trigger out-of-memory failures when several long prompts arrive together.
Use reuse, not capacity, as the sizing test
Increase the limit in controlled increments and compare the marginal gain in hit rate or prefix-reuse rate with the additional memory consumed. If evictions are frequent and the evicted entries are requested again shortly afterward, the cache is undersized for that workload. If adding capacity produces little additional reuse while memory pressure rises, the extra space is not earning its keep.
Track p95 and p99 latency as well as average latency. A cache that improves the mean but causes memory contention can worsen tail latency. Also track recomputation latency and cost per request; a hit is valuable only when the saved work exceeds the memory and restoration cost of retaining the entry.
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Account for multiple tenants and request classes
A single global pool can allow one tenant, agent workflow, or long-context request class to crowd out others. Measure fairness by tenant and by request category. If one group dominates the cache, reserve separate budgets or apply quotas before changing the global limit. The correct size is therefore a policy decision about who receives reuse, not merely a hardware specification.
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Set TTL as a freshness contract
Match expiration to the source of change
TTL should reflect the fastest event that can make a cached result unsafe or misleading. Shorten it when source documents, system prompts, model versions, tools, or safety rules change frequently. Lengthen it for stable, expensive-to-recompute prefixes whose inputs remain unchanged for long periods. A long TTL is not automatically efficient if it creates stale hits that must be corrected downstream.
Write the freshness requirement in operational terms: for example, whether a request may observe an older system prompt, or how soon a changed retrieval corpus must affect responses. Then choose a TTL that satisfies that requirement and measure stale-hit rate after deployment.
Distinguish fixed and sliding TTLs
Some systems start a timer when an entry is written and let it expire at a fixed time. Others refresh the timer whenever a matching read occurs. RedisVL documents the latter behavior for configured TTLs: check() refreshes TTL on matched entries. This sliding-window behavior can keep a popular entry alive indefinitely, even when its original generation is old.
If you use sliding expiration, pair it with an absolute age limit or a generation invalidation rule. A practical design stores both an idle-expiry timestamp and a creation or generation timestamp. Reads may extend the idle window, but no read can extend the entry beyond the absolute freshness boundary.
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Cloudflare Workers KV documentation lists a cacheTtl minimum of 30 seconds and a default of 60 seconds. Those values describe that service, not a universal recommendation for LLM KV caches. The documentation also warns that frequently updated data may remain stale in a region until TTL expiry or eviction, so a TTL is unsuitable when callers must see remote writes shortly after they occur.
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Use provider defaults only as implementation constraints. Your application’s freshness contract still determines the appropriate value, and you may need explicit invalidation when a change must take effect immediately.
Prevent incorrect hits with cache identity and generations
A byte-for-byte prefix match is not sufficient evidence that a cached state is valid. Bind every cache key to the inputs that affect tokenization, interpretation, or policy:
- Model name and deployed model version.
- Tokenizer and chat-template version.
- System prompt and its generation identifier.
- Tenant or authorization scope.
- Locale and other response-affecting settings.
- Tool definitions and schema versions.
- Safety configuration and policy generation.
When any of these changes, either include the new value in the key or invalidate the old generation. Generation invalidation is usually safer than waiting for TTL when a model or safety rule changes globally. It also avoids deleting every physical object synchronously: a reader can reject entries whose generation no longer matches while background cleanup reclaims their memory.
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Semantic caches require additional safeguards. Redis documentation describes combining metadata filters and distance thresholds with TTL and eviction. A semantically similar request can still require a different tenant scope, locale, tool set, or policy. Similarity is a retrieval signal, not permission to bypass identity checks.
Choose an eviction policy from observed reuse
LRU: a strong first baseline
Least recently used (LRU) removes entries that have gone the longest without a hit. It is a sensible baseline when recent access predicts near-future reuse, such as repeated prefixes in an interactive application. LRU can perform poorly when a burst of one-time requests displaces a small set that is accessed steadily but less recently.
LFU: protect a stable hot set
Least frequently used (LFU) favors entries with many accesses. It is useful when a small, stable group of prefixes should survive bursts of cold traffic. LFU needs an aging strategy in practice: without decay or another way to reduce old counts, an entry that was popular months ago can retain priority after its usefulness has ended. Verify how your implementation handles counter aging before relying on LFU.
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TTL-first and generation invalidation
When correctness requires a known freshness window, expiration can take precedence over recency or frequency. TTL-first behavior prevents an old entry from surviving merely because it is popular. Generation invalidation is appropriate when a model, system prompt, tool schema, or safety policy changes; the generation mismatch makes an entry unusable immediately, independent of its remaining TTL.
Workload-aware policies
Single-turn, multi-turn, agent, and retrieval-augmented requests can have different reuse intervals and probabilities. The 2025 USENIX Association study KV Cache in the Wild reports workload-skewed reuse and concludes that eviction should be tailored to reuse features rather than chosen as a universal rule. A workload-aware policy can assign separate pools, weights, or admission rules to request classes when one policy cannot serve them fairly.
| Policy or control | Use when | Main risk to measure |
|---|---|---|
| LRU | Recent access predicts near-future reuse | Burst traffic can displace a stable hot set |
| LFU | A small, persistent hot set should survive cold bursts | Historical counts can outlive current usefulness |
| TTL-first | Freshness must end after a defined window | Useful entries may be recomputed after expiry |
| Generation invalidation | Model, prompt, tool, or safety generation changes | Requires complete and consistent generation labeling |
| Workload-aware | Request categories have different reuse behavior | More configuration and monitoring complexity |
Compare policies with the metrics that matter
Do not select a policy from hit rate alone. Compare candidates on the same replay or production trace and record:
- Hit rate and prefix-reuse rate: how often cached state is usable and how much prompt work it replaces.
- Stale-hit rate: the fraction of hits that violate the freshness or generation contract.
- p95 and p99 latency: include lookup, restoration, and any recomputation path.
- Eviction rate and reuse-after-eviction: whether the policy discards entries that are soon needed again.
- Entry age: how long retained objects actually live, not just their configured TTL.
- Recomputation latency and cost per request: the work caused by misses.
- Memory footprint and fairness: whether one tenant or request class receives disproportionate capacity.
- Operational complexity: extra counters, invalidation paths, and failure modes.
Change one control at a time. Keep the trace, model version, tokenizer, prompt generation, and traffic mix constant while comparing alternatives. Otherwise, a simultaneous TTL and policy change can hide which control produced an improvement or regression.
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LLM KV caches can be tiered across GPU VRAM, host RAM, and external storage. Redis describes this as an L0/L1/L2-style hierarchy, and AWS discusses external KV tiers with TTL-based invalidation when system prompts change. Tiering increases effective capacity but adds movement, bandwidth, and restoration latency.
| Tier | Strength | Trade-off to evaluate |
|---|---|---|
| GPU VRAM | Fastest access for active inference | Scarce capacity and contention with model/runtime memory |
| Host RAM | More capacity than VRAM for warm entries | Transfer latency and host-memory pressure |
| External storage or service | Largest shared capacity and persistence options | Network bandwidth, restoration latency, and storage cost |
Use a measured promotion and demotion rule. Keep entries in VRAM when restoration is frequent and latency-sensitive; place colder but still valuable entries in host RAM or an external tier. Include tier-transfer latency in p95 and p99 measurements. A hit that requires a slow restore may save less time than recomputation, especially for short prefixes.
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Apply the same identity, TTL, and generation checks in every tier. Moving an invalid entry from external storage to VRAM wastes bandwidth and can reintroduce stale state. Invalidate logically before reclaiming physical copies.
A repeatable tuning procedure
- Define correctness. List which changes must invalidate a prefix immediately and which may wait for TTL expiry. Include model, tokenizer/template, system prompt, tenant, locale, tools, and safety settings.
- Measure allocation. Run a representative trace and capture KV bytes by prefix length, concurrency, request class, and tier. Record memory used by weights and runtime buffers so the cache budget has headroom.
- Set a bounded starting size. Enforce a hard limit below the available remainder. Confirm that peak concurrency does not cause out-of-memory errors when long prompts arrive together.
- Instrument the baseline. Collect hit rate, prefix-reuse rate, eviction rate, entry age, stale-hit rate, recomputation latency, tail latency, and cost per request.
- Choose an initial policy. Start with LRU when recency is the only established reuse signal. Use LFU for a demonstrably stable hot set, TTL or generation rules for correctness boundaries, and separate workload policies when categories behave differently.
- Set TTL from freshness. Select the shortest value that meets the freshness contract without discarding expensive, stable work unnecessarily. Determine whether reads refresh TTL and add an absolute age or generation limit if they do.
- Replay the same trace. Change only size, TTL, or eviction in each experiment. Compare the full metric set, including stale hits and tail latency.
- Stress failure paths. Test model deployment, system-prompt edits, safety-rule changes, tenant isolation, regional writes, cache-tier outages, and memory pressure. Verify that invalid entries are rejected before use.
- Roll out gradually. Monitor eviction storms, restoration bandwidth, stale-hit rate, and per-tenant fairness. Keep a rollback setting for the previous size, TTL, and policy.
Diagnose common tuning failures
Hit rate is low and evictions are high
Check whether entries evicted early are requested again soon. If they are, increase the budget, improve admission, or separate request classes. If they are not, the workload may simply have little prefix reuse; adding memory will not create reuse that does not exist.
Memory pressure appears despite a configured limit
Verify that the limit covers all cache representations and that model weights, runtime buffers, transfers, and fragmentation remain outside the cache budget. For a tiered design, inspect each tier independently. A limit on external cache data does not protect GPU VRAM from restored entries.
Answers remain stale after an update
Determine whether the implementation uses sliding TTL. A frequently matched entry may never become idle long enough to expire. Add an absolute age or generation check, and increment the generation when the model, system prompt, source data contract, tool schema, or safety rules change.
A fast hit is incorrect
Audit the key fields and metadata filters. Confirm model version, tokenizer/template, tenant, locale, tool schema, system-prompt generation, and safety configuration are all bound to the entry. For semantic matching, tighten distance thresholds or metadata filters before increasing cache size.
Latency worsens after moving entries off GPU
Measure restoration latency and bandwidth separately from lookup latency. Promote frequently restored entries back to VRAM, keep only less latency-sensitive prefixes in host RAM or external storage, or recompute short prefixes when restoration costs more than the saved computation.
Practical decision framework
- If recent requests are the best predictor of reuse, begin with LRU and validate against burst traces.
- If a small hot set dominates access, test LFU with counter aging and monitor whether old popularity persists.
- If serving old state is unacceptable, enforce TTL and generation invalidation before optimizing hit rate.
- If single-turn, multi-turn, agent, and RAG traffic show different reuse intervals, separate pools or use a workload-aware policy.
- If VRAM is the bottleneck but reuse remains valuable, add host-RAM or external tiers and include restoration latency, bandwidth, and storage cost in the decision.
The durable operating model is a feedback loop: measure real reuse, enforce a safe budget, align expiration with freshness, invalidate by generation when identity changes, and reevaluate with the same trace after each change. No universal cache-size or TTL number can replace those measurements because the optimal values depend on the model, hardware, traffic, and freshness requirements.
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