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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThere is no universal KV-cache size or TTL that works across models and workloads. Tune the three controls as one system: set a measured memory budget, choose a TTL that matches freshness and recomputation cost, and select an eviction policy based on observed reuse. Then validate each change against the same representative request trace.
For an LLM, a cache hit is useful only when the cached state belongs to the same model and request context. Model version, tokenizer and template, system prompt, tenant, locale, tool schema, and safety configuration must be part of cache identity. Otherwise, a fast hit can still produce an incorrect answer.
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What you are tuning
A KV cache stores the attention key and value tensors created while processing tokens. Reusing those tensors avoids recomputing a prompt prefix, which can reduce latency and GPU work. The trade-off is memory: longer prefixes and more concurrent sequences consume more cache space, while an oversized cache takes capacity away from model weights, runtime buffers, and other requests.
The three controls answer different questions:
- Size: how much memory may be occupied by cached entries before the system must reclaim space.
- TTL: how long an entry may remain valid without being refreshed or invalidated.
- Eviction: which entries are removed when the cache reaches its limit.
Changing only one control can hide the real problem. A larger cache with an overly long TTL can retain stale prompts. A short TTL can make an effective eviction algorithm look ineffective because entries expire before they are reused. An aggressive eviction policy can create recomputation spikes even when the nominal cache size is adequate.
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Set a KV-cache size from measurements, not a token rule
Start with the memory that can safely be assigned to KV data, then measure how much one cached token or prefix consumes on the target model and runtime. Your budget must leave room for model weights, activations, CUDA or other runtime buffers, communication workspaces, and ordinary allocation overhead. The usable cache ceiling is therefore lower than the device’s advertised VRAM.
Build the budget
- Reserve non-cache memory first. Record the memory used by model weights and the runtime at the concurrency and sequence lengths you intend to support. Include headroom for temporary buffers and allocator fragmentation.
- Measure KV bytes. Measure memory for representative sequence lengths rather than assuming every request has the same prefix. Include any metadata or block-management overhead added by the serving system.
- Model concurrent demand. Estimate the number of active sequences, their prefix lengths, and how much of each prefix is eligible for reuse. A cache sized for one long request can fail under many shorter simultaneous requests.
- Set a hard ceiling. Configure a limit that the cache cannot exceed. In Redis,
maxmemoryis the ceiling for cache data; once usage exceeds it, the configured eviction policy removes entries until usage is below the limit. - Verify under load. Run a representative trace at the intended concurrency and observe peak memory, allocation failures, evictions, and tail latency before increasing the limit.
A useful planning expression is:
cache budget = measured KV bytes per reusable token × reusable tokens held concurrently + cache overhead
That expression is a measurement framework, not a universal sizing formula. The measured bytes vary with model architecture, precision, attention implementation, paging scheme, and runtime. Re-measure after changing any of those variables.
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When to increase or decrease the limit
- Increase it when evictions are frequent, the entries being reclaimed are reused soon afterward, and the additional memory does not push model or runtime allocations into pressure.
- Decrease it when the marginal hit-rate improvement is small, reclaimed entries are rarely reused, or the extra memory increases allocation failures and p95 or p99 latency.
- Keep it stable while diagnosing. Change the size only after capturing a baseline trace; otherwise, a workload change can be mistaken for a sizing improvement.
Choose TTL as a freshness contract
TTL should reflect how long a cached result remains correct, not simply how long storage is available. Shorten it when source data, system prompts, model versions, or safety rules change frequently. Lengthen it for stable prefixes that are expensive to recompute and have a predictable reuse interval.
Fixed expiration
With fixed expiration, an entry receives an expiry time when it is written and expires at that deadline even if it is frequently read. This gives a clear maximum age. It is a good fit when compliance, pricing, policy, or source-data freshness requires a hard bound.
Sliding expiration
A sliding TTL is refreshed by qualifying reads. RedisVL documents that check() refreshes TTL on matched entries when TTL is configured. A popular entry can therefore remain alive indefinitely. Sliding expiration is useful when active entries should survive, but it needs a separate absolute-age or generation rule if data must eventually be forced out.
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Absolute age and generation invalidation
Use an absolute maximum age alongside a sliding window when you want both hot-entry retention and a guaranteed refresh. Generation invalidation is stronger when the reason for expiration is known: increment a generation whenever the model, system prompt, tool schema, or safety configuration changes, and reject entries from older generations. This avoids waiting for a long TTL to expire after a correctness-sensitive change.
Service-specific TTL behavior
Cloudflare Workers KV documents a cacheTtl minimum of 30 seconds and a default of 60 seconds in its 2026 documentation. A value may remain cached in a region until TTL expiry or eviction. The documentation warns that frequently updated data may not reflect remote writes until the cached value expires, so these values are service defaults rather than general recommendations for LLM caches.
Document three TTL facts in your own implementation: whether the clock starts at write or refresh, whether reads refresh the deadline, and whether an administrative or generation invalidation can override the timer. Operators otherwise tend to assume fixed expiration when the system is actually sliding.
Pick eviction by reuse pattern
Eviction decides what happens after the cache reaches its limit. Compare policies using hit rate, stale-hit rate, p95 and p99 latency, recomputation cost, memory footprint, tenant fairness, burst sensitivity, and operational complexity. No policy is best for every request mix.
| Policy | Best fit | Strength | Risk or limitation |
|---|---|---|---|
| LRU | Recent access predicts near-future reuse | Simple baseline that protects recently used prefixes | A burst of one-time traffic can displace entries that have high long-term frequency |
| LFU | A stable, small hot set dominates requests | Frequently reused entries survive bursts of cold traffic | Old frequency counts can preserve entries whose popularity has ended unless aging is handled |
| TTL-first or generation invalidation | Correctness requires a known freshness window or explicit configuration change | Provides a predictable validity boundary | Can discard useful entries before reuse if the freshness window is too short |
| Workload-aware policy | Single-turn, multi-turn, agent, and RAG requests have different reuse intervals | Can protect entries according to request class and observed reuse features | Requires classification, instrumentation, and more operational logic |
Use LRU as the first baseline
LRU is a sensible starting point when a prefix used recently is likely to be used again soon. Establish its hit rate and tail latency under a fixed trace before trying a more complex policy. If the workload contains long cold bursts, inspect whether LRU is evicting a smaller set of consistently popular prefixes.
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Move to LFU when popularity is stable
LFU is appropriate when a small hot set receives most requests and cold traffic arrives in bursts. It protects high-frequency entries better than pure recency. Measure whether the hot set changes over time; a static frequency history can otherwise retain yesterday’s popular entries after demand moves.
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Add workload awareness when request classes differ
The USENIX ATC 2025 study KV Cache in the Wild reports that reuse time and reuse probability vary by request category. A single-turn request, a multi-turn conversation, an agent workflow, and a retrieval-augmented request should not automatically share the same reuse assumptions. If traces show materially different intervals or probabilities, use separate pools, class-aware admission, or class-specific eviction weights rather than forcing one global policy.
Prevent stale or incorrect LLM hits
TTL alone cannot identify every condition that makes a KV entry unsafe. Bind the cache key or metadata to every input that changes the meaning of the prefix:
- model and model version;
- tokenizer and chat-template version;
- system prompt and prompt generation;
- tenant or authorization scope;
- locale and other response-format settings;
- tool definitions and schema versions;
- safety and policy configuration.
When any of these changes, either use a new namespace or increment a generation and reject older entries. This is especially important for multi-tenant systems: sharing a prefix across tenants can leak context even when the token sequence looks identical.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Semantic caches need additional safeguards. Use metadata filters to restrict candidates to compatible model, tenant, locale, and generation values, then apply a distance threshold before accepting a semantically similar entry. A TTL and an eviction policy control lifetime and capacity; they do not prove that two prompts are interchangeable.
Decide whether to offload KV data
LLM KV caches can be tiered across GPU VRAM, host RAM, and external storage. Redis describes an L0/L1/L2-style hierarchy, while AWS discusses external KV tiers and TTL-based invalidation when system prompts change. Tiering increases effective capacity but adds movement, bandwidth, and restoration latency.
Keep hot data in VRAM
VRAM provides the lowest access latency for the active working set. It is the right location for prefixes that are reused often enough to justify occupying scarce accelerator memory. A VRAM-only design is simplest when concurrency and prefix lengths fit comfortably within the device budget.
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Use host RAM as a larger, slower tier
Host RAM can hold less frequently reused entries and reduce VRAM pressure. The cost is transfer time and CPU or interconnect bandwidth when an entry is promoted. Measure restoration latency and tail behavior, not just the larger hit rate; a host hit that stalls a p99 request may be less valuable than a recomputation.
Use external storage for capacity, not as a free speedup
External tiers can preserve more prefixes across a fleet, but network bandwidth, serialization, storage cost, and failure handling become part of the request path. Define which entries are eligible, how long retrieval may block, and what happens when the external tier is unavailable. Treat the external tier as a capacity and durability decision, not an assumption that every hit is faster than recomputation.
When comparing tiers, record restoration latency, bandwidth consumption, storage cost, hit rate by tier, and the p95 or p99 impact of promotions. Eviction and TTL rules should be explicit at each tier so an expired or invalid generation cannot be resurrected from slower storage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Instrument the tuning loop
Capture a representative request trace before changing policy. The trace should include normal traffic, bursts, long and short prefixes, each request class, and the tenants or locales whose reuse differs. Replay the same trace after each single-variable change.
At minimum, measure:
- Hit rate: how often an eligible lookup avoids recomputation.
- Prefix-reuse rate: how much of a request’s prefix is actually reused.
- Eviction rate: how often entries are removed for capacity pressure.
- Entry age: the age distribution of hits and evictions.
- Stale-hit rate: hits later shown to use an expired, superseded, or incompatible generation.
- Recomputation latency: the cost of misses, including token count and accelerator time.
- Tail latency: p95 and p99 separately for cache hits, misses, and tier promotions.
- Cost per request: accelerator, host, network, and storage cost attributable to the cache path.
Break these metrics down by model version, request class, tenant, and tier. Aggregate hit rate can improve while one tenant suffers from unfair eviction or one class experiences a worse p99.
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A repeatable tuning procedure
- Define correctness boundaries. List the fields that make a KV entry incompatible and implement generation or namespace checks before tuning performance.
- Set the initial memory ceiling. Reserve model and runtime memory, measure KV usage, and configure a hard cache limit with operational headroom.
- Capture the baseline trace. Record the metrics above under representative concurrency and prefix lengths.
- Choose a baseline policy. Start with LRU when recency is the most plausible predictor; choose LFU when the trace clearly shows a stable hot set.
- Set a freshness rule. Pick fixed TTL, sliding TTL with an absolute maximum, or generation invalidation according to the data and configuration change process.
- Change one control. Adjust size, TTL, or eviction separately so the result can be attributed to one decision.
- Replay the same trace. Compare hit rate, stale-hit rate, tail latency, eviction rate, recomputation, and cost—not just one headline metric.
- Check burst and recovery behavior. Test cold-starts, traffic spikes, model rollouts, prompt changes, and tier outages. Confirm that invalid generations are rejected and that the system degrades safely to recomputation.
- Promote only measured improvements. Keep the change when it improves the target metric without unacceptable freshness, fairness, memory, or cost regressions; otherwise revert and test the next control.
Common symptoms and corrective actions
High eviction rate with low hit rate
Check whether entries are too large, concurrency is higher than planned, or a burst of one-time requests is polluting the cache. Increase the budget only if reclaimed entries are reused soon. Otherwise, add admission filtering or a workload-aware pool so cold traffic does not displace the reusable set.
High hit rate but stale answers
Inspect whether reads refresh TTL, whether the cache has an absolute age, and whether model or prompt generations are part of identity. Replace an unbounded sliding window with a maximum age or generation invalidation where freshness is mandatory.
Good average latency but poor p99
Separate VRAM hits from host or external-tier promotions and from misses. A larger cache may raise the average hit rate while adding slow restoration paths. Cap promotion work, keep the hottest set in VRAM, or accept recomputation for entries whose retrieval cost exceeds recomputation.
Hot entries disappear after a traffic burst
Compare LRU and LFU on the same burst trace. If popularity is stable, LFU can protect the hot set. If request classes have different reuse intervals, isolate them or use class-aware admission instead of relying on one global queue.
Old answers survive a model or policy rollout
Invalidate by generation or namespace at rollout time. Do not rely on waiting for TTL expiry when a changed system prompt, model version, tool schema, or safety rule can alter correctness immediately.
Operational checklist
- Measure KV bytes for the actual model, precision, runtime, and prefix lengths.
- Reserve memory for weights, activations, runtime buffers, and allocator overhead before setting the cache limit.
- Configure a hard ceiling and alert on memory pressure, eviction rate, and allocation failures.
- Document whether TTL is fixed, sliding, or both, and record the service’s minimum and default behavior where applicable.
- Implement absolute age or generation invalidation for entries that cannot remain valid indefinitely.
- Include model, tokenizer/template, prompt, tenant, locale, tool, and safety identity in keys or metadata.
- Use semantic distance thresholds and metadata filters in addition to TTL and eviction.
- Compare LRU, LFU, and workload-aware approaches using the same trace and the same metric set.
- Measure tier restoration latency, bandwidth, and cost before moving entries out of VRAM.
- Re-test after model, prompt, traffic, hardware, or runtime changes.
Bottom line
Size the cache from measured KV consumption and a hard memory budget. Set TTL to the actual freshness and recomputation contract, remembering that sliding refreshes can keep hot entries alive indefinitely. Start with LRU or LFU only as evidence-based baselines, then move to generation-aware or workload-aware behavior when traces justify it. For LLM serving, correctness identity and invalidation are as important as hit rate, and every tier beyond VRAM must be evaluated on tail latency as well as capacity.
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