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A year of production traffic shows that LLM serving cannot be planned around one “typical” request. In an anonymized multi-model platform trace covering April 2025 to April 2026, 6.12 billion API calls generated roughly 35.8 trillion input tokens and 2.5 trillion output tokens. Request totals, token work, active models, user behavior and cache reuse all changed on different schedules. The practical answer is to size capacity from long-window token demand, keep reusable prefixes near the instance that serves them when load allows, and retain load-aware escape routes for bursts. A request-per-second target by itself can leave a fleet short on token-heavy days or waste GPUs on quiet, model-diverse periods.
What does a full year of LLM serving reveal that a short benchmark misses?
The central finding is that production workload is non-stationary. A week or month can describe a moment, but it cannot reliably represent a year in which demand, model popularity, token lengths, API mix and cache behavior all evolve. The study A Year in LLM Serving: Workload Evolution, Caching and Load-Balancing analyzes an anonymized one-year production trace from CompanyX, with one row per API invocation and fields for timing, model, anonymized user, serving instance, token counts, latency, time to first token (TTFT) and prefix-cache reuse.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems| Reported scale | What it represents | Qualification |
|---|---|---|
| 6.12 billion requests | API invocations in the year-long trace | Reported by the technical account for the anonymized platform |
| 314,970 users | Anonymized user identifiers associated with requests | Platform-level count, not a claim about all LLM users |
| 9,174 models | Models appearing in the request trace | The repository and paper establish the model trace; popularity changes over time |
| 875,921 serving instances | Instances recorded as serving requests | Reported platform figure, not a recommended fleet size |
| About 35.8 trillion input tokens | Prompt and context processing volume | Reported aggregate for April 2025–April 2026 |
| About 2.5 trillion output tokens | Generated-token volume | Reported aggregate for the same period |
The authors describe their contribution as a “global characterization and a longitudinal study of a one-year production trace.” That longitudinal view matters because the busiest day is not necessarily the day with the most calls, the most input tokens or the greatest number of active models.
Why is request count a weak capacity-planning metric?
Two days with the same request total can require very different GPU capacity because each request carries a different amount of input and output work. Input tokens drive prefill computation and strongly affect TTFT; output tokens drive the sequential decode phase and much of the completion tail. A counter that records only requests per second hides both effects.
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Prefill creates front-loaded pressure
Most inputs in the trace range from hundreds to tens of thousands of tokens. Long contexts must be processed before the first generated token appears, so a token-heavy burst can raise prefill utilization and TTFT even when the request rate looks ordinary.
Decode creates a serial tail
Outputs cluster more tightly, from tens to low thousands of tokens, but they still consume decoder cycles one token at a time. Output lengths drifted downward over the year, with the reported median falling from a few hundred tokens to under 100 near the end. A fleet sized on an early output distribution can therefore be over-provisioned for later decode work while remaining vulnerable to long-context prefill spikes.
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Use phase-specific demand signals
Capacity reviews should chart input tokens, output tokens, prefill latency and decode or end-to-end latency separately. Comparing fleets only by requests per second obscures whether a bottleneck is caused by context ingestion, generation, memory pressure or queueing.
How did demand and model mix change over the year?
Daily request and token totals move together only imperfectly. Weekly and diurnal rhythms remain visible, with UTC afternoons and weekdays generally busiest, but the amplitude and composition of those periods change. Active models rose from fewer than 100 early in the trace to more than 400 around the peak before settling lower, while the models attracting the most traffic turned over.
That turnover creates two separate planning problems. First, an autoscaler that watches only calls can miss a day when fewer requests contain much longer prompts. Second, a placement plan tuned to the early model population can age as new models arrive, popular models cool off and replicas become fragmented across a larger catalog.
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Track at least these series over a long window:
- Requests by hour, day and week, including burst duration rather than only peak points.
- Input and output tokens by model and time bucket.
- Number of active models and the concentration of traffic among the busiest models.
- Model-specific TTFT, queue time, generation latency and error rate.
- User and model repetition patterns that indicate where prefixes may be reusable.
How should I size an inference fleet for a year of traffic?
Start with token work and its time distribution, then map that demand to model placement, memory and latency objectives. A defensible process uses the full trace or the longest representative history available instead of a single average day.
- Build hourly and daily token baselines. Aggregate input and output tokens separately. Preserve percentiles and burst duration; an hourly average can hide a short interval that saturates prefill.
- Segment by model. Keep a time series for each materially used model, including active replicas and traffic concentration. A fleet with thousands of low-volume models has different loading and cold-start behavior from one dominated by a few models.
- Translate tokens into phase demand. Estimate prefill capacity from input-token rates and decode capacity from output-token rates. Validate both against TTFT and completion latency rather than treating a single utilization number as sufficient.
- Reserve for model-set drift. Revisit placement as the active set changes. Keep a mechanism for moving capacity toward newly popular models and retiring replicas whose demand has faded.
- Test burst persistence. Measure how long traffic stays above baseline. Autoscaling that reacts to a one-minute spike may oscillate; scaling that waits for a long average may miss a latency violation.
- Define failure-mode headroom. Model the effect of a hot replica, an unavailable instance group or a sudden concentration of traffic on one model. The target is not merely average throughput but acceptable TTFT and tail latency during redistribution.
- Recompute with new observations. The year-long trace demonstrates drift, so capacity assumptions should be recalibrated as token lengths, model popularity and user behavior change.
This approach does not yield one universal GPU count. Hardware requirements depend on model sizes, quantization, batching, memory limits, service-level objectives and the amount of replication. The evidence supports the planning method—long windows, token-based accounting and model-aware placement—not a portable fleet number.
Does prefix caching still help at production scale?
Yes, but its value is sharply concentrated in short time windows and repeated user-model paths. Per-request cache hit ratios in the trace are bimodal: a request usually reuses almost the entire prior prefix or starts nearly from scratch, rather than landing at a smooth middle value.
Reuse is strongly recency-driven. Roughly half of consecutive requests from the same user and model arrive within 0.1 seconds, about 80% within 10 seconds and 99% within 15 minutes. A retention policy that preserves hot prefixes for a short period can therefore capture much of the observed opportunity without keeping every historical context resident indefinitely.
Do not read those percentages as a universal time-to-live recommendation. They describe this platform’s reconstructed request relationships, and the study did not have explicit session identifiers or prompt contents. Sessions were inferred heuristically from request structure, so a deployment should build its own reuse histogram by model, user or tenant and context length.
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Which cache policy is a credible starting point?
Simple recency policies are credible baselines. In the reported replay, FIFO and LRU matched or outperformed more elaborate policies in the tested workloads, while the best choice changed with workload and cache size.
FIFO
FIFO evicts the oldest resident item regardless of recent reuse. It is easy to reason about and can perform well when objects have similar lifetimes or when maintaining recency metadata is costly.
LRU
LRU protects recently used prefixes and aligns naturally with the trace’s strong short-term locality. It is a sensible first implementation when repeated user-model requests arrive close together.
More complex policies
The replay found ARC performing worse at intermediate capacities in the reported examples. The offline Belady curve remains an upper bound because it uses future knowledge unavailable to a live serving system. Use such results to measure the gap between a deployable policy and an ideal, not as a production algorithm.
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Should an LLM router optimize for cache hits or load balance?
Neither objective wins in every operating regime. Sticky or cache-first routing keeps repeated traffic on one instance and preserves warm prefixes when load is light. Load-first or round-robin routing spreads work and protects replicas when demand is high, but it scatters reusable context and can leave each instance with a partial copy of the same state.
Why locality has a physical cost
The account gives a concrete scale example: a 100,000-token MiniMax-M2.5 context is roughly 27 GB of key-value (KV) state. Moving that state over a 10 GB/s link would take about 2.7 seconds one way before contention. Sending a request to a different replica can therefore impose a transfer or recomputation penalty that is invisible in a request counter.
What to compare in a routing experiment
| Metric | Why it matters |
|---|---|
| Cache hit ratio and reused-token ratio | Shows whether routing preserves useful context, not just any hit |
| Mean and maximum token load per instance | Exposes concentration and hot replicas |
| P90 TTFT and tail completion latency | Captures user-visible harm from queueing or misses |
| Session spread | Shows how many instances hold state for one user-model stream |
| KV-memory duplication | Quantifies memory consumed by replicated prefixes |
| Operational complexity | Includes state tracking, failure handling and rebalancing cost |
A practical design is load-aware stickiness: keep a session on its warm instance while queue depth, memory use and token load remain safe; when an instance becomes hot, permit migration or a controlled cache miss. This preserves locality during normal periods without allowing one popular user, model or prefix to melt a replica.
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What should operators measure before changing the router?
Instrument the serving path so every decision can be tied to token work and latency. At minimum, retain model and instance identifiers, input and output token counts, queue time, TTFT, completion latency, cache state, routing decision and eviction reason. Aggregate by hour and day, then inspect model and user cohorts rather than relying only on fleet-wide averages.
Run replay or shadow tests with the same request order against round-robin, load-first, sticky and cache-first variants. Compare cache reuse with token imbalance, memory duplication and tail latency. A policy that wins on hits but creates a small number of overloaded replicas may be worse for users than one with fewer hits and steadier queues.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the evidence limits?
This is one anonymized, multi-model serverless platform, not a census of LLM providers. Its users may include agents, routers and API integrations whose traffic patterns differ from a consumer chatbot or an internal enterprise assistant.
Cache logging covers only the final two months of the trace. Because prompts and explicit session IDs are absent, sessions and prefix relationships are reconstructed heuristically. The reported user, instance and aggregate token totals come from the technical account, while the paper and repository directly establish the request trace and its fields. The release and paper version are time-sensitive, so readers should verify the current artifact before using it for procurement or capacity commitments.
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No published figure in the cited material establishes a universal cost per request, energy per token, carbon total or dollar value for the six-billion-request workload. Those quantities depend on hardware, utilization, electricity, model mix, pricing and accounting boundaries and should not be inferred from the traffic totals.
What is the practical operating playbook?
Use the year-long findings as a set of operating rules rather than a single benchmark number:
- Plan on input and output tokens across long windows; retain request counts only as a secondary signal.
- Separate prefill and decode capacity and attach TTFT and tail-latency targets to both.
- Refresh model placement as the active set and popularity ranking change.
- Measure prefix reuse by workload before committing to large cache allocations.
- Start with FIFO or LRU, then add policy complexity only when replay shows a measurable gain.
- Use cache-aware routing under light load and load-aware escape hatches during contention.
- Track KV duplication and transfer time, especially for very long contexts.
- Re-run sizing and routing tests whenever token distributions, model mix or traffic geography shifts.
The enduring lesson is that serving scale is a moving target. A robust fleet treats time, tokens, locality and imbalance as first-class variables instead of compressing them into one requests-per-second figure.
Frequently Asked Questions
Can this trace predict my cloud inference bill?
No. Traffic totals do not reveal the provider’s GPU types, utilization, autoscaling policy, electricity price or internal accounting. A bill model needs your own hardware rates, replica hours, storage, network transfer and any managed-service charges.
How do agent workloads complicate session reconstruction?
Agents may fan out parallel calls, retry requests or switch models within one task, making adjacency an unreliable proxy for a human conversation. Treat inferred sessions as a workload signal and validate them with application-level identifiers in your own telemetry.
Is the offline Belady result something I can deploy?
No. Belady chooses an eviction using knowledge of future accesses, which a live server does not have. It is useful as an upper-bound benchmark for judging how much headroom a practical policy might recover.
Do longer contexts always increase end-to-end latency?
They generally add prefill work and memory pressure, but observed latency also depends on batching, hardware, queue depth, cache reuse and output length. A reused prefix can avoid much of the repeated prefill even when the logical context is long.
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