llmtrafficlens 0.11.0

Profile LLM gateway logs for prefix-cache reuse potential, and export anonymized replayable traces

llmtrafficlens

Analyze an LLM gateway's request log for prefix-cache reuse, then export the log as an anonymized trace that standard benchmark tools can replay.

On the public Mooncake conversation trace, which the Try it section below reproduces verbatim:

$ llmtrafficlens profile conversation_trace.jsonl --format mooncake -o report
12,031 requests · unknown
input p50 6,909 tok · output p50 350 tok · stream 0% · 3.4 req/s

hit rate by cache size  (steady state: last 50% of requests)
      16K tokens    3.6%
      64K tokens    4.5%
     256K tokens    4.5%
       1M tokens    5.6%
       4M tokens   18.8%
      16M tokens   35.6%
       unbounded   39.8%  <- ceiling

session identifiers  0.0% of requests  (affinity routing cannot reach this reuse)
top prefix by volume 512 tok x 12,031 reqs = 5.3% of reusable volume
top prefix by count  512 tok x 12,031 reqs = 5.3% of reusable volume

wrote report.json, report.html

report.html carries the full leaderboards and distributions; report.json is the same content for scripts.

Two commands:

• profile — how much a prefix cache could save on this traffic, and how much cache memory that takes.
• export — the same log as a Mooncake- or bailian-format trace, with all text removed.

Background

An engine serves a request in two phases: prefill reads the whole prompt at once, then decode emits output tokens one by one. Prefill cost grows with prompt length, and it is pure recomputation whenever two requests begin with the same text — the same system prompt, the same few-shot examples, the same attached document.

A prefix cache avoids that: the intermediate state a prompt produces (its KV cache) stays in memory, and the next request starting with the same text reuses it instead of recomputing. Whether that pays off depends on three things, which are exactly what this tool reports:

• The traffic. If requests share no leading text, nothing can be reused. This is a property of your workload, not of your setup.
• Cache memory. KV state is bulky, so a cache holds a bounded number of tokens and evicts the rest. More memory, more hits — with diminishing returns you can measure instead of guess.
• Routing. Across several workers, a request only hits if it reaches the worker that already holds its prefix. Sending it there is what prefix-aware routing does; the alternative, session affinity, only works when requests carry a session identifier.

Reuse is tracked in fixed-size blocks (16 tokens by default) because engines cache at block granularity, not per character.

Install

pip install llmtrafficlens

No runtime dependencies. Python ≥ 3.10.

profile

llmtrafficlens profile gateway.csv -o report

Input. A CSV with a request_json column holding the OpenAI-format request body. These columns are used when present: response_json, model_name, status_code, and a timestamp column (timestamp, ts, created_at, ...; epoch or ISO-8601, or set --ts-column). The model name selects which chat-template layout to assume; see What counts as the prompt?. Mooncake and qwen-bailian traces are accepted as input too (--format mooncake|bailian).

Output. A summary on stdout (shown above), plus report.html and report.json containing:

• hit rate at each cache size, ending with the unbounded case — the ceiling, and split by what could supply each hit: text shared between unrelated conversations, or an earlier turn of the same one. Measured over the last half of the log, the first half filling the cache; --warmup 0 measures the whole log from cold instead;
• top prefixes by reuse count, and separately by reusable token volume;
• share of requests carrying a session identifier;
• input/output token distributions, streaming ratio, model mix, QPS.

How to read it.

Ceiling — the share of input tokens that could be served from cache if memory were unlimited and every request reached the right worker. It bounds everything else: at a few percent, no amount of engineering makes prefix caching worthwhile on this traffic.

Capacity curve — the hit rate at each cache size, so you can see what the ceiling costs. Where it flattens is the point past which buying memory stops helping.

Session-identifier share — whether the cheap option is enough. When most requests carry an identifier, pinning each session to a worker captures the reuse. When none do (a common case for API traffic), the reuse sits in prefixes shared between unrelated requests, and only prefix-aware routing reaches it.

The two leaderboards — which prefixes to keep resident. They rank differently, and the token-volume one is what determines savings: a short prefix reused very often contributes almost no reusable volume, while a long prefix reused a few dozen times can account for most of it.

Handing off to kvcache-simulator

Our curve is one LRU replay per capacity. kvcache.ai's simulator does more on the same input — FIFO, LRU and Belady-optimal side by side, per-model byte accounting, a C++ core — so profile can run it on the same trace and fold the result in:

pip install kvcache-simulator
llmtrafficlens profile gateway.csv --kvcache-sim glm-5.2 -o report

kvcache-simulator · glm-5.2 · 93 KiB/token
  ceiling 62.1%
        16 GiB  lru 10.4% (1.1x) optimal 31.6% (1.5x)
       128 GiB  lru 44.5% (1.8x) optimal 62.1% (2.6x)
       512 GiB  lru 60.8% (2.6x) optimal 62.1% (2.6x)

The gap between the two policies is the part of the shortfall that better eviction could recover rather than more memory: at 16 GiB above, three times the hit rate is reachable at the same capacity. The step is skipped with a note if the simulator is not installed.

On a production log

The public trace above is chat traffic. A production agent workload looks different — 2,519 requests through a gateway, tool-calling loops rather than conversation, each turn resending the whole history:

hit rate by cache size  (steady state: last 50% of requests)
      16K tokens    1.5%   (1.3% across + 0.2% same-conv)
      64K tokens    5.4%   (5.1% across + 0.3% same-conv)
     256K tokens   11.9%   (10.8% across + 1.1% same-conv)
       1M tokens   35.3%   (19.9% across + 15.4% same-conv)
       4M tokens   60.2%   (29.5% across + 30.7% same-conv)
       unbounded   62.1%   (30.9% across + 31.2% same-conv)  <- ceiling

The ceiling splits almost evenly, but the two halves cost very different amounts of memory. Text shared between unrelated conversations — system prompts, tool definitions — is touched by hundreds of requests, so LRU keeps it resident even in a small cache: a third of its potential is already there at 256K tokens. History resent within one conversation is touched only by that conversation's later turns, and there is a lot of it per conversation, so it survives only once the cache can hold many conversations at once. Below 1M tokens it contributes essentially nothing.

Read that as a sizing rule: a small cache buys you the templates, and the conversational reuse — half the prize here — needs an order of magnitude more.

Two findings from the same log worth checking for in your own:

• No request carried a session identifier. Half the reuse is conversational, so pinning sessions to workers would capture it — but nothing in the request says which conversation it belongs to, leaving prefix matching as the only way to find out. Having clients send prompt_cache_key would be cheaper than any routing change.
• Tool sets changed mid-conversation in 11% of conversations, one of them 191 times. Where a template renders tools near the front, changing them breaks the prefix immediately and discards the entire history behind it. Keeping the tool array stable for the length of a conversation costs nothing and protects the reuse.

export

llmtrafficlens export gateway.csv --to mooncake -o trace.jsonl   # or --to bailian

A benchmark written by hand, with equally popular prompt groups, reports a higher hit rate than production reaches, because every group stays warm. Replaying the real log avoids that, and the export can be shared, because each request is reduced to one line of structure:

{"timestamp": 1753340000123, "input_length": 1994, "output_length": 117,
 "hash_ids": [4251731047194047120, 8125214104179782736, ...]}

hash_ids is a salted chained block hash: two requests share their first N hashes exactly when they share their first N blocks. Replay tools generate one synthetic block per hash, so the prefix-sharing structure is preserved while the content is not real.

# replay at the recorded pace
aiperf profile --custom-dataset-type mooncake_trace --input-file trace.jsonl --fixed-schedule ...
# replay at 2x the pace, for rate sweeps
aiperf profile --custom-dataset-type mooncake_trace --input-file trace.jsonl --synthesis-speedup-ratio 2.0 ...
# or SGLang
python -m sglang.bench_serving --dataset-name mooncake --dataset-path trace.jsonl ...

--to bailian adds chat_id, parent_chat_id and turn for AIPerf's bailian_trace mode; use it when the log carries session identifiers. Tell the consumer which block size the export used — AIPerf defaults to 512 for mooncake and 16 for bailian (--prompt-input-tokens-block-size).

Try it on a public trace

No data of your own needed:

curl -LO https://raw.githubusercontent.com/kvcache-ai/Mooncake/main/FAST25-release/traces/conversation_trace.jsonl
llmtrafficlens profile conversation_trace.jsonl --format mooncake -o mooncake-report

That prints the summary at the top of this README, and reproduces the 39.8% ceiling the official kvcache-simulator reports for this trace. Reading the curve: this workload gains almost nothing below 1M tokens, and most of its ceiling needs a cache past 16M — which for GLM-5.2 at bf16 (93 KiB/token) is 1.5 TiB, more than device memory alone holds, and the case tiered KV stores (host memory, SSD) are built for.

Cache size is reported in tokens because bytes are model-specific: 93 KiB/token for GLM-5.2, 68.6 for Kimi K2.5, 128 for a 32-layer fp16 GQA model. Some architectures also carry a DSA indexer cost on top of attention. Look yours up with kvcache-simulator list-models.

The repository also contains a synthetic sample log (examples/, not shipped in the pip package): 360 requests, three shared system prompts with skewed popularity, sparse session identifiers, real timestamps. It reports a 63.0% ceiling, and it shows the two leaderboards disagreeing — 16 tokens × 180 requests is 2.3% of the reusable volume, while 2400 tokens × 30 requests is 55.6% of it.

Privacy

Processing is local. Reports contain aggregates only. An export contains lengths, timestamps, anonymized session identifiers and salted block hashes; prompts cannot be reconstructed from it. The hash key is written to .ltl-salt on first run — keep it unchanged so runs stay comparable, and treat it as a secret.

FAQ

What counts as the prompt?

Everything a chat template renders before generation: the tool definitions, then each message with its role, name, tool_call_id, content and tool_calls. On one production log those extras were 23% of the request body, so leaving them out inflates the measured reuse substantially.

Tool definitions go ahead of the conversation, which is where GLM-5.2 and the DeepSeek family put them. Qwen- and Llama-style templates append them to the user's system message instead, which lets requests with differing tool sets keep sharing the system prefix; on the same log that placement read 6 points higher. We take the conservative one. reasoning_content is excluded, since clients echo it back but engines generally strip it before prefill.

How is the hit rate computed?

Requests are replayed in timestamp order against an LRU block cache. Unbounded capacity gives the "ideal hit rate" of KVCache in the Wild (ATC'25), which is also the Mooncake simulator's infinite-capacity ceiling; bounded capacities give that simulator's capacity curve. LRU is the default eviction policy in both vLLM (FreeKVCacheBlockQueue) and SGLang (--radix-eviction-policy lru), though SGLang evicts radix-tree leaves rather than flat blocks.

Prefixes are identified by chained per-block hashing (hash(parent, block), 16-token blocks; 16 is the smallest block vLLM's FlashAttention backend supports and the granularity it aligns to), following the qwen-bailian trace convention.

What does the curve assume?

One global cache, i.e. a single worker or perfect routing. With N workers behind hash routing each sees a partition, so read it as fleet-level guidance rather than per-worker sizing. An engine's KV pool also holds in-flight requests; only the remainder is available for reuse.

The ceiling covers sharing between requests only. Intra-session reuse is not measurable without session identifiers in the log.

How do I convert token counts to gigabytes?

Per-token cost follows the architecture. GLM-5.2 caches two things:

component	derivation	per token
MLA latent	(512 + 64) x 2 bytes x 78 layers	87.75 KiB
DSA indexer	128 x 2 bytes x 21 full-indexer layers	5.25 KiB
		93 KiB

Only 21 of the 78 layers run a full indexer, the rest reuse the previous one's selection. Both terms match the kvcache-simulator catalog, which reports 95,232 bytes per token for this model.

Do not scale that figure to another model. Only some architectures carry a DSA indexer, and the attention term differs as well: Kimi K2.5 is 68.6 KiB per token, a 32-layer fp16 GQA model 128 KiB. Look yours up with kvcache-simulator list-models.

Has this been checked against other tools?

Yes, twice, both on the public Mooncake conversation trace with 512-token blocks, and both pinned by tests in tests/test_core.py.

NVIDIA AIPerf v0.11.0. aiperf analyze-trace reports cache_hit_rate: 0.38425808746366197, which is the unweighted mean of per-request hit-block ratios. Computing that same definition from our own block matching yields 0.3842580874636671 — a difference of 5e-15, so the two implementations agree on every request's prefix match.

The engine's own rendering. The strongest check we have, since it skips our approximations entirely: render each request with the model's real chat_template.jinja, tokenize with its real tokenizer, chunk those token ids and hash them. On the production log above that pipeline reports a 61.7% ceiling where ours reports 62.1% — 0.4 points apart, over 51.7M real tokens versus our 48.8M estimated. Chunking characters rather than tokens costs almost nothing; getting the tool placement wrong costs 6 points, and dropping tools costs 27.

kvcache.ai kvcache-simulator. It reports a 39.8% ceiling on this trace, which our default now reproduces exactly, and an LRU curve of 4.5% at 30 blocks, 5.5% at 1,937, 17.9% at 7,729 and 34.6% at 30,918 — the same shape as ours at comparable block counts. Feeding it our own export output works too: it reads the file and lands within 0.1 points of our figure on the same data.

Why measure over only the last half of the log?

Because the first half is the cache filling up. Counting it reports what one cold replay of that particular log would have achieved, not what a long-running service settles at. The simulator makes the same choice and we follow it, so the two are directly comparable: both report 39.8% on the Mooncake trace. --warmup 0 gives the cold-start reading, 37.4% on the same trace.

The AIPerf comparison is a separate axis. It averages per-request hit ratios, weighting a 900-token request the same as a 126,000-token one, while we divide total hit tokens by total input tokens. Token weighting is the right choice for sizing a cache, since it is tokens that occupy it.

How exact are the token counts?

Characters divided by four, unless the log carries usage, in which case exact counts are used and the report says so. CJK-heavy text tokenizes closer to 1-2 characters per token, so absolute counts run low while ratios remain usable.

What has not been verified?

• Replay round-trip. We have not fed an export through AIPerf or SGLang and confirmed it replays. The formats match their documented schemas, which is not the same thing.
• Exact tokenization. Approximate mode hashes character chunks, so reported structure is a lower bound on what a real tokenizer would match: formatting differences that tokenize identically read as distinct prefixes here.
• Landscape claims. The statement below that no tool goes from a raw gateway log to a hashed trace comes from a survey of vendor documentation in July 2026. That is an absence of evidence, not a vendor denial.

Why not just use AIPerf or the Mooncake simulator?

If you already have a hashed trace, use them. kvcache-simulator does more than we do on that input: per-model byte accounting across dozens of models, FIFO/LRU/Optimal policy comparison, a C++ replay core. AIPerf is a full benchmarking harness that actually drives an endpoint.

They start from a line like {"timestamp": 0, "input_length": 6909, "hash_ids": [1234, 5678, ...]}. A gateway log is not that — it is prompts. Turning one into the other means extracting the prefill text, chunking it, salting and chaining the hashes, and doing it on a machine you control because that step is the one that touches real prompts. That is the step this tool covers, and its export output is what those tools consume.

Reading the raw log also preserves two things a hashed trace has already discarded: whether requests carry session identifiers, which decides whether cheap affinity routing would work at all, and which specific prefixes carry the reusable volume. Neither analyzer reports those, because by the time they see the data it is gone.

License

Apache-2.0