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dsh-llm-latency

Llm Latency

用于 DeepSeek Harness 的按供应商/模型划分的大语言模型延迟遥测和跨供应商基准测试插件

插件会安装到这里;不确定时保持 web。

npx -y @deepseek-ai/dsh plugin --profile web add github:shengbinxu/dsh-llm-latency#ba2f10be1506532a19bd83ea8974217e544254fe
README兼容性版本
Recent quick viewNative dashboard

兼容性与来源证明

Llm Latency 以 dsh-llm-latency 发布,当前版本为 0.2.0。Plugin Hub 会校验它的 manifest,并保存精确安装来源,便于复现安装结果。

DSH 兼容范围
*
运行环境
web
发布来源
github
Registry 更新时间
2026/10/7

版本

0.2.0stable
2026/10/7
0.1.0stable
2026/8/29

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0.2.0
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未提供
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未提供
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web
许可证
MIT
发布源
github
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最近提交
2026/10/7
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README

dsh-llm-latency

English · 中文

A passive LLM latency dashboard for DeepSeek Harness. See how long models take to respond, compare vendors, and inspect individual requests without starting extra model calls.

What's in v0.2.0

  • Native dashboard: open LLM Latency in the DSH sidebar. Overview, time-window comparison, session comparison, and request log share the existing recorded data.
  • Composer quick view: the Latency button opens recent latency. Select the last 15 minutes, hour, or three hours; inspect the latest response delay, median response delay, median first-text delay, successful/total samples, and last sampling time.
  • Honest freshness: idle models show No recent samples. The panel never substitutes a historical average for a current measurement or probes an unused model.
  • Metric explanations: hover or keyboard-focus latency and total-output headings for their timing and eligibility rules. The native interface follows DSH's theme and locale; Chinese metric names use plain Chinese.
  • Legacy rate restored: total output speed remains available alongside strict visible-answer output and overall rates. Missing reasoning usage does not hide the legacy value or turn missing visible rates into zero.
  • Existing interfaces retained: /llm-latency/, JSON endpoints, the latency_report tool, recorded statistics, and CSV export remain available.

Screenshots

Recent quick view — completed calls only, with last-sampling times and explicit idle states.

Recent quick view

Native dashboard — total output speed and visible-answer rates are separate; hover explanations describe the measurements.

Native dashboard

Install or update

Tested with DeepSeek Harness 0.2.1-alpha.1, Node.js 22, and the Web profile. Harness extension APIs are pre-stable; compatibility with other versions is not guaranteed.

Install the tagged GitHub version:

dsh plugin --profile web add 'github:shengbinxu/dsh-llm-latency#v0.2.0'

Alternatively, download the .tgz from GitHub Releases and install that file:

dsh plugin --profile web add ./dsh-llm-latency-0.2.0.tgz

Refresh the GUI after installation. If activation requires a restart, restart the existing DSH service using its normal service manager. The distributed package includes built Host files and its browser entry; installation does not require a build step.

Use the plugin

EntryWhat it shows
DSH sidebar → LLM LatencyFull dashboard with four views and CSV export
Conversation composer → LatencyExact recent latency from retained completed-call samples
http://127.0.0.1:3080/llm-latency/Retained standalone dashboard; use your configured host/port
latency_report toolRecorded vendor/model or session comparison

The quick panel refreshes every 10 seconds while open and stops on close. Close, Escape, and outside clicks dismiss it; Open full dashboard switches to the native page without changing the selected conversation.

Recent windows use request start timestamps and exact retained samples. Medians include only successful calls with that metric; first-response and first-text sample sets can differ. Ring limits can reduce the retained count. No recent sample means this installation has no fresh measurement, not that the model is unavailable.

The full dashboard defaults to the last 30 days. Model/vendor/time filters apply to overview and cross-vendor comparison. Session comparison summarizes full sessions; use single-model sessions and similar prompts. Request logs support search, status, model, time, and one selected vendor. Historical percentiles use overlapping whole-hour buckets, so boundary hours may contain calls outside the requested window.

What the metrics mean

MetricCalculation
First-response wait / TTFTFirst stream pull → first nonempty text, reasoning, or tool-call delta
First-text waitFirst stream pull → first nonempty visible-answer text delta; reasoning and tools do not count
End-to-end durationFirst stream pull → stream completion; excludes other tools in the Agent turn
Median / p50Middle eligible value; historical dashboard values are histogram estimates, quick-view values are exact
p95Approximate value that 95% of eligible successful samples do not exceed
Total output speedLegacy cumulative total-output tokens ÷ successful calls' cumulative time from first content to completion
Visible-answer output speedEligible visible tokens ÷ seconds between first and last nonempty text delta
Visible-answer overall speedEligible visible tokens ÷ full stream duration, including the initial wait
Rate samplesSeparate eligible counts for visible-answer output / overall rates
Cache hit / writeCached-read / cached-write tokens ÷ total uncached + cached-read + cached-write input

Timing starts when the adapter is first pulled, usually when it starts its request, rather than when the user clicks Send. A first response can be reasoning or a tool call before any answer text appears. Browser rendering time is excluded.

Why a visible rate can be —: visible tokens require output usage, a separate reasoning count (explicit zero is valid), visible text, success, and no tool-call deltas. Missing data is not zero. A single text delta supports overall speed only. The tested Harness pi-ai adapter folds reasoning into total output without exposing its separate count; its strict visible rates therefore remain unavailable. No character estimates or assumed zero reasoning counts are used.

Legacy rate limitation: total output may include reasoning, tools, and reported failed-call output, while the old duration denominator includes successful calls only. Failures can inflate this legacy rate. It is retained for compatibility and displayed separately, not presented as pure answer-text speed. JSON and CSV keep tokensPerSecond for this legacy metric and outputTokensPerSecond / overallTokensPerSecond for strict visible rates.

Latency also depends on prompt size, reasoning settings, network conditions, and tool use. Recorded samples describe this installation's requests, not a provider-wide load monitor. Cross-vendor confidence intervals concern TTFT only, not throughput.

Data and configuration

Aggregates persist in $DSH_HOME/llm-latency/stats.json and individual records in $DSH_HOME/llm-latency/requests.jsonl (~/.dsh by default). Auxiliary compaction/title calls appear in the request log but not rankings or the recent panel. Upgrades preserve old latency/cache data; old samples without visible-rate fields contribute no eligible visible-rate samples.

Configure the llm-latency row through the bundle patch or a profile override:

KeyDefaultMeaning
retentionDays30Historical aggregation retention
recentLimit2000Exact-sample ring capacity per vendor/provider/model
sessionLimit500Retained sessions
spikeFloorMs10000Slow first-response threshold
modelAliases{}Canonical model → provider model IDs
minSamplesForComparison20Minimum successful samples for median confidence intervals
logLimit5000Request-log memory capacity
logRetentionDays7Request-log retention
statsPath / logPathDefault data pathsOptional persistence overrides

Development

npm ci
npm run typecheck
npm run build
npm test

Commit lib/ with its source changes: GitHub installation consumes the built Host files. The browser entry is self-contained JavaScript loaded through the Harness module table and requires no added bundler or separate React installation.

The Host wraps llm/stream and exposes read-only JSON. Native UI contributions register in main, sidebar.panellist, conversation.input.left, and shell.overlay; both browser forms read the same Host data. Resource cleanup belongs to Cordis effects so unloading releases the route and listener. See DESIGN.md for the original data/comparison design.

License

MIT

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