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dsh-live-perf-gauges

Client Updated 2026.09.05

Run the following command in DeepSeek Harness:

dsh plugin install TnzGit/dsh-live-perf-gauges

Paste the following prompt into your AI chat to install this plugin:

Install the plugin in DeepSeek Harness by running dsh plugin install TnzGit/dsh-live-perf-gauges (source: https://github.com/TnzGit/dsh-live-perf-gauges), then restart the web service and hard-refresh the browser.

About this plugin

Text streaming in DeepSeek Harness Web gives you characters on screen but little feel for the inference happening underneath. dsh-live-perf-gauges drops an automotive-dashboard-style live performance strip right below the composer so you can read the reasoning rhythm and spot bottlenecks at a glance.

It tracks five dimensions in real time: decode tok/s (refreshed every 250 ms, preferring the provider cumulative usage an adapter exposes), TTFT (elapsed time from step start to the first streamed text, reasoning, or tool-argument delta), logical prompt rate (input plus cache read and write divided by end-to-end TTFT, deliberately not labelled vLLM kernel prefill throughput because it includes DSH overhead, network, and queue time), stream-event diagnostics (per-second delta events and generated tokens per event, making MTP and speculative-decoding acceptance visible), and output-token progress. Every metric is projected purely from timestamps, counts, and final token usage already present in the session event stream; the plugin makes no network calls and never stores message text, prompts, tools, or credentials.

If you run the DSH 0.1.x Web profile and care about inference latency, speculative-decoding efficiency, or want quantified feedback while tuning prompt structure, the strip is ready to use with zero build-time steps: restart the web service, hard-refresh the browser, and the dashboard appears the moment the next model step begins.

Use Cases

  • Observe TTFT and logical prompt-rate shifts while adjusting prompt structure
  • Verify MTP and speculative-decoding acceptance rates in real time
  • Isolate streaming stalls by separating network queue time from inference cost

Best For

  • DeepSeek Harness users who track inference latency and throughput
  • Inference engineers running speculative decoding or MTP who need quantified acceptance metrics
  • Developers tuning the DSH 0.1.x Web profile front-end experience