> ## Documentation Index
> Fetch the complete documentation index at: https://amd-gaia.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Performance Analysis Plotter

# Performance Analysis Plotter

Visualize llama.cpp telemetry by turning one or more server logs into plots for prompt tokens, input tokens, output tokens, time-to-first-token (TTFT), tokens-per-second (TPS), and a prefill vs decode time split.

***

## Requirements

* Python 3.8+
* `matplotlib` (`pip install matplotlib`)

***

## Run the Plotter (GAIA CLI)

```bash theme={null}
gaia perf-vis <log_file> [<log_file> ...]
```

* Pass multiple log files to compare runs; each plot adds one line per log with a legend.

***

## Collecting llama.cpp Logs

The script expects llama.cpp server logs. With [Lemonade](https://lemonade-server.ai/), you can capture telemetry like this:

```bash theme={null}
lemonade-server serve --ctx-size 32768 2>&1 | tee agent.log
gaia perf-vis agent.log
```

***

## Outputs

Images are written to the directory where you run the script:

* `prompt_token_counts.png` — prompt token totals per call
* `input_token_counts.png` — input token counts
* `output_token_counts.png` — output token counts
* `ttft_seconds.png` — time to first token
* `tps.png` — tokens per second
* `prefill_decode_split.png` — one pie per log showing prefill (TTFT) vs decode (output tokens / TPS) time

When multiple logs are provided, every plot includes one line/pie per log plus legends mapping each series to its log filename.

***

<small style="color: #666;">
  **License**

  Copyright(C) 2024-2026 Advanced Micro Devices, Inc. All rights reserved.

  SPDX-License-Identifier: MIT
</small>