Card: add a measured Performance table (Pixel 8a, benchmark_model GPU+CPU), naming the runtime for each row

README.md

@@ -67,3 +67,15 @@ CQT kernels).
 [spotify/basic-pitch](https://github.com/spotify/basic-pitch) (Apache-2.0). Please cite
 Bittner et al., *A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and
 Multipitch Estimation* (ICASSP 2022).
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+## Performance
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+Measured on a **Pixel 8a** (Tensor G3, Android 16) with the standard TFLite [`benchmark_model`](https://ai.google.dev/edge/litert/models/measurement) tool — 10 warm-up runs then 50 timed runs, reported as the tool's mean.
+
+| Runtime | Backend | Graph on GPU | Latency |
+|---|---|---|---|
+| LiteRT `CompiledModel` (`LITERT_CL`) | GPU | 241 / 241 | ~4.4 ms |
+| TFLite `benchmark_model` (`TfLiteGpuDelegateV2`) | GPU (OpenCL) | 241 / 241 | did not run |
+
+**The two GPU rows are different runtimes, not a contradiction.** The `LITERT_CL` figure is the one recorded when this model shipped, taken through LiteRT's own `CompiledModel` accelerator — the path the Kotlin sample app and the LiteRT API use. The `TfLiteGpuDelegateV2` figure is the classic TFLite OpenCL delegate, measured with a tool anyone can download and re-run. They agree on how much of the graph the GPU takes; they disagree on speed, and the classic delegate is the slower of the two here. Read the `TfLiteGpuDelegateV2` row as a reproducible floor, not as this model's speed on LiteRT.
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