Text-to-speech Measured by us
Piper (voices) on CPU only (Intel N100 mini-PC / x86 desktop)
We ran it. Results below, with the exact hardware and settings.
No GPU. Covers low-power N100 boxes up to desktop CPUs. Our own CPU numbers come from an 8 vCPU cloud Xeon, which is faster than an N100.
EvidenceMeasured by us
Commercial useConditional
LicenseVoices repo: MIT; engine (piper1-gpl): GPL-3.0
Stated memoryNot stated
Device memory16 GB RAM
Results
| Runtime / settings | RTF | Time to first audio | Peak RAM | Load |
|---|---|---|---|---|
| piper-tts (onnxruntime) voice=en_US-lessac-medium | 0.0174 (57× real time) | 0.051 s | 378.9 MB | 1.44 s |
Measured on Cloud x86 VM, 8 vCPU Intel Xeon, 16 GB RAM, no GPU (Intel(R) Xeon(R) Processor, 8 threads, Linux 6.12.94+, Python 3.13.5) on 2026-10-21. This is a cloud Xeon, not an N100: expect an N100 (4 cores, 6 W) to be several times slower. We'll post real N100 numbers when we have one.
Method
- Harness:
bench/run.py(open source), 5 timed runs after 1 warm-up, median reported. - Input: a fixed 2-sentence English passage (~10–11 s of audio). Time to first audio = first chunk from the streaming/sentence API.
- RTF = wall-clock processing time ÷ audio duration (lower is faster). Model load time is reported separately, not included.
- Peak RAM = whole-process RSS sampled every 20 ms, including model weights.
- Machine: Cloud x86 VM, 8 vCPU Intel Xeon, 16 GB RAM, no GPU. No other benchmark ran at the same time; a browser was open on the same VM.
Ways to run Piper (voices)
| Runtime | Precision | Documented devices | |
|---|---|---|---|
| piper-tts (onnxruntime | fp32 ONNX voices | CPU (optional --cuda) | source |
Run it
pip install piper-tts
python -m piper.download_voices en_US-lessac-medium
python -m piper -m en_US-lessac-medium -f out.wav -- 'This is a test.'We ran this exact command on our test box on 2026-10-21.
Caveats
- Our CPU numbers come from an 8 vCPU cloud Xeon, which is several times faster than an Intel N100