# Export formats and quantization

## Release formats

| Format | Release status | Notes |
| --- | --- | --- |
| PyTorch FP32 | Supported | Canonical weights and the fully tested runtime. |
| PyTorch FP16/BF16 | Not released | Smaller in memory, but not validated for waveform quality or CPU compatibility. |
| ONNX | Not released | Dynamic text length, stochastic latent sampling, and custom model operations require a dedicated export/runtime path. |
| INT8 / INT4 | Not released | Naive weight quantization can audibly damage the integrated waveform decoder. |
| GGUF | Not applicable today | GGUF targets transformer/LLM runtimes and is not a drop-in container for this VITS-family convolutional waveform model. |
| Core ML / TFLite | Not released | Requires separate conversion and target-device validation. |

## Why only FP32 ships initially

The model is already small: 37.53 MB for Micro and 15.97 MB for Nano. A format
is useful only if it is smaller or faster **and** preserves pronunciation,
stability, and waveform quality. An export that loads but produces degraded
speech is not release-ready.

The canonical `model.pth` remains the source of truth. Future exports must:

1. reproduce deterministic fixed-seed outputs within a declared tolerance;
2. pass matched semantic-WER and signal-diagnostic gates;
3. complete a held-out listening comparison against FP32;
4. report target hardware, runtime version, memory, latency, and file size;
5. remain compatible with punctuation-aware long-text synthesis.

## Practical deployment today

Use the FP32 package and keep one `InflectTTS` instance loaded. Nano is the
recommended footprint-first deployment; Micro is the quality-first deployment.
Install the CPU or CUDA PyTorch build appropriate for the target platform before
installing the remaining requirements.