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kokoro-coreml

mattmireles

**15.1x faster than MLX on iPhone 15 Pro Max – while using 58% less RAM (1,338 MB vs 3,192 MB).** MLX was measured on the same iPhone, with the same text, same 27 sec audio duration. Kokoro-82M running on all three of Apple Silicon's key compute components (Neural Engine, GPU and CPU) through Core ML. No Python, no server, no API key. The model is cut into pieces at its seams, and each stage runs on the chip that suits it. That includes the vocoder, which the Neural Engine rejects as exported; a reshape that leaves its math unchanged gets it in. Warm median end-to-end time, token IDs in, 24 kHz audio out. Measured October 23, 2026 on the shipped pipeline, against MLX on the same device in the same session:

Speech synthesis

Input Text→ Output Audio

Price not verifiedCheck multimodal and channel pricing separately

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Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

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kokoro-coreml

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