Kokoro 82M
KokoroFeaturedApache 2.0audio

Kokoro 82M

Updated Jun 7, 2026
Parameters
0.08B
Context
8,192
License
Apache 2.0
Updated
Jun 7, 2026

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Runs on your 24 GB - best at FP16
2 of 2 quantizations fit Kokoro 82M with real runtime overhead.
Q4_K_M
0.5 GB
FP16
0.5 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

Ollamaeasiest
ollama run kokoro
vLLMOpenAI-compatible API
vllm serve hexgrad/Kokoro-82M

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

Kokoro 82M

Kokoro 82M is the most popular open text-to-speech model on HuggingFace with 12.7 million monthly downloads. Despite its tiny 82-million parameter size, it delivers quality comparable to much larger models while being significantly faster.

Why Kokoro dominates TTS

  1. 82M params - runs on CPU, no GPU required
  2. Apache 2.0 - deploy anywhere, even commercially
  3. 12.7M monthly downloads - the most popular TTS model
  4. Multiple languages - English, Japanese, Chinese, French, Korean, and more
  5. Voice cloning - fine-tune on any voice
  6. ~$0.06/hour - extremely cheap to run

Quick start

from kokoro import KPipeline
pipeline = KPipeline(lang_code='a')
generator = pipeline('Hello world!', voice='af_heart')
for i, (gs, ps, audio) in enumerate(generator):
    # audio is a numpy array ready to play/save
    pass

When to use

  • Voice assistants - lightweight TTS for any assistant
  • Audiobooks - natural-sounding narration
  • Accessibility - screen readers and text-to-speech
  • Content creation - voiceovers and dubbing

What the community says

"Kokoro is the best open TTS model. 82M params, Apache 2.0, and it sounds better than most 1B+ models."

  • r/LocalLLaMA, 456 upvotes

Frequently asked

Quick answers to common questions

How much VRAM does Kokoro 82M need?

Kokoro 82M with 0.08B parameters needs approximately 0.5 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is Kokoro 82M better than other Kokoro models?

Kokoro 82M has 0.08B parameters with 8,192 context - a strong choice for text-to-speech, audio, voice-cloning.

What license is Kokoro 82M under?

Kokoro 82M is released under the Apache 2.0 license, making it suitable for most commercial and personal projects.

What hardware runs Kokoro 82M well?

With 0.08B parameters, Kokoro 82M requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for Kokoro 82M?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~? GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

What models compete with Kokoro 82M?

Kokoro 82M competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Compare & pair with

Similar models and recommended hardware

Related models

qwen3-tts-1-7b

Nearby options

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