Can RTX 3060 12GB run Kokoro 82M TTS?
Yes — runs locally
~84 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3060 12GB (12 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 84 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. High quality 82M parameter TTS model. Excellent speech synthesis with multiple voice options. 86MB download.
Setup tutorial: Kokoro 82M TTS on RTX 3060 12GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run the high-quality Kokoro 82M TTS model on your NVIDIA GeForce RTX 3060 12GB with Grade S performance at ~716 tok/sec using the ONNX-Q8F16 quantization.
Prerequisites
Before starting, ensure you have at least 1GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 510.47.03 or later) with CUDA 11.2 or higher installed.
Expected performance
With the ONNX-Q8F16 quantization, you can expect ~716 tok/sec performance and 0.6GB VRAM usage, leaving 11.4GB of VRAM for context. This allows for a practical context window of several minutes of speech synthesis without running out of VRAM.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the 0.1GB ONNX-Q8F16 quantized model from Hugging Face.
ollama pull onnx-community/Kokoro-82M-v1.0-ONNX:onnx/model_q8f16.onnx3. Run it
ollama run onnx-community/Kokoro-82M-v1.0-ONNX:onnx/model_q8f16.onnx --interactive
ollama generate --model onnx-community/Kokoro-82M-v1.0-ONNX:onnx/model_q8f16.onnx --text "Your text here"4. Optimize for RTX 3060 12GB
For optimal performance on the NVIDIA GeForce RTX 3060 12GB, set --n-gpu-layers to 82 to fully utilize the 12GB VRAM. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. Tensor parallelism is not necessary for this model size but can be explored for larger models.
Troubleshooting
Insufficient VRAM during inference
Reduce --n-gpu-layers to 64 or lower.
Slow inference speed
Ensure CUDA is properly installed and enabled in Ollama.
Model not found
Verify the model path and ensure it matches the downloaded model name.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced configurations or different hardware setups. LM Studio is ideal for GUI-based workflows, llama.cpp offers more fine-grained control over optimizations, and Jan is suitable for distributed training scenarios. However, Ollama provides a simpler and more streamlined experience for running the Kokoro 82M TTS model on your NVIDIA GeForce RTX 3060 12GB.
Other models that run great on RTX 3060 12GB
FAQ (20)
What GPU do I need to run Kokoro 82M TTS?
Kokoro 82M TTS requires at least 0.6 GB of VRAM. Any modern GPU with this amount of VRAM should suffice.
Is Kokoro 82M TTS good for coding?
Kokoro 82M TTS is primarily designed for text-to-speech applications and not specifically for coding. However, it can be useful for generating spoken code snippets or documentation.
Kokoro 82M TTS vs Llama 3.1 8B?
Kokoro 82M TTS is a smaller, more focused model for text-to-speech with 82 million parameters, while Llama 3.1 8B is a larger, more versatile language model with 8 billion parameters, suitable for a wider range of tasks.
Can I run Kokoro 82M TTS on a Mac?
Yes, you can run Kokoro 82M TTS on a Mac as long as your system meets the minimum VRAM requirement of 0.6 GB.
How much VRAM does Kokoro 82M TTS need?
Kokoro 82M TTS requires 0.6 GB of VRAM to run smoothly.
Is Kokoro 82M TTS censored?
Kokoro 82M TTS is not inherently censored, but its output can be controlled through the input and configuration settings.
Is Kokoro 82M TTS commercial-use allowed?
Yes, Kokoro 82M TTS is licensed under the Apache-2.0 license, which allows for commercial use.
Kokoro 82M TTS context length?
The context length for Kokoro 82M TTS is currently unknown, but it is designed to handle typical text-to-speech inputs effectively.
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