Can RTX 3080 Ti run Kokoro 82M TTS?
Yes — runs locally
~108 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3080 Ti (12 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 108 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 3080 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run the high-quality Kokoro 82M TTS model on your NVIDIA GeForce RTX 3080 Ti with Grade S performance, using the ONNX-Q8F16 quantization. Expect ~716 tok/sec.
Prerequisites
Before starting, ensure you have at least 1GB of free disk space, a compatible OS (Windows or Linux), the latest NVIDIA drivers (version 525.60.11 or later), and CUDA 11.8 installed.
Expected performance
With the ONNX-Q8F16 quantization, expect the model to run at ~716 tok/sec, consuming approximately 0.6GB of VRAM. This leaves 11.4GB of VRAM available for context, allowing for a practical context window of several thousand tokens.
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:q8f163. Run it
ollama run onnx-community/Kokoro-82M-v1.0-ONNX:q8f16 --device cuda
ollama chat --model onnx-community/Kokoro-82M-v1.0-ONNX:q8f164. Optimize for RTX 3080 Ti
For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 82 to fully utilize the GPU while leaving enough VRAM for other operations. Flash attention is not applicable for this model, but tensor parallelism can be used if you have multiple GPUs. With 12GB VRAM, you can achieve a large context window without running out of memory.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers value to 64 or lower to reduce VRAM usage.
Slow inference speed
Ensure CUDA is properly installed and the model is running on the GPU with the --device cuda flag.
Model not loading
Verify the model file integrity and try re-downloading it using the 'ollama pull' command.
Alternative runtimes
For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 3080 Ti.
Other models that run great on RTX 3080 Ti
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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