Can RTX 4090 run Kokoro 82M TTS?
Yes — runs locally
~192 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (24 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 192 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 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run the high-quality Kokoro 82M TTS model on an NVIDIA GeForce RTX 4090 with the ONNX-Q8F16 quantization for Grade S performance at ~1432 tok/sec.
Prerequisites
Before starting, ensure you have at least 100MB of free disk space, a 64-bit version of 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 approximately 1432 tokens per second, using around 0.6GB of VRAM. This leaves 23.4GB of VRAM available for context, allowing for a practical context window of several thousand tokens depending on the input complexity.
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 --interactive
ollama serve onnx-community/Kokoro-82M-v1.0-ONNX:q8f164. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. The flash-attn optimization can also be enabled for faster inference. Given the 24GB VRAM, you can set --tensor-parallelism to 2 or 4 to further speed up processing while maintaining a large context window.
Troubleshooting
Low token generation speed
Ensure that the CUDA toolkit is correctly installed and that the --flash-attn flag is set to True.
Out of memory errors
Reduce the --tensor-parallelism value or increase the --n-gpu-layers value to better balance the workload across the GPU.
Model fails to load
Verify that the model file has been downloaded correctly and that there are no network issues. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different deployment scenarios. For instance, LM Studio offers a more user-friendly interface, while llama.cpp provides a lightweight, portable solution suitable for edge devices. Jan is ideal for integrating the model into web applications.
Other models that run great on RTX 4090
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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