Can RTX 4080 SUPER run Kokoro 82M TTS?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 SUPER (16 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 156 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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Kokoro 82M TTS on an NVIDIA GeForce RTX 4080 SUPER with Ollama using the ONNX-Q8F16 quantization. Expect Grade S performance at ~955 tok/sec.
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 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the ONNX-Q8F16 quantization, expect a token generation rate of ~955 tok/sec, consuming approximately 0.6GB of VRAM. This leaves 15.4GB of VRAM available for context, allowing for a practical context window of several thousand tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. 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 generate onnx-community/Kokoro-82M-v1.0-ONNX:q8f16 --text 'Hello, how are you today?'4. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 82 to fully utilize the GPU's 16GB VRAM. Enable flash attention with --flash-attn to further enhance speed and efficiency. Tensor parallelism is not necessary for this model but can be explored for larger models.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers <num_layers> or enable flash attention with --flash-attn.
Slow token generation rate
Ensure CUDA and NVIDIA drivers are up to date. Use the --flash-attn flag to speed up inference.
Model not loading
Verify the model path and ensure the model files are correctly downloaded and accessible.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. LM Studio is suitable for users who prefer a graphical interface and advanced model management features. llama.cpp is ideal for those who need more control over the inference process and want to run models on CPUs or with different quantizations. Jan is another option for users looking for a lightweight, easy-to-use runtime, especially for smaller models.
Other models that run great on RTX 4080 SUPER
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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