Can RTX 4060 Ti 16GB run Kokoro 82M TTS?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4060 Ti 16GB (16 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 114 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 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run the high-quality Kokoro 82M TTS model on your NVIDIA GeForce RTX 4060 Ti 16GB with Grade S performance, achieving ~955 tok/sec using the ONNX-Q8F16 quantization.
Prerequisites
Before starting, ensure you have at least 100MB 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 ~955 tok/sec and 0.6GB VRAM usage, leaving 15.4GB of VRAM for context. This allows for a practical context window of several thousand tokens, depending on the input complexity.
1. Install runtimeOllama
pip install ollama
ollama setup2. Download the model
Download the 86MB 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 --device cuda
ollama interactive onnx-community/Kokoro-82M-v1.0-ONNX:onnx/model_q8f16.onnx --device cuda4. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 82 to fully utilize the 16GB VRAM. Enable flash attention (--flash-attn) to speed up inference. Given the 16GB VRAM, you can achieve a large context window while maintaining low latency. Tensor parallelism is not necessary for this model size.
Troubleshooting
Low token generation speed
Ensure CUDA is properly installed and the model is running on the GPU with --device cuda.
Out of memory errors
Reduce --n-gpu-layers or decrease the batch size.
Inference is not using the GPU
Check the CUDA installation and ensure the --device cuda flag is used in the run command.
Alternative runtimes
For users preferring a different runtime, consider LM Studio for a more graphical interface, llama.cpp for lightweight deployment, or Jan for advanced customization. Each has its own strengths, but Ollama provides a balanced approach with ease of use and performance on the NVIDIA GeForce RTX 4060 Ti 16GB.
Other models that run great on RTX 4060 Ti 16GB
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.
Want personalized recommendations for your exact setup? Detect my hardware →