Can RTX 5060 Ti run Kokoro 82M TTS?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5060 Ti (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 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Kokoro 82M TTS on an NVIDIA GeForce RTX 5060 Ti 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), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.
Expected performance
With the ONNX-Q8F16 quantization, expect the model to run at approximately 955 tokens per second, consuming around 0.6GB of VRAM. This leaves 15.4GB of VRAM available for context, allowing for a practical context window of several thousand tokens.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. Download the model
Download the 86MB ONNX-Q8F16 quantized model from Hugging Face.
ollama pull onnx-community/Kokoro-82M-v1.0-ONNX3. Run it
ollama run onnx-community/Kokoro-82M-v1.0-ONNX --model onnx/model_q8f16.onnx
ollama serve4. Optimize for RTX 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti 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. Enable flash attention with --flash-attn to speed up inference. Given the 16GB VRAM, you can achieve a large context window while maintaining high performance.
Troubleshooting
Inference is slow or hangs
Ensure CUDA is correctly installed and the Ollama runtime is set to CUDA. Try reducing the number of GPU layers with --n-gpu-layers.
Out of memory errors
Reduce the number of GPU layers with --n-gpu-layers or decrease the batch size.
Model does not load
Verify that the model file is correctly downloaded and not corrupted. Re-run the 'ollama pull' command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you prefer different features or better integration with other tools. LM Studio is ideal for a graphical interface, llama.cpp offers more control over quantization, and Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with good performance and ease of use on the NVIDIA GeForce RTX 5060 Ti.
Other models that run great on RTX 5060 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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