Can RTX 5070 Ti run Kokoro 82M TTS?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5070 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 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run the high-quality Kokoro 82M TTS model on your NVIDIA GeForce RTX 5070 Ti with Grade S performance, using the ONNX-Q8F16 quantization. Expect ~955 tok/sec and minimal VRAM usage.
Prerequisites
Before starting, ensure you have at least 100MB 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 ~955 tok/sec performance and 0.6GB VRAM usage. The remaining 15.4GB of VRAM can support a large context window, enabling high-quality speech synthesis with minimal latency.
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-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 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 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 (--flash-attn) for faster inference. With 0.6GB VRAM used by the model, you have 15.4GB of VRAM available for context, allowing for a large practical context window.
Troubleshooting
Low performance or high CPU usage
Ensure that the CUDA backend is correctly configured by running 'ollama config set runtime cuda'. Verify that the GPU drivers and CUDA are up to date.
Out of memory errors
Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 64. This will free up more VRAM for context.
Inconsistent audio output
Check that the model is fully downloaded and not corrupted. Re-run the 'ollama pull' command to ensure the model is up to date.
Alternative runtimes
Consider using LM Studio for a more user-friendly GUI, llama.cpp for more advanced customization, or Jan for lightweight deployment. Ollama is recommended for its ease of use and CUDA integration, but the alternatives offer different trade-offs depending on your specific needs.
Other models that run great on RTX 5070 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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