Can RTX 4070 SUPER run Kokoro 82M TTS?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 SUPER (12 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 132 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 4070 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Kokoro 82M TTS on an NVIDIA GeForce RTX 4070 SUPER with Grade S performance at ~716 tok/sec using the ONNX-Q8F16 quantization. Requires 0.6GB VRAM.
Prerequisites
Before starting, ensure you have at least 1GB 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 or later installed.
Expected performance
With the ONNX-Q8F16 quantization, expect ~716 tok/sec performance and 0.6GB VRAM usage, leaving 11.4GB of VRAM for context. This allows for a practical context window of several minutes of audio, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. Download the model
Download the ONNX-Q8F16 quantized model (0.1GB) from the Hugging Face repository.
ollama pull onnx-community/Kokoro-82M-v1.0-ONNX:q8f163. Run it
ollama run onnx-community/Kokoro-82M-v1.0-ONNX:q8f16
ollama chat --model onnx-community/Kokoro-82M-v1.0-ONNX:q8f164. Optimize for RTX 4070 SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 82 to fully utilize the 12GB VRAM. Additionally, enable flash attention with --flash-attn to speed up inference. Tensor parallelism is not necessary for this model size but can be explored for larger models.
Troubleshooting
Low token generation speed
Ensure CUDA is correctly installed and the Ollama runtime is set to 'cuda'. Use the command 'ollama config set runtime cuda' to set the runtime.
Out of memory errors
Reduce the number of GPU layers with the --n-gpu-layers flag. For example, use 'ollama run onnx-community/Kokoro-82M-v1.0-ONNX:q8f16 --n-gpu-layers 64'.
Inconsistent audio output
Check the model's configuration and ensure the correct sampling rate and audio settings are used. You can adjust these settings in the Ollama configuration or during the run command.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for users who prefer a graphical interface. llama.cpp offers more fine-grained control over quantization and optimization, making it ideal for advanced users. Jan is a lightweight option for quick testing but may lack some features available in Ollama. Choose based on your specific needs and preferences.
Other models that run great on RTX 4070 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.
Want personalized recommendations for your exact setup? Detect my hardware →