Can RTX 4070 Ti SUPER run Kokoro 82M TTS?
Yes — runs locally
~144 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 Ti SUPER (16 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 144 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 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run the high-quality Kokoro 82M TTS model on your NVIDIA GeForce RTX 4070 Ti SUPER with Grade S performance, achieving ~955 tok/sec using the ONNX-Q8F16 quantization.
Prerequisites
Before starting, ensure you have at least 1GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60.11 or later) installed along with CUDA 11.8.
Expected performance
With the ONNX-Q8F16 quantization, expect to achieve ~955 tok/sec with only 0.6GB VRAM in use, leaving 15.4GB of VRAM for context. This allows 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:onnx/model_q8f16.onnx3. Run it
ollama run onnx-community/Kokoro-82M-v1.0-ONNX:onnx/model_q8f16.onnx --interactive
ollama generate onnx-community/Kokoro-82M-v1.0-ONNX:onnx/model_q8f16.onnx --text 'Hello, how are you today?'4. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti 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. Additionally, enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 16GB VRAM, you can achieve a large context window while maintaining high performance.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers value to 64 or lower to decrease VRAM usage.
Slow inference speed
Ensure that CUDA is properly installed and configured. Enable flash attention with --flash-attn to improve speed.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for different scenarios. LM Studio is ideal for GUI-based workflows, llama.cpp offers more fine-grained control over optimizations, and Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with ease of use and strong performance on the NVIDIA GeForce RTX 4070 Ti SUPER.
Other models that run great on RTX 4070 Ti 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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