Can RTX 3070 Ti run Kokoro 82M TTS?
Yes — runs locally
~90 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3070 Ti (8 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 90 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 3070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run the high-quality Kokoro 82M TTS model on an NVIDIA GeForce RTX 3070 Ti with Ollama using the ONNX-Q8F16 quantization. Expect Grade S performance at ~477 tok/sec.
Prerequisites
Before starting, ensure you have at least 100MB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 512.95 or later), and CUDA 11.2 or later installed.
Expected performance
With the ONNX-Q8F16 quantization, expect the model to run at ~477 tok/sec, utilizing 0.6GB of VRAM. Given the remaining 7.4GB of VRAM, you can achieve a practical context window of several hundred tokens without running out of memory.
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 stream4. Optimize for RTX 3070 Ti
For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 64 to utilize the 8GB VRAM efficiently. Flash attention is not applicable for this model, but tensor parallelism can be used if you have multiple GPUs. With 0.6GB VRAM usage, you have 7.4GB of VRAM headroom for larger context windows.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using the --n-gpu-layers flag, e.g., --n-gpu-layers 32.
Slow inference speed
Ensure CUDA is properly installed and configured. Verify that the Ollama runtime is set to use the CUDA backend with 'ollama config set runtime cuda'.
Model not loading
Check the model path and ensure it is correctly specified. Use 'ollama list' to verify the model is available.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can also run this model. LM Studio is suitable for users who prefer a graphical interface, while llama.cpp offers more control over low-level optimizations. Jan is a good choice for those who need a lightweight, portable solution. For the NVIDIA GeForce RTX 3070 Ti, Ollama provides the best balance of ease of use and performance.
Other models that run great on RTX 3070 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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