Can RTX 3090 Ti run Kokoro 82M TTS?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3090 Ti (24 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 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run the high-quality Kokoro 82M TTS model on your NVIDIA GeForce RTX 3090 Ti with Grade S performance, using the ONNX-Q8F16 quantization. Expect ~1432 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 525.60.11 or later), and CUDA 11.8 installed.
Expected performance
With the ONNX-Q8F16 quantization, you can expect the model to run at approximately 1432 tokens per second, consuming around 0.6GB of VRAM. Given the remaining 23.4GB of VRAM, you can achieve a practical context window of several thousand tokens, depending on the specific requirements of your application.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the 0.1GB 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 --device cuda
ollama interactive onnx-community/Kokoro-82M-v1.0-ONNX:q8f16 --device cuda4. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. The --flash-attn flag can also enhance performance. With 0.6GB VRAM usage, you have 23.4GB of VRAM headroom, allowing for large context windows. For example, you can set --n-gpu-layers 128 and --flash-attn true to maximize efficiency.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using --n-gpu-layers <N> where <N> is a lower value.
Slow inference speed
Enable flash attention with --flash-attn true and increase the number of GPU layers with --n-gpu-layers <N>.
Model not found
Ensure the model is correctly downloaded and available in the Ollama cache. Run ollama list to check.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for specific use cases. LM Studio offers a user-friendly interface and is suitable for non-technical users. llama.cpp provides more control over optimizations and is ideal for advanced users. Jan is lightweight and efficient but may lack some features. Choose based on your specific needs and comfort level with the tools.
Other models that run great on RTX 3090 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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