Can RTX 5090 run Kokoro 82M TTS?
Yes — runs locally
~216 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 216 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run the high-quality Kokoro 82M TTS model on your NVIDIA GeForce RTX 5090 with Ollama using the ONNX-Q8F16 quantization. Expect Grade S performance at ~1910 tok/sec.
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 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the ONNX-Q8F16 quantization, you can expect ~1910 tok/sec performance, using approximately 0.6GB of VRAM. This leaves 31.4GB of VRAM available for context, allowing 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: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 "Your text here"4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 82 to fully utilize the GPU's 32GB VRAM. Enable flash attention with --flash-attn to further enhance speed. Tensor parallelism is not necessary for this model but can be explored for larger models.
Troubleshooting
Low token generation speed
Ensure that the CUDA runtime is correctly configured with 'ollama config set runtime cuda'. Also, verify that the --n-gpu-layers flag is set to 82.
Out of memory errors
Reduce the --n-gpu-layers value to 64 or lower to decrease VRAM usage.
Inconsistent audio output
Check the model's configuration and ensure that the audio parameters (e.g., sample rate) are correctly set.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more graphical interface, llama.cpp for CPU-based inference, or Jan for a lightweight, web-based solution. Ollama is recommended for its ease of use and CUDA backend support, which is ideal for the NVIDIA GeForce RTX 5090.
Other models that run great on RTX 5090
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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