Can RTX 5080 run Kokoro 82M TTS?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5080 (16 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 156 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 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Kokoro 82M TTS model runs at Grade S on an NVIDIA GeForce RTX 5080 with the ONNX-Q8F16 quantization, achieving ~955 tok/sec.
Prerequisites
Before starting, ensure you have at least 100MB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.
Expected performance
With the ONNX-Q8F16 quantization, the Kokoro 82M TTS model should achieve ~955 tok/sec on the NVIDIA GeForce RTX 5080, using approximately 0.6GB of VRAM. This leaves 15.4GB of VRAM available for context, allowing for a practical context window of several minutes of audio synthesis.
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 --model onnx-community/Kokoro-82M-v1.0-ONNX:onnx/model_q8f16.onnx --text 'Hello, how are you today?'4. Optimize for RTX 5080
For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. The ONNX-Q8F16 quantization is already optimized for low VRAM usage, so you can set --n-gpu-layers to a high value like 128. Flash attention is not applicable here, but tensor parallelism can be used to further speed up inference if you have multiple GPUs. With 16GB VRAM, you should have ample headroom for large contexts.
Troubleshooting
Low token generation speed
Increase the --n-gpu-layers value to offload more layers to the GPU.
Out of memory errors
Reduce the --n-gpu-layers value or decrease the batch size.
Inference is not interactive enough
Use the --interactive flag and adjust the --temperature and --top-k parameters for better responsiveness.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can also be used for running the Kokoro 82M TTS model. LM Studio is suitable for users who prefer a GUI, while llama.cpp offers more fine-grained control over inference settings. Jan is a lightweight alternative that can be useful for quick prototyping. For the NVIDIA GeForce RTX 5080, Ollama is generally the most performant option due to its optimized CUDA backend.
Other models that run great on RTX 5080
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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