Can RTX 5080 run Whisper Medium?

Yes — runs locally

~156 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

0.77B

Best quant

Q8_0

VRAM needed

1.9 GB

The verdict

The RTX 5080 (16 GB VRAM) handles Whisper Medium comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 156 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Mid-size Whisper model. Strong multilingual speech recognition.

Setup tutorial: Whisper Medium on RTX 5080

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Whisper Medium runs at Grade S on the NVIDIA GeForce RTX 5080 with Q8_0 quantization, achieving ~495 tok/sec.

Prerequisites

Before starting, ensure you have at least 2GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) with CUDA 11.8 installed.

Expected performance

You can expect ~495 tok/sec performance with 1.9GB VRAM in use, leaving 14.1GB of VRAM for context. This allows for a practical context window of several minutes of audio, depending on the complexity of the input.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q8_0 quantized version of Whisper Medium (1.4GB) from Hugging Face.

ollama pull ggerganov/whisper.cpp:ggml-medium.bin

3. Run it

ollama run --model ggerganov/whisper.cpp:ggml-medium.bin --n-gpu-layers 32 --flash-attn
ollama interactive --model ggerganov/whisper.cpp:ggml-medium.bin

4. Optimize for RTX 5080

For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn to speed up attention computations. With 1.9GB VRAM used by the model, you have 14.1GB of VRAM left for context, allowing for longer audio clips without running out of memory.

Troubleshooting

Out of memory errors during inference

Reduce the number of --n-gpu-layers or decrease the batch size.

Slow inference times

Ensure CUDA is properly installed and enabled with --flash-attn.

Inference fails with unrecognized commands

Verify that Ollama is installed correctly and try reinstalling with 'pip install --upgrade ollama'.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for advanced customization options, or Jan for lightweight deployment. Each has its own strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 5080.

Full Whisper Medium details →

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FAQ (20)

What GPU do I need to run Whisper Medium?

To run Whisper Medium, you need a GPU with at least 1.9 GB of VRAM. NVIDIA GPUs such as the GTX 1060 or higher are recommended for optimal performance.

Is Whisper Medium good for coding?

Whisper Medium is primarily designed for speech recognition and is not optimized for coding tasks. For coding, models like Codex or CodeLlama are more suitable.

Whisper Medium vs Llama 3.1 8B?

Whisper Medium has 0.77 billion parameters and is specialized for speech recognition, while Llama 3.1 8B has 8 billion parameters and is a general-purpose language model. Llama 3.1 8B is better for text generation but requires more resources.

Can I run Whisper Medium on a Mac?

Yes, you can run Whisper Medium on a Mac. Ensure your Mac has a compatible GPU with at least 1.9 GB of VRAM and the necessary drivers installed.

How much VRAM does Whisper Medium need?

Whisper Medium requires at least 1.9 GB of VRAM to run efficiently. This can vary slightly depending on the quantization level used.

Is Whisper Medium censored?

Whisper Medium is not censored. It is an open-source model released under the MIT license, allowing for unrestricted use and modification.

Is Whisper Medium commercial-use allowed?

Yes, Whisper Medium is licensed under the MIT license, which allows for commercial use without any restrictions.

Whisper Medium context length?

The context length for Whisper Medium is not explicitly defined, but it is designed to handle typical speech segments effectively. For longer audio, you may need to split the input into smaller chunks.

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