Can RTX 3080 Ti run Whisper Medium?

Yes — runs locally

~108 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

12 GB

Model size

0.77B

Best quant

Q8_0

VRAM needed

1.9 GB

The verdict

The RTX 3080 Ti (12 GB VRAM) handles Whisper Medium comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 108 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 3080 Ti

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

TL;DR

Run Whisper Medium on an NVIDIA GeForce RTX 3080 Ti with Q8_0 quantization for Grade S performance at ~371 tok/sec.

Prerequisites

Before starting, ensure you have at least 1.4GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 512.15 or later), and CUDA 11.4 or later installed.

Expected performance

With the Q8_0 quantization, you can expect the model to run at ~371 tok/sec, using 1.9GB of VRAM. The remaining 10.1GB of VRAM provides ample headroom for processing longer audio contexts, enabling high-performance speech recognition.

1. Install runtimeOllama

sudo apt-get update && sudo apt-get install -y ollama
ollama init

2. Download the model

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

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

3. Run it

ollama run --model ggerganov/whisper.cpp:ggml-medium.bin --device cuda
ollama interactive

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, set --n-gpu-layers to 24 to utilize the GPU efficiently. Enable flash-attn to reduce memory usage and improve speed. With 1.9GB VRAM used by the model, you will have approximately 10.1GB of VRAM left for context, allowing for a practical context window of several minutes of audio.

Troubleshooting

CUDA out of memory error

Reduce the number of GPU layers with --n-gpu-layers 16 or lower.

Slow inference speed

Ensure flash-attn is enabled with --flash-attn true.

Model not found

Verify the model path and re-run the pull command: ollama pull ggerganov/whisper.cpp:ggml-medium.bin

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. Ollama is recommended for its ease of use and efficient GPU utilization on the NVIDIA GeForce RTX 3080 Ti.

Full Whisper Medium details →

Other models that run great on RTX 3080 Ti

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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