Can RTX 4070 SUPER run Whisper Medium?

Yes — runs locally

~132 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 4070 SUPER (12 GB VRAM) handles Whisper Medium comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 132 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 4070 SUPER

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

TL;DR

Run Whisper Medium on an NVIDIA GeForce RTX 4070 SUPER 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), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.

Expected performance

With the recommended settings, you can expect ~371 tok/sec performance, using approximately 1.9GB of VRAM, leaving 10.1GB 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 config set device cuda

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 ggerganov/whisper.cpp:ggml-medium.bin --n-gpu-layers 12 --flash-attn --tensor-parallelism 2

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use --n-gpu-layers 12 to offload most layers to the GPU, enable --flash-attn for faster attention computations, and set --tensor-parallelism 2 to utilize the GPU's parallel processing capabilities effectively.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 8 or 4 to lower VRAM usage.

Low tokenization speed

Ensure CUDA is properly installed and the device is set to cuda in Ollama config.

Inference crashes with a segmentation fault

Update your NVIDIA drivers to the latest version and reinstall CUDA 11.8.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or if you prefer a different framework. LM Studio is ideal for a GUI-based approach, llama.cpp offers more fine-grained control over quantization, and Jan is suitable for cloud deployments. However, Ollama provides a streamlined and efficient experience for running Whisper Medium on the NVIDIA GeForce RTX 4070 SUPER.

Full Whisper Medium details →

Other models that run great on RTX 4070 SUPER

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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