Can RTX 4070 Ti SUPER run Whisper Medium?

Yes — runs locally

~144 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 4070 Ti SUPER (16 GB VRAM) handles Whisper Medium comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 144 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 Ti SUPER

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

TL;DR

Whisper Medium runs at Grade S on the NVIDIA GeForce RTX 4070 Ti SUPER with Q8_0 quantization, achieving ~495 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 525.60.11 or later) with CUDA 11.8 installed.

Expected performance

With the Q8_0 quantization, you can expect ~495 tok/sec, using 1.9GB of VRAM. The remaining 14.1GB of VRAM provides ample headroom for handling large context windows, making it suitable for long audio transcriptions.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

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

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

3. Run it

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

4. Optimize for RTX 4070 Ti SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU. Enable flash attention (--flash-attn) for faster inference. With 1.9GB VRAM used by the model, you have 14.1GB of VRAM left for context, allowing for a large practical context window.

Troubleshooting

Out of memory error during inference

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

Low token generation speed

Ensure flash attention (--flash-attn) is enabled and check your CUDA installation.

Model not found

Verify the model path and ensure the model is correctly downloaded using the 'ollama pull' command.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or specific use cases. LM Studio is ideal for GUI-based model management, llama.cpp offers more fine-grained control over quantization, and Jan is suitable for distributed training scenarios. However, Ollama provides a streamlined and user-friendly experience, making it the best choice for most users on the NVIDIA GeForce RTX 4070 Ti SUPER.

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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