Can RTX 3070 Ti run Whisper Medium?

Yes — runs locally

~90 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

8 GB

Model size

0.77B

Best quant

Q8_0

VRAM needed

1.9 GB

The verdict

The RTX 3070 Ti (8 GB VRAM) handles Whisper Medium comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 90 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 3070 Ti

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

TL;DR

Run Whisper Medium on an NVIDIA GeForce RTX 3070 Ti with Q8_0 quantization for Grade S performance at ~247 tok/sec.

Prerequisites

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

Expected performance

With the Q8_0 quantization, you can expect ~247 tok/sec performance using approximately 1.9GB of VRAM, leaving 6.1GB of VRAM available for context. This allows for a practical context window of several minutes of audio, depending on the specific requirements.

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 --model ggerganov/whisper.cpp:ggml-medium.bin --n-gpu-layers 32 --flash-attn --tensor-parallelism 1

4. Optimize for RTX 3070 Ti

For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory without running out of VRAM. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 1 to avoid splitting the model across multiple GPUs.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 24 or 16 to lower VRAM usage.

Low tokenization speed

Ensure CUDA is properly installed and the correct version of the NVIDIA driver is used.

Model not found

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

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a more user-friendly interface but may require more system resources. llama.cpp is highly optimized for CPU inference and can be a good choice if you need to run the model on a system without a GPU. Jan is another lightweight runtime that can be used for quick prototyping, but it may not offer the same level of performance as Ollama on this GPU.

Full Whisper Medium details →

Other models that run great on RTX 3070 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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