Can RTX 3090 Ti run Whisper Medium?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3090 Ti (24 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 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Medium runs at Grade S on the NVIDIA GeForce RTX 3090 Ti with Q8_0 quantization, achieving ~742 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 525.60 or later) with CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you can expect ~742 tok/sec, with 1.9GB VRAM in use, leaving 22.1GB 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 init2. Download the model
Download the Q8_0 quantized version of Whisper Medium (1.4GB file) from Hugging Face.
ollama pull ggerganov/whisper.cpp:ggml-medium.bin3. Run it
ollama run ggerganov/whisper.cpp:ggml-medium.bin --model-path ggml-medium.bin
ollama interact ggerganov/whisper.cpp:ggml-medium.bin4. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU. Enable flash-attn for faster inference and consider using tensor parallelism if running multiple instances. With 1.9GB VRAM used by the model, you have 22.1GB of VRAM left for context, allowing for large context windows.
Troubleshooting
Low tokenization speed
Ensure that the latest NVIDIA drivers and CUDA are installed. Check if the --n-gpu-layers parameter is set correctly.
Out of memory errors
Reduce the --n-gpu-layers parameter or increase the batch size to better manage VRAM usage.
Inference is slow
Enable flash-attn and ensure that the GPU is not being throttled due to power or thermal limits.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more customization options or support for different frameworks. LM Studio is ideal for a graphical interface, llama.cpp offers more control over quantization, and Jan is suitable for distributed training scenarios. However, Ollama provides a streamlined and easy-to-use experience for running Whisper Medium on the NVIDIA GeForce RTX 3090 Ti.
Other models that run great on RTX 3090 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.
Want personalized recommendations for your exact setup? Detect my hardware →