Can RTX 4070 Ti SUPER run Whisper Tiny English (Quantized)?

Yes — runs locally

~144 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

0.039B

Best quant

Q5_1

VRAM needed

0.1 GB

The verdict

The RTX 4070 Ti SUPER (16 GB VRAM) handles Whisper Tiny English (Quantized) comfortably using the Q5_1 quantization, which fits in 0.1 GB. Expected throughput is around 144 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Smallest possible speech recognition model. Only 32MB. English only. Default speech model - guaranteed to run on any iPhone.

Setup tutorial: Whisper Tiny English (Quantized) on RTX 4070 Ti SUPER

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

TL;DR

The Whisper Tiny English (Quantized) model runs at Grade S on an NVIDIA GeForce RTX 4070 Ti SUPER with Q5_1 quantization, achieving ~955 tok/sec.

Prerequisites

Before starting, ensure you have at least 32MB of disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the Q5_1 quantization, the model is expected to achieve ~955 tok/sec, utilizing only 0.1GB of VRAM. This leaves 15.9GB of VRAM available for context, allowing for a practical context window of several minutes of audio.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the 0.0GB Q5_1 quantized model from Hugging Face.

ollama pull ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin

3. Run it

ollama run ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin --device cuda
ollama chat --model ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin

4. Optimize for RTX 4070 Ti SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 12 to utilize the available VRAM efficiently. Enable flash attention (--flash-attn) for faster inference and consider using tensor parallelism (--tensor-parallel-size 2) if you are running multiple instances.

Troubleshooting

Model does not load due to insufficient VRAM.

Reduce --n-gpu-layers to 8 or 4.

Inference is slow.

Ensure CUDA is properly installed and enabled with 'ollama config set runtime cuda'.

Flash attention causes errors.

Disable flash attention with '--no-flash-attn' and try again.

Alternative runtimes

Alternative runtimes include LM Studio and llama.cpp. LM Studio is suitable for a graphical interface and easier model management, while llama.cpp offers more customization and control over inference parameters. Jan is another option for users preferring a lightweight, command-line interface. For the NVIDIA GeForce RTX 4070 Ti SUPER, Ollama is recommended for its ease of use and CUDA integration.

Full Whisper Tiny English (Quantized) details →

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FAQ (20)

What GPU do I need to run Whisper Tiny English (Quantized)?

Whisper Tiny English (Quantized) requires minimal GPU resources, needing only 0.1 GB of VRAM. It can run efficiently on most modern GPUs, including integrated graphics.

Is Whisper Tiny English (Quantized) good for coding?

Whisper Tiny English (Quantized) is primarily designed for speech recognition and may not be optimized for coding tasks. However, it can be useful for voice-to-text applications in development environments.

Whisper Tiny English (Quantized) vs Llama 3.1 8B?

Whisper Tiny English (Quantized) has only 0.039 billion parameters, making it much smaller and more resource-efficient compared to Llama 3.1 8B, which has 8 billion parameters. It is ideal for low-resource devices but less powerful for complex tasks.

Can I run Whisper Tiny English (Quantized) on a Mac?

Yes, Whisper Tiny English (Quantized) can run on a Mac. It is lightweight and compatible with macOS, requiring minimal system resources.

How much VRAM does Whisper Tiny English (Quantized) need?

Whisper Tiny English (Quantized) requires only 0.1 GB of VRAM, making it suitable for devices with limited graphics memory.

Is Whisper Tiny English (Quantized) censored?

Whisper Tiny English (Quantized) is not censored. It processes speech data as input without any content filtering or restrictions.

Is Whisper Tiny English (Quantized) commercial-use allowed?

Yes, Whisper Tiny English (Quantized) is licensed under the MIT license, allowing commercial use without restrictions.

Whisper Tiny English (Quantized) context length?

The context length for Whisper Tiny English (Quantized) is not explicitly defined, but it is designed to handle short speech segments efficiently.

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