Can RTX 4060 Run Whisper Large?

Yes, the RTX 4060 runs Whisper Large-v3 very well. With only ~3GB of VRAM needed for the model in FP16, the RTX 4060’s 8GB GDDR6 has plenty of headroom for Whisper hosting. This is one of the best-matched workloads for this card, delivering real-time transcription with room to spare for other processes.

The Short Answer

YES. Whisper Large-v3 runs comfortably with fast transcription speeds.

Whisper Large-v3 has 1.55 billion parameters, translating to approximately 3.1GB in FP16. The RTX 4060 with 8GB VRAM loads the model with over 4GB to spare for audio buffers and batch processing. This is one of the few AI workloads where the RTX 4060 genuinely excels, as Whisper’s memory requirements are modest compared to LLMs or large diffusion models.

The RTX 4060’s Ada Lovelace architecture also brings hardware-accelerated FP16 and INT8 compute, which Whisper benefits from during the encoder and decoder passes. Transcription runs well above real-time speed.

VRAM Analysis

Even in FP16, Whisper Large-v3 uses less than half the RTX 4060’s VRAM. This leaves room for processing longer audio files and running batch transcription. However, if you want to pair Whisper with an LLM for summarisation or translation, the 8GB becomes insufficient. See our Whisper VRAM requirements page for all configurations.

Performance Benchmarks

Transcription speed measured as Real-Time Factor (RTF), where lower is faster. An RTF of 0.1 means 1 hour of audio is transcribed in 6 minutes:

The RTX 4060 transcribes 1 hour of audio in under 5 minutes, which is more than adequate for most production workflows. Faster Whisper with CTranslate2 further improves these numbers. Review speed comparisons on our benchmarks page.

Setup Guide

The fastest way to deploy Whisper Large-v3 is with faster-whisper, which uses CTranslate2 for optimised inference:

# Install faster-whisper
pip install faster-whisper

# Python one-liner for transcription
python -c "
from faster_whisper import WhisperModel
model = WhisperModel('large-v3', device='cuda', compute_type='float16')
segments, info = model.transcribe('audio.mp3', beam_size=5)
for segment in segments:
    print(f'[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}')
"

For a REST API endpoint, use the whisper-webui or faster-whisper-server projects:

# Run faster-whisper as an OpenAI-compatible API
pip install faster-whisper-server
faster-whisper-server --model large-v3 --device cuda --host 0.0.0.0 --port 8000

The FP16 compute type is optimal for the RTX 4060. INT8 quantisation saves VRAM but is unnecessary given the generous headroom, and can slightly reduce transcription accuracy.

Recommended Alternative

The RTX 4060 is genuinely a good fit for Whisper workloads on its own. If you need to run Whisper alongside an LLM for post-processing (summarisation, translation, entity extraction), then the RTX 4060 Ti with 16GB lets you run both Whisper and a quantised 7B model simultaneously.

For high-throughput transcription pipelines processing many hours of audio daily, the RTX 3090 offers faster processing and can handle concurrent streams. If you are also considering LLM workloads on this card, check our RTX 4060 DeepSeek analysis or the RTX 4060 Flux.1 guide. For a combined Whisper and LLM setup, see whether the RTX 5080 can run Whisper and LLM together. Browse all dedicated GPU servers or compare options in our best GPU for inference guide.