Can RTX 3090 Run Whisper Large-v3?

Yes, the RTX 3090 runs Whisper Large-v3 effortlessly. At only ~3.1GB in FP16, Whisper leaves over 20GB of the RTX 3090’s 24GB VRAM free for concurrent streams, batch processing, or pairing with an LLM. For Whisper hosting at scale, the 3090 is one of the strongest single-GPU options available.

The Short Answer

YES. Whisper Large-v3 uses under 4GB, leaving 20GB+ free for other tasks.

Whisper Large-v3 with 1.55 billion parameters needs roughly 3.1GB in FP16. The RTX 3090 with 24GB GDDR6X loads the model and has enough remaining VRAM to simultaneously run a 7B LLM for post-processing, handle multiple concurrent transcription streams, or process batch audio files with large buffers.

The 3090’s high memory bandwidth (936 GB/s) also accelerates the encoder and decoder passes, delivering some of the fastest single-GPU transcription speeds available on consumer hardware.

VRAM Analysis

The standout capability is running Whisper alongside a full LLM. Transcribe audio with Whisper, then pipe the text to LLaMA 3 8B for summarisation, translation, or entity extraction, all on a single GPU. Review our Whisper VRAM requirements guide for all model sizes and combinations.

Performance Benchmarks

Transcription speed as Real-Time Factor (RTF). Lower is faster:

The RTX 3090 transcribes 1 hour of audio in 3 minutes with FP16, and can process up to 6 concurrent streams when using INT8 quantisation. For production transcription pipelines processing hundreds of hours daily, this throughput is significant. See comparisons on our benchmarks page.

Setup Guide

faster-whisper with CTranslate2 is the optimal deployment for the RTX 3090:

# Install faster-whisper
pip install faster-whisper

# High-throughput transcription with batched decoding
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, batch_size=16)
for segment in segments:
    print(f'[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}')
"

For an API server handling concurrent requests:

# faster-whisper server with concurrent processing
pip install faster-whisper-server
faster-whisper-server \
  --model large-v3 \
  --device cuda \
  --compute-type float16 \
  --host 0.0.0.0 --port 8000

The batch_size=16 parameter in faster-whisper processes multiple audio segments in parallel, fully utilising the 3090’s compute capacity. With 20GB+ free VRAM, you can also load an LLM in a separate process for post-processing.

Recommended Alternative

The RTX 3090 is already overkill for Whisper alone. The real value is in combined workloads. If you need even more concurrent streams or faster processing, the RTX 5090 with 32GB delivers better throughput. See whether the RTX 5090 can run DeepSeek and Whisper together for the ultimate pipeline.

For other 3090 workloads, check whether it can run LLaMA 3 8B in FP16, run Mixtral 8x7B, or run SDXL and LLM together. If Whisper is your primary workload and budget matters, the RTX 4060 handles Whisper well at a lower price. Browse configurations on our dedicated GPU servers page or read the best GPU for inference guide.