Speech / TTS
How to deploy whisper-large-v3 on a GPU cloud
A 1.5B-parameter speech model. Full specs, license and use cases.
whisper-large-v3 size and hardware requirements
1.5B
Total parameters
Dense (no MoE)
Architecture
F16
Published precision
3.4 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP16 | 2.9 GB | 3.4 GB | V100 | 1 | $0.060/hr |
| FP8 (quantized) | 1.4 GB | 1.7 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 0.7 GB | 0.9 GB | A16 | 1 | $0.059/hr |
How to run whisper-large-v3
Run whisper-large-v3 with Transformers (Python)
Generic example using Hugging Face's transformers library, not from the model's own docs.
from transformers import pipeline
asr = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3", device="cuda")
result = asr("audio.wav")
print(result["text"])Deploy whisper-large-v3 on Aquanode
Aquanode has no one-click deploy template for whisper-large-v3; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× V100 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod