Speech / TTS
How to deploy orpheus-3b-0.1-ft on a GPU cloud
A 3.8B-parameter speech model. Full specs, license and use cases.
orpheus-3b-0.1-ft size and hardware requirements
3.8B
Total parameters
Dense (no MoE)
Architecture
F32
Published precision
16.9 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 14.1 GB | 16.9 GB | RTX 3090 | 1 | $0.147/hr |
| FP8 (quantized) | 3.5 GB | 4.2 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 1.8 GB | 2.1 GB | RTX 4070 Super | 1 | $0.110/hr |
How to run orpheus-3b-0.1-ft
Run orpheus-3b-0.1-ft with Transformers (Python)
Generic example using Hugging Face's transformers library, not from the model's own docs.
from transformers import pipeline
import soundfile as sf
tts = pipeline("text-to-speech", model="canopylabs/orpheus-3b-0.1-ft", device="cuda")
speech = tts("Hello from Aquanode.")
sf.write("output.wav", speech["audio"], speech["sampling_rate"])Deploy orpheus-3b-0.1-ft on Aquanode
Aquanode has no one-click deploy template for orpheus-3b-0.1-ft; 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× RTX 3090 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