Speech / TTS
How to deploy Dia-1.6B on a GPU cloud
A 1.6B-parameter speech model. Full specs, license and use cases.
Dia-1.6B size and hardware requirements
1.6B
Total parameters
Dense (no MoE)
Architecture
F32
Published precision
7.2 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 6.0 GB | 7.2 GB | V100 | 1 | $0.060/hr |
| FP8 (quantized) | 1.5 GB | 1.8 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 0.8 GB | 0.9 GB | A16 | 1 | $0.059/hr |
How to run Dia-1.6B
Run Dia-1.6B 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="nari-labs/Dia-1.6B", device="cuda")
speech = tts("Hello from Aquanode.")
sf.write("output.wav", speech["audio"], speech["sampling_rate"])Deploy Dia-1.6B on Aquanode
Aquanode has no one-click deploy template for Dia-1.6B; 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