What GPU do I need to run canopylabs/orpheus-3b-0.1-ft?
A 3.8B-parameter speech model. 3.8B parameters, published in F32. View on Hugging FaceGated
orpheus-3b-0.1-ft is published by canopylabs on Hugging Face, with 237,323 downloads and 730 likes to date. It's a LlamaForCausalLM model built for text-to-speech, published natively in F32, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
What orpheus-3b-0.1-ft is
orpheus-3b-0.1-ft is a 3.8B-parameter model published by Canopy Labs on Hugging Face that synthesizes speech from text, released under Apache 2.0.
License note: fully permissive, no gating and no usage restrictions. Facts in this section are sourced from orpheus-3b-0.1-ft's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Speech synthesis
- Voice agent pipelines
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 14.1 GB | 16.9 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 3.5 GB | 4.2 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.8 GB | 2.1 GB | RTX 5060 Ti | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run orpheus-3b-0.1-ft at its published (F32) precision: 1× RTX A5000, at $0.176/hr per GPU ($0.176/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
orpheus-3b-0.1-ft: common questions
Does orpheus-3b-0.1-ft fit on a 24 GB GPU?
Yes. At FP32 it needs 16.9 GB of VRAM, so a 24 GB card holds it with 7.1 GB to spare. A 16 GB card is not enough for it at FP32.
Do I need approval to download orpheus-3b-0.1-ft?
Yes. canopylabs gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 16.9 GB the model needs once you have them.
Can orpheus-3b-0.1-ft run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 14.1 GB, or 16.9 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 7.0 GB, or 8.5 GB with overhead. That moves it onto a 12 GB card instead of a 24 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM orpheus-3b-0.1-ft can run in?
2.1 GB, at INT4 (quantized), which fits a 6 GB card, against 16.9 GB at FP32. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
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 A5000 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 podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 3.2 models
- svara-tts-v1 (3.3B, F32)
- orpheus-3b-0.1-ft (3.3B, BF16)
- alloma-3B-Instruct (3.2B, BF16)
- Llama-3.2-3B-Instruct (3.2B, BF16)
- Llama-3.2-3B (3.2B, BF16)
Related reading: RTX A5000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
