What GPU do I need to run unsloth/orpheus-3b-0.1-ft?
3.3B parameters, published in BF16. View on Hugging Face
Set up orpheus-3b-0.1-ftorpheus-3b-0.1-ft is published by unsloth on Hugging Face, with 55,455 downloads and 15 likes to date. It's a LlamaForCausalLM model built for text-to-speech, published natively in BF16.
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) |
|---|---|---|---|---|---|
| BF16 | 6.1 GB | 7.4 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 3.1 GB | 3.7 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.5 GB | 1.8 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 (BF16) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/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 8 GB GPU?
Yes. At BF16 it needs 7.4 GB of VRAM, so an 8 GB card holds it with 0.6 GB to spare. A 6 GB card is not enough for it at BF16.
How many copies of orpheus-3b-0.1-ft fit on one RTX 5060 Ti?
2, by VRAM alone. That card carries 16.0 GB and one copy needs 7.4 GB at BF16, on a live rate of $0.110/hr for the whole card. Throughput is not modelled here, so 2 copies is not 2 times the requests served.
What is the least VRAM orpheus-3b-0.1-ft can run in?
1.8 GB, at INT4 (quantized), which fits a 6 GB card, against 7.4 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Weight-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)
- alloma-3B-Instruct (3.2B, BF16)
- Llama-3.2-3B-Instruct (3.2B, BF16)
- Llama-3.2-3B (3.2B, BF16)
- Llama-3.2-3B-Instruct (3.2B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
