What GPU do I need to run microsoft/VibeVoice-1.5B?
2.7B parameters, published in BF16. View on Hugging Face
Set up VibeVoice-1.5BVibeVoice-1.5B is published by microsoft on Hugging Face, with 119,947 downloads and 2,464 likes to date. It's a VibeVoiceForConditionalGeneration 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 | 5.0 GB | 6.0 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 2.5 GB | 3.0 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.3 GB | 1.5 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 VibeVoice-1.5B 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.
VibeVoice-1.5B: common questions
Does VibeVoice-1.5B fit on a 8 GB GPU?
Yes. At BF16 it needs 6.0 GB of VRAM, so an 8 GB card holds it with 2.0 GB to spare. A 6 GB card is not enough for it at BF16.
How many copies of VibeVoice-1.5B fit on one RTX 5060 Ti?
2, by VRAM alone. That card carries 16.0 GB and one copy needs 6.0 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 VibeVoice-1.5B can run in?
1.5 GB, at INT4 (quantized), which fits a 6 GB card, against 6.0 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 VibeVoice models
- VibeVoice-ASR-HF (8.3B, BF16)
- VibeVoice-ASR (8.7B, BF16)
- VibeVoice-ASR-BitNet (323M, F32)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
