What GPU do I need to run microsoft/Phi-4-multimodal-instruct?
5.6B parameters, published in BF16. View on Hugging Face
Set up Phi-4-multimodal-instructPhi-4-multimodal-instruct is published by microsoft on Hugging Face, with 258,928 downloads and 1,613 likes to date. It's a Phi4MMForCausalLM model built for automatic-speech-recognition, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for activations and allocator fragmentation. Speech models don't build the same growing KV-cache a text model does. Memory scales primarily with input audio length. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 10.4 GB | 12.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 5.2 GB | 6.2 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 2.6 GB | 3.1 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 Phi-4-multimodal-instruct 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.
Phi-4-multimodal-instruct: common questions
Does Phi-4-multimodal-instruct fit on a 16 GB GPU?
Yes. At BF16 it needs 12.5 GB of VRAM, so a 16 GB card holds it with 3.5 GB to spare. A 12 GB card is not enough for it at BF16.
What is the least VRAM Phi-4-multimodal-instruct can run in?
3.1 GB, at INT4 (quantized), which fits a 6 GB card, against 12.5 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 Phi-4 models
- Phi-4-mini-instruct (3.8B, BF16)
- Phi-4-mini-reasoning (3.8B, BF16)
- phi-4 (14.7B, BF16)
- Phi-4-reasoning (14.7B, BF16)
- Phi-4-reasoning-plus (14.7B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
