What GPU do I need to run 01-ai/Yi-6B?
6.1B parameters, published in BF16. View on Hugging Face
Set up Yi-6BYi-6B is published by 01-ai on Hugging Face, with 32,894 downloads and 375 likes to date. It's a LlamaForCausalLM model built for text-generation, 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 | 11.3 GB | 13.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 5.6 GB | 6.8 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 2.8 GB | 3.4 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 Yi-6B 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.
Yi-6B: common questions
Does Yi-6B fit on a 16 GB GPU?
Yes. At BF16 it needs 13.5 GB of VRAM, so a 16 GB card holds it with 2.5 GB to spare. A 12 GB card is not enough for it at BF16.
What is the least VRAM Yi-6B can run in?
3.4 GB, at INT4 (quantized), which fits a 6 GB card, against 13.5 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 Yi models
- Yi-6B-Chat (6.1B, BF16)
- Yi-6B-200K (6.1B, BF16)
- Yi-34B-Chat (34.4B, BF16)
- Yi-34B (34.4B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
