What GPU do I need to run inclusionAI/Ling-3.0-flash?
127.5B parameters, published in BF16. View on Hugging Face
Set up Ling-3.0-flashLing-3.0-flash is published by inclusionAI on Hugging Face, with 20,206 downloads and 385 likes to date. It's a BailingMoeV3ForCausalLM 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 | 237.5 GB | 285.0 GB | RTX A6000 | 6 | $2.18/hr |
| FP8 (quantized) | 118.7 GB | 142.5 GB | RTX 4090 | 3 | $1.45/hr |
| INT4 (quantized) | 59.4 GB | 71.2 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | RTX A5000 | 3 | $0.528/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 Ling-3.0-flash at its published (BF16) precision: 6× RTX A6000, at $0.363/hr per GPU ($2.18/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Ling-3.0-flash: common questions
Can Ling-3.0-flash run on a single GPU?
No. At BF16 it needs 285.0 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 48.0 GB RTX A6000, and it takes 6 of them.
How many GPUs do I need to run Ling-3.0-flash?
6 at BF16. It needs 285.0 GB of VRAM and the cheapest capable live offer is a 48.0 GB RTX A6000, so 6 of them come to $2.18/hr in total.
Does quantizing Ling-3.0-flash lower the GPU bill?
Yes. At BF16 the cheapest live fit is 6 RTX A6000 cards at $2.18/hr. At INT4 (quantized) it drops to one A100 at $1.21/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Ling models
- Ling-3.0-tiny (7.9B, BF16)
- Ling-mini-2.0 (16.3B, BF16)
Related reading: RTX A6000 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.
