What GPU do I need to run inclusionAI/Ling-3.0-flash?
127.5B parameters, published in BF16. View on Hugging Face
Ling-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.
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 caveat: 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× A40 on runpod, at $0.440/hr per GPU ($2.64/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More inclusionAI models
- LLaDA2.0-mini (16.3B, BF16)
- LLaDA2.1-mini (16.3B, BF16)
- Ling-3.0-tiny (7.9B, BF16)
- Ling-mini-2.0 (16.3B, BF16)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
