What GPU do I need to run FINAL-Bench/Darwin-180B-RSI?
180.0B parameters, published in BF16. View on Hugging Face
Set up Darwin-180B-RSIDarwin-180B-RSI is published by FINAL-Bench on Hugging Face, with 843 downloads and 115 likes to date. It's a Qwen4ExpForConditionalGeneration model built for image-text-to-text, 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 | 335.3 GB | 402.3 GB | A100 | 6 | $7.27/hr |
| FP8 (quantized) | 167.6 GB | 201.2 GB | RTX 4080 Super | 7 | $2.37/hr |
| INT4 (quantized) | 83.8 GB | 100.6 GB | RTX A5000 | 5 | $0.880/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 Darwin-180B-RSI at its published (BF16) precision: 6× A100, at $1.21/hr per GPU ($7.27/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Darwin-180B-RSI: common questions
Can Darwin-180B-RSI run on a single GPU?
No. At BF16 it needs 402.3 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 80.0 GB A100, and it takes 6 of them.
How many GPUs do I need to run Darwin-180B-RSI?
6 at BF16. It needs 402.3 GB of VRAM and the cheapest capable live offer is a 80.0 GB A100, so 6 of them come to $7.27/hr in total.
Does quantizing Darwin-180B-RSI lower the GPU bill?
Yes. At BF16 the cheapest live fit is 6 A100 cards at $7.27/hr. At INT4 (quantized) it drops to 5 RTX A5000 cards at $0.880/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More FINAL-Bench models
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
