What GPU do I need to run swiss-ai/Apertus-70B-Instruct-2509?
70.6B parameters, published in BF16. View on Hugging Face
Apertus-70B-Instruct-2509 is published by swiss-ai on Hugging Face, with 29,139 downloads and 194 likes to date. It's a ApertusForCausalLM 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 Apertus-70B-Instruct-2509 at its published (BF16) precision: 7× RTX 3090 on simplepod, at $0.160/hr per GPU ($1.12/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 swiss-ai models
- Apertus-8B-Instruct-2509 (8.1B, BF16)
- Apertus-8B-2509 (8.1B, BF16)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
