What GPU do I need to run swiss-ai/Apertus-8B-Instruct-2509?
8.1B parameters, published in BF16. View on Hugging Face
Set up Apertus-8B-Instruct-2509Apertus-8B-Instruct-2509 is published by swiss-ai on Hugging Face, with 623,818 downloads and 489 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 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Apertus-8B-Instruct-2509 at its published (BF16) precision: 1× RTX 3090 on akash, at $0.147/hr per GPU ($0.147/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Apertus-8B-Instruct-2509: common questions
Does Apertus-8B-Instruct-2509 fit on a 24 GB GPU?
Yes. At BF16 it needs 18.0 GB of VRAM, so a 24 GB card holds it with 6.0 GB to spare. A 16 GB card is not enough for it at BF16.
What is the least VRAM Apertus-8B-Instruct-2509 can run in?
4.5 GB, at INT4 (quantized), which fits a 6 GB card, against 18.0 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.
Does quantizing Apertus-8B-Instruct-2509 lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 3090 on akash at $0.147/hr. At INT4 (quantized) it drops to one RTX 3070 on simplepod at $0.050/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More swiss-ai models
- Apertus-70B-Instruct-2509 (70.6B, BF16)
- Apertus-8B-2509 (8.1B, BF16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
