What GPU do I need to run internlm/Intern-S1?
240.7B parameters, published in BF16. View on Hugging Face
Intern-S1 is published by internlm on Hugging Face, with 19,518 downloads and 258 likes to date. It's a InternS1ForConditionalGeneration 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.
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 Intern-S1 at its published (BF16) precision: 7× A100 on runpod, at $1.19/hr per GPU ($8.33/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 internlm models
- internlm3-8b-instruct (8.8B, BF16)
- internlm2-chat-7b (7.7B, BF16)
- internlm2_5-7b-chat (7.7B, BF16)
- internlm2-chat-20b (19.9B, BF16)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
