What GPU do I need to run ibm-research/PowerLM-3b?
3.5B parameters, published in F32. View on Hugging Face
PowerLM-3b is published by ibm-research on Hugging Face, with 200,564 downloads and 21 likes to date. It's a GraniteForCausalLM model built for text-generation, published natively in F32.
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 PowerLM-3b at its published (F32) precision: 1× V100 on simplepod, at $0.060/hr per GPU ($0.060/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 ibm-research models
- PowerMoE-3b (3.4B, F32)
- Qwen3-0.6B (752M, BF16)
- Qwen3-8B (8.2B, BF16)
- Qwen2.5-1.5B-Instruct (1.5B, BF16)
- gpt2 (137M, F32)
- Qwen2.5-7B-Instruct (7.6B, BF16)
