What GPU do I need to run openai-community/openai-gpt?
120M parameters, published in F32. View on Hugging Face
openai-gpt is published by openai-community on Hugging Face, with 173,863 downloads and 304 likes to date. It's a OpenAIGPTLMHeadModel 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 openai-gpt at its published (F32) precision: 1× P4 on akash, at $0.032/hr per GPU ($0.032/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 openai-community models
- gpt2 (137M, F32)
- gpt2-large (812M, F32)
- gpt2-medium (380M, F32)
- gpt2-xl (1.6B, F32)
- Qwen3-0.6B (752M, BF16)
- Qwen3-8B (8.2B, BF16)
