What GPU do I need to run EleutherAI/gpt-neo-1.3B?
1.4B parameters, published in F32. View on Hugging Face
gpt-neo-1.3B is published by EleutherAI on Hugging Face, with 45,908 downloads and 325 likes to date. It's a GPTNeoForCausalLM 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 gpt-neo-1.3B 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 EleutherAI models
- pythia-160m (213M, F16)
- gpt-neox-20b (20.7B, F16)
- pythia-160m-deduped (213M, F16)
- gpt-neo-125m (150M, F32)
- pythia-410m (506M, F16)
- pythia-1.4b (1.5B, F16)
