What GPU do I need to run EleutherAI/gpt-neox-20b?
20.7B parameters, published in F16. View on Hugging Face
Set up gpt-neox-20bgpt-neox-20b is published by EleutherAI on Hugging Face, with 787,110 downloads and 585 likes to date. It's a GPTNeoXForCausalLM model built for text-generation, published natively in F16.
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 gpt-neox-20b at its published (F16) precision: 1× RTX 8000 on akash, at $0.221/hr per GPU ($0.221/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gpt-neox-20b: common questions
Can gpt-neox-20b run on a single GPU?
Yes, but not on a desktop card. At FP16 it needs 46.4 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 48.0 GB RTX 8000 on akash at $0.221/hr.
What is the least VRAM gpt-neox-20b can run in?
11.6 GB, at INT4 (quantized), which fits a 12 GB card, against 46.4 GB at FP16. 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 gpt-neox-20b lower the GPU bill?
Yes. At FP16 the cheapest live fit is one RTX 8000 on akash at $0.221/hr. At INT4 (quantized) it drops to one RTX 3060 on simplepod at $0.080/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More EleutherAI models
- pythia-160m (213M, F16)
- pythia-160m-deduped (213M, F16)
- gpt-neo-125m (150M, F32)
- pythia-410m (506M, F16)
- pythia-1.4b (1.5B, F16)
- pythia-1b (1.1B, F16)
