What GPU do I need to run EleutherAI/pythia-2.8b?
2.9B parameters, published in F16. View on Hugging Face
Set up pythia-2.8bpythia-2.8b is published by EleutherAI on Hugging Face, with 43,800 downloads and 35 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP16 | 5.4 GB | 6.5 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 2.7 GB | 3.3 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.4 GB | 1.6 GB | RTX 5060 Ti | 1 | $0.110/hr |
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 pythia-2.8b at its published (F16) precision: 1× V100, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
pythia-2.8b: common questions
Does pythia-2.8b fit on a 8 GB GPU?
Yes. At FP16 it needs 6.5 GB of VRAM, so an 8 GB card holds it with 1.5 GB to spare. A 6 GB card is not enough for it at FP16.
How many copies of pythia-2.8b fit on one V100?
2, by VRAM alone. That card carries 16.0 GB and one copy needs 6.5 GB at FP16, on a live rate of $0.088/hr for the whole card. Throughput is not modelled here, so 2 copies is not 2 times the requests served.
What is the least VRAM pythia-2.8b can run in?
1.6 GB, at INT4 (quantized), which fits a 6 GB card, against 6.5 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.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Pythia models
- pythia-1.4b (1.5B, F16)
- pythia-1.4b-deduped (1.4B, F32)
- pythia-6.9b (7.0B, F16)
- pythia-1b (1.1B, F16)
- pythia-1b-deduped (1.1B, F16)
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
