What GPU do I need to run openai-community/gpt2-xl?
1.6B parameters, published in F32. View on Hugging Face
Set up gpt2-xlgpt2-xl is published by openai-community on Hugging Face, with 130,028 downloads and 384 likes to date. It's a GPT2LMHeadModel 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 6.0 GB | 7.2 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 1.5 GB | 1.8 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 0.7 GB | 0.9 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 gpt2-xl at its published (F32) 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.
gpt2-xl: common questions
Does gpt2-xl fit on a 8 GB GPU?
Yes. At FP32 it needs 7.2 GB of VRAM, so an 8 GB card holds it with 0.8 GB to spare. A 6 GB card is not enough for it at FP32.
Can gpt2-xl run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 6.0 GB, or 7.2 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 3.0 GB, or 3.6 GB with overhead. That moves it onto a 6 GB card instead of an 8 GB one. How much accuracy the cast costs is model-specific and is not measured here.
How many copies of gpt2-xl fit on one V100?
2, by VRAM alone. That card carries 16.0 GB and one copy needs 7.2 GB at FP32, 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 gpt2-xl can run in?
0.9 GB, at INT4 (quantized), which fits a 6 GB card, against 7.2 GB at FP32. 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 openai-community models
- gpt2-large (812M, F32)
- gpt2-medium (380M, F32)
- gpt2 (137M, F32)
- openai-gpt (120M, F32)
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
