What GPU do I need to run microsoft/DialoGPT-small?
176M parameters, published in F16. View on Hugging Face
Set up DialoGPT-smallDialoGPT-small is published by microsoft on Hugging Face, with 50,885 downloads and 146 likes to date. It's a GPT2LMHeadModel 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 | 0.3 GB | 0.4 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 0.2 GB | 0.2 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 0.1 GB | 0.1 GB | RTX 4070 Super | 1 | $0.121/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 DialoGPT-small 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.
DialoGPT-small: common questions
How much VRAM does DialoGPT-small need?
0.4 GB at FP16, 0.2 GB at FP8 (quantized), 0.1 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 0.3 GB of weights plus inference overhead is the whole requirement.
How many copies of DialoGPT-small fit on one V100?
40, by VRAM alone. That card carries 16.0 GB and one copy needs 0.4 GB at FP16, on a live rate of $0.088/hr for the whole card. Throughput is not modelled here, so 40 copies is not 40 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More microsoft models
- trocr-base-printed (333M, F32)
- trocr-base-handwritten (333M, F32)
- kosmos-2-patch14-224 (1.7B, F32)
- FrogNano-4B-2609 (4.7B, BF16)
- Mage-VL (4.7B, BF16)
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
