NVIDIA
NVIDIA
Language model · United States ·Dec 2019

StyleGAN2

Language Open (Restricted)
30M
Parameters

About StyleGAN2

StyleGAN2 is an AI model developed by NVIDIA, in the language category, released in 2019, made available as an open-weights model with 0B params.

On this page you'll find StyleGAN2's full specifications. Review provider pricing and benchmark scores below, or compare StyleGAN2 head-to-head with other language models.

→ More models by NVIDIA → All language models → AI benchmarks leaderboard

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Run StyleGAN2 locally — what to buy

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Sized to what StyleGAN2 actually needs (~8k context at Q4), not to the biggest GPU: Good = cheapest that runs it, Better = best value, Best = most headroom. Speed is a hardware estimate; anything past ~120 tok/s is shown as instant.

Best
Intel Arc B580 12GB
Consumer GPU · ~$249
~120 tok/s
Q4 · fits full context · up to 8k ctx
Runs with: ExLlamaV2, KoboldCpp, LM Studio
See full estimate →

Software support

·ExLlamaV2 ·KoboldCpp ·LM Studio ·MLX ·Ollama ·SGLang ·TensorRT-LLM ·llama.cpp ·vLLM ?KTransformers ?text-generation-webui

✓ measured · · compatible · — not supported. Informational only — speed is hardware-based.

Consumer GPU
120+ tok/s
33 options →
Apple
120+ tok/s
18 options →
Datacenter GPU
120+ tok/s
13 options →

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Frequently asked questions

What is StyleGAN2? ▶

StyleGAN2 is an AI model developed by NVIDIA, in the language category, released in 2019. It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.

Who created StyleGAN2? ▶

StyleGAN2 was developed by NVIDIA and released in 2019.

Is StyleGAN2 open source or proprietary? ▶

StyleGAN2 ships as an open-weights model: you can download and self-host the weights, though the licence may place some restrictions on use.

How much does StyleGAN2 cost? ▶

Pricing for StyleGAN2 depends on the provider. See the providers table on this page for the latest API rates.

How does StyleGAN2 perform on benchmarks? ▶

Benchmark scores for StyleGAN2 are listed on this page as they are published. You can compare it head-to-head with other models on the GenAIList compare tool.

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