You need to train a ControlNet model with Stable Diffusion XL for an image editing use case. You want to train this model as quickly as possible. Which hardware configuration should you choose to…

The correct answer is B. Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use. An A100 GPU with 80 GB of RAM is optimized for training large models like ControlNet with Stable Diffusion XL. Using bfloat16 precision accelerates training while maintaining model accuracy and is natively supported by A100 GPUs. This configuration offers the best performance…

Submitted by rohit_dlh· Apr 18, 2026ML model development

Question

You need to train a ControlNet model with Stable Diffusion XL for an image editing use case. You want to train this model as quickly as possible. Which hardware configuration should you choose to train your model?

Options

AConfigure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use
BConfigure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use
CConfigure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of
DConfigure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of

How the community answered

(46 responses)

A
15% (7)
B
76% (35)
C
2% (1)
D
7% (3)

Explanation

An A100 GPU with 80 GB of RAM is optimized for training large models like ControlNet with Stable Diffusion XL. Using bfloat16 precision accelerates training while maintaining model accuracy and is natively supported by A100 GPUs. This configuration offers the best performance for fast training without compromising quality.

Topics

#GPU Selection#Deep Learning Hardware#Model Training Performance#Diffusion Models

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