Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A machine learning engineer is deploying a large generative model on Vertex AI. The model requires a GPU with high memory. Which machine configuration should they choose?
⚠ Common exam trap
A common mix-up: candidates choose a cheaper or single-GPU option (like C or D) without calculating the total GPU memory needed, or mistakenly think a CPU-only instance (A) can handle GPU-accelerated workloads, ignoring that large generative models require both high GPU memory and parallel processing.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
a2-highgpu-4g with 4 A100 GPUs
The a2-highgpu-4g machine series is specifically designed for large-scale GPU-accelerated workloads, offering 4 NVIDIA A100 GPUs with 40GB of high-bandwidth memory (HBM2e) each, totaling 160GB of GPU memory. This configuration provides the high memory capacity required for training or serving large generative models, such as LLMs or diffusion models, which often exceed the memory limits of smaller GPUs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
c2-standard-16 with no GPU
Why it's wrong here
A c2-standard-16 provides only CPU compute, so the model cannot load onto GPU memory at all. It tempts as a cost-saving general-purpose node, and would suit CPU-bound inference or preprocessing, but the stated high-memory GPU requirement is unmet.
- ✓
a2-highgpu-4g with 4 A100 GPUs
Why this is correct
The a2-highgpu-4g machine type pairs four NVIDIA A100 GPUs, each with 40 GB of high-bandwidth memory, satisfying the high GPU memory constraint for serving a large generative model. Smaller accelerator configurations would lack sufficient VRAM.
- ✗
n1-standard-4 with a single T4 GPU
Why it's wrong here
A single T4 offers only 16 GB of GPU memory, insufficient for a large generative model needing high memory. It tempts as a cheap inference accelerator, and would suit smaller models or light serving, but cannot hold the weights required here.
- ✗
n2-standard-8 with a single P4 GPU
Why it's wrong here
A single P4 provides 8 GB of GPU memory, well below what a large generative model demands. It tempts because P4 instances accelerate inference economically, and would suit modest vision or NLP workloads, but the high-memory requirement rules it out.
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