Google Cloud Deep Learning VM Image
Reviews
Star rating
Rank in Artificial Neural Network
Pros and cons
Pros
Cons
Traffic & LLM citations
Source: G2 review data
Winning from
Key stats
Adoption by company size
| Segment | Share of reviews |
|---|---|
| Small Business | 40.4% |
| Mid-Market | 35.1% |
| Enterprise | 24.6% |
Quarterly history
| Quarter | Reviews | Star rating |
|---|---|---|
| 2026 Q2 | 1 | 4.0 |
| 2026 Q3 | 42 | 4.3 |
Satisfaction ratings
What reviewers say
Seamless Scaling from CPU Prototypes to Multi-GPU Power on Google Compute Engine
“Seamless scalability: since it runs on Google Compute Engine, I can start by prototyping on a lightweight CPU instance and then move up to high-end multi-GPU instances (like NVIDIA L4, A100, or H100) while keeping the exact same environment configuration.”
GPU-Ready GCP Images Make ML Setup Painless—Train in Jupyter Right Away
“The best part is never having to deal with broken GPU drivers or broken PyTorch/TensorFlow builds again. Setting up a local ML environment usually turns into a full day of resolving CUDA and cuDNN version conflicts. With these GCP images,rhe drivers, libraries, and GPU runtime a”
Ready-to-Go ML Environment That Saves Time
“What i like most is that the environment is largely ready to go for ML work. Having thr common frameworks, GPU drivers, CUDA libraries and ML tooling preconfigured saves a lot of time. This is particularly useful for experimentation.”