How to run Large OpenWeight models on Infra like cloud run?

**Yes, this snippet is very good and standard practice** for handling large open-weights models like Gemma 4 on serverless infrastructure like Cloud Run.

### How This Works and Why It's Designed This Way

* **Bypassing Cloud Build Limits:** Cloud Build has strict storage limits and timeouts. Trying to download a massive 30GB+ model inside a standard build step or pushing it straight into a container image will typically break or exceed size thresholds.

* **Leveraging Google's Public Cache (`vertex-model-garden-public-us`):** Google hosts public copies of model weights inside specialized regional GCS buckets. If your Cloud Run service runs in a `us-` region, the snippet intelligently skips downloading entirely (`exit 0`), allowing your app to stream weights straight from Google’s ultra-fast internal network.

* **Parallel Upload Optimization:** If you are using a non-US region or deploying a custom fine-tuned variant, the `gcloud config set storage/...` commands configure parallel composite uploads and sliced downloads. This breaks large model safetensors files into chunks, downloading them concurrently to bypass single-stream bottlenecks.

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### Your Options to Run Gemma 4 on Google Cloud Run

When deploying Gemma 4 (such as the 31B Dense or 26B MoE variants) to Cloud Run, you have two primary deployment architectures:

| Approach | Architecture | Best For |

| --- | --- | --- |

| **1. vLLM Container + GCS Cache (The Snippet Method)** | Packages vLLM inside a custom container image, pulls weights directly from GCS into GPU memory using high-performance streaming. | High throughput, production-grade OpenAI-compatible API serving, and maximum token generation speed. |

| **2. Ollama on Cloud Run** | Deploys an Ollama runtime container to Cloud Run backed by serverless GPUs, pulling model weights dynamically on start. | Lightweight setups, rapid prototyping, and simpler configuration files. |

Both approaches require provisioning **NVIDIA RTX Pro 6000 (Blackwell)** or similar serverless GPUs on Cloud Run to supply enough VRAM to hold the model weights.