Observed Signal · Apr 4, 2026 · Technical Release · Source: DEV Community · Impact: 4/5 · Sentiment: Positive
Deploy Gemma 4 on Cloud Run with Scale-to-Zero
This technical guide explains how to deploy Google's Gemma 4 family on Google Cloud Run using vLLM and the Run:ai Model Streamer to enable scalable, cost-efficient, self‑hosted inference. It describes Gemma 4's four model variants (E2B, E4B, 26B A4B MoE, 31B), multimodal inputs, improved reasoning and function-calling, and the tradeoffs between downloading weights from HuggingFace versus streaming from Google Cloud Storage (GCS). The author documents VPC setup (Private Google Access), GPU quota checks, model upload steps, and full gcloud deploy commands for each model size. Measured cold-start and warm-response timings are reported for multiple configurations, showing that GCS + VPC streaming with the Run:ai streamer and vLLM yields substantially faster cold starts for large models and that Cloud Run's scale-to-zero can eliminate idle GPU cost for development/testing.
Gemma 4 is a major open-model release from Google and the guide documents a practical, production-oriented self-hosted deployment pattern (vLLM + Run:ai streamer + Cloud Run) that materially affects cost, privacy, and edge/on‑premise inference options—important for teams building AI-enabled products and martech stacks.
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Key Takeaways & Evidence Grounding
- Gemma 4 is released as four models: E2B (2.3B effective), E4B (4.5B effective), 26B A4B (26B on disk, 4B active, MoE), and 31B (31B dense).
- The deployment stack recommended uses vLLM for inference and the Run:ai Model Streamer to stream weights from Google Cloud Storage to reduce cold-start impact for large models.
- Private Google Access on the Cloud Run VPC subnet is required for fast GCS streaming; without it GCS streaming can be slower than downloading from HuggingFace for small models.
- Measured single-run cold start times (examples): 2B from HuggingFace 311s, 4B GCS+VPC 246s, 26B GCS+VPC 191s, 31B GCS+VPC 251s; warm responses ranged ~1.6s (26B) to 5.9s (31B).
- Cloud Run's scale-to-zero means no GPU/instance cost when idle; the primary tradeoff is cold-start latency, which the guide optimizes for testing deployments.
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Running Google's Gemma 4 Locally on a Laptop
A developer-published how-to explains how to download and run Google's Gemma 4 models locally on a consumer laptop using the Ollama tool. The author describes model size tiers (E2B ~2GB, E4B ~4GB, 31B ~20GB), shows a simple three-step flow (install Ollama, run a model with a terminal command, then chat), and demonstrates a Windows setup with 8 GB RAM and an Nvidia GPU (4 GB VRAM). The post contrasts local inference (no internet, no API key, lower cost) with using hosted APIs for production and highlights offline use cases—e.g., deploying small models in low-connectivity communities. It also names OpenRouter as an easy API option for apps that need cloud-based Gemma access.
Serving Google's Gemma 4 on AWS G5g with Pure JAX
A technical deployment guide demonstrating how to serve Google's open model Gemma 4 (google/gemma-4-E2B-it) on an AWS EC2 G5g instance (Graviton2 + NVIDIA T4G) using a pure JAX stack. The article includes a reproducible repo, prerequisites, automated install and verification steps, performance measurements (approximately 13 tokens/sec decode on the T4G with pure JAX versus 43 tok/s for a patched vLLM), and operational notes on AMI selection, Secrets Manager use, SSM Run Command administration, and S3-based XLA cache. The author emphasizes the 117-second reproducible deployment time and trade-offs between ease-of-deploy and raw throughput.
Google Releases Gemma 4 Open-Weight Multimodal LLMs
Google released Gemma 4 — a family of open-weight, multimodal LLMs — in April 2026 and published the model weights under the permissive Apache 2.0 license. The family includes four variants (E2B, E4B, 26B MoE, 31B) designed to run offline across phones, laptops and desktops; the smaller edge models support a 128,000-token context window while the larger 26B/31B variants support 256,000 tokens. Gemma 4 adds features for function calling, agent-like workflows, multimodal vision/audio inputs and a "Thinking Mode" for chain-of-reasoning style outputs. The release emphasizes local, cost-free inference (no per-call cloud billing) and data sovereignty for developers; common local runtimes and GUIs (Ollama, LM Studio and others) make deployment straightforward. Architectural innovations reported with the family (e.g., scaling optimizations for long contexts) aim to enable practical on-device inference and broad commercial use without runtime fees.
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