Gemma 4 on Android, Cloud Run, and GKE: Where to Deploy and How to Start

Tools

Gemma 4 Brings Local Agentic Coding and On-Device Inference to Android Studio

Gemma 4 E2B and E4B run directly on Android hardware via the AICore Developer Preview, with claims of 4x faster inference and 60% less battery draw than the previous generation. Android Studio's Agent Mode now uses Gemma 4 locally, meaning your coding assistant never leaves your machine.

Gemma 4 on Cloud Run Gets NVIDIA Blackwell GPUs with 96GB vGPU Memory

Cloud Run now lets you serve Gemma-4-31B-it on NVIDIA RTX PRO 6000 Blackwell GPUs serverlessly, scaling to zero when idle so you only pay for actual inference. This is the lowest-friction path to production serving of a 31B open model without managing your own GPU cluster.

Gemma 4 NVFP4 Quantized Checkpoint Lands for NVIDIA Blackwell Developers

NVIDIA is shipping an NVFP4 quantized checkpoint for Gemma-4-31B via NVIDIA Model Optimizer and vLLM, giving Blackwell GPU owners 4-bit precision with near-8-bit accuracy at lower cost per token. If you are running inference on RTX PRO or H100 Blackwell hardware, this is the checkpoint to target first.

Analysis

GKE Inference Gateway Cuts Gemma 4 Time-to-First-Token Latency by Up to 70%

GKE's new Inference Gateway uses predicted-latency-based scheduling via llm-d to route requests based on real-time capacity rather than heuristics, cutting TTFT by up to 70% with no manual tuning. Pair that with the GKE Agent Sandbox for isolated tool-call execution and you have a solid production agentic stack.

Gemma 4 26B MoE Activates Only 3.8B Parameters at Inference for Low-Latency Serving

The 26B MoE model activates just 3.8B parameters per forward pass, which means faster tokens-per-second and lower memory pressure at serving time compared to a dense model of the same benchmark quality. For latency-sensitive agentic workflows, this is the model size to benchmark first before defaulting to 31B.

News

Gemma 4 E2B and E4B Run on Jetson Orin Nano for Physical AI and Robotics

The edge model variants now run on Jetson Orin Nano with multimodal input including audio and vision, making Gemma 4 viable for robotics and industrial automation at the embedded hardware tier. This is the first Gemma generation with native audio input on power-constrained devices.

Gemma 4 Ships Under Apache 2.0: No Custom License, No Restrictions on Commercial Use

Previous Gemma releases used a custom terms-of-service that created ambiguity for enterprise and sovereign deployments. Apache 2.0 removes that friction and makes Gemma 4 the first OSI-approved model in the Gemmaverse, which matters a lot for teams with legal review requirements.