Observed Signal · Aug 29, 2026 · Technical Release · Source: DEV Community · Impact: 3/5 · Sentiment: Neutral
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.
Practical, reproducible guide showing how to serve a modern open LLM on Arm+CUDA AWS hardware with measurable cost/performance trade-offs; relevant to teams building LLM inference infrastructure but not industry-shifting.
Track Google Signals & Market Shifts in Real-Time
Polaris7 autonomous intelligence agents track regulatory filings, primary sources, executive changes, and deal flow 24/7. Create your free Explorer workspace to monitor these entities.
Key Takeaways & Evidence Grounding
- Published on 2026-08-29.
- Guide shows serving google/gemma-4-E2B-it on an AWS EC2 G5g (Graviton2 + NVIDIA T4G) using pure JAX.
- Default rig uses g5g.2xlarge: 1 GPU, 8 vCPU, 16 GiB RAM (T4G reports 15,360 MiB device memory).
- Cloud-init installs jax[cuda13] and related dependencies; the measured install takes 117 seconds with no compile step.
- Measured decode throughput for pure JAX on T4G: ~13 tok/s (cold 18.77s vs warm 4.35s); vLLM on same silicon reached ~43 tok/s but required lengthy builds and Triton patches.
Connected Companies & Entities
6 Entities mapped“This article provides a step by step deployment guide for serving Google's Gemma 4 on an AWS EC2 G5g instance using pure JAX....”
“G5g instances pair an AWS Graviton2 (64-bit Arm) processor with NVIDIA T4G Tensor Core GPUs....”
“G5g instances pair an AWS Graviton2 (64-bit Arm) processor with NVIDIA T4G Tensor Core GPUs....”
“A Hugging Face token with access to the Gemma weights...”
“the engine is this repo's own Gemma 4 port driven by a JAX generation loop behind an OpenAI-compatible FastAPI server, running under systemd...”
“The code is here: https://github.com/xbill9/gemma4-dev...”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Gemma 4 in Pure JAX: TPU-to-GPU Port Lessons
A developer ported a Gemma 4 E2B checkpoint to a single pure-JAX codebase and ran it on Cloud TPU v5e/v6e and an NVIDIA T4G (on an AWS Graviton2 host). Most model code, compilation cache behavior, and static-shape discipline transferred unchanged, but two hardware-dependent issues surfaced: a fused W4A16 kernel written in Pallas (tiled for TPU VMEM) cannot run on GPUs due to much smaller shared-memory limits, and compute-dtype selection must be detected at runtime (float16 vs bfloat16) to avoid hidden conversion costs. The article documents Gemma 4’s four model irregularities, a KV-ring padding bug that produced silent token loops, measured decode throughput (13.10 tok/s on T4G), and profiling that shows unexpected conversion overhead on a Turing GPU.
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.
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.
Track Real-Time Market Signals & Shifts
Set up custom watchlists to receive automated, evidence-grounded executive digests whenever material signals or shifts occur across your tracked landscape.