Deploy GPU workloads in Autopilot

Learn how to request hardware accelerators (GPUs) in your GKE Autopilot workloads.

Serve a LLM with multiple GPUs in GKE

Learn how to serve Llama 2 70B or Falcon 40B using multiple NVIDIA L4 GPUs with GKE.

Serve an LLM using multi-host TPUs on GKE with Saxml

Learn how to serve Falcon 7b, Llama2 7b, Falcon 40b, or Llama2 70b using the Ray framework in GKE.

Getting started with Ray on GKE

Learn how to easily start using Ray on GKE by running a workload on a Ray cluster.

Serve an LLM on L4 GPUs with Ray

Learn how to serve Falcon 7b, Llama2 7b, Falcon 40b, or Llama2 70b using the Ray framework in GKE.

Orchestrate TPU Multislice workloads using JobSet and Kueue

Learn how to orchestrate a Jax workload on multiple TPU slices on GKE by using JobSet and Kueue.

Monitoring GPU workloads on GKE with NVIDIA Data Center GPU Manager (DCGM)

Learn how to observe GPU workloads on GKE with NVIDIA Data Center GPU Manager (DCGM).

Quickstart: Train a model with GPUs on GKE Standard clusters

This quickstart shows you how to deploy a training model with GPUs in GKE and store the predictions in Cloud Storage.

Running large-scale machine learning on GKE

This video shows how GKE helps solve common challenges of training large AI models at scale, and the best practices for training and serving large-scale machine learning models on GKE.

TensorFlow on GKE Autopilot with GPU acceleration

This blog post is a step-by-step guide to the creation, execution, and teardown of a Tensorflow-enabled Jupiter notebook.

Implement a Job queuing system with quota sharing between namespaces on GKE

This tutorial uses Kueue to show you how to implement a Job queueing system, and configure workload resource and quota sharing between different namespaces on GKE.

Advanced scheduling for AI/ML with Ray and Kueue

Orchestrate Ray applications in GKE with KubeRay and Kueue.

How to secure Ray on Google Kubernetes Engine

Apply security insights and hardening techniques for training AI/ML workloads using Ray on GKE.

Design storage for AI and ML workloads in Google Cloud

Select the best combination of storage options for AI and ML workloads on Google Cloud.

Automatic driver installation simplifies using NVIDIA GPUs in GKE

Automatically install Nvidia GPU drivers in GKE.

Accelerate your generative AI journey with NVIDIA NeMo framework on GKEE

Train generative AI models using GKE and NVIDIA NeMo framework.

Why GKE for your Ray AI workloads?

Improve scalability, cost-efficiency, fault tolerance, isolation, and portability by using GKE for Ray workloads.

Running AI on fully managed GKE, now with new compute options, pricing and resource reservations

Gain improved GPU support, performance, and lower pricing for AI/ML workloads with GKE Autopilot.

How SEEN scaled output 89x and reduced GPU costs by 66% using GKE

Startup scales personalized video output with GKE.

How Let's Enhance uses NVIDIA AI and GKE to power AI-based photo editing

Innovative AI for beautifying images using NVIDIA GPUs and GKE.

How GKE's GPU sharing helps scientists' quest for neutrinos

GKE GPU sharing helps scientists' quest for neutrinos.

Time-sharing GPUs in GKE

Learn how GPU time-sharing feature on GKE that lets multiple containers share a single physical GPU, thereby improving its utilizatio.

Multi-instance GPUs in GKE

See how multi-instance GPUs in GKE help you drive better value from your GPU investments.

How Etsy is using GKE to redesign their Machine Learning Platform

Etsy leverages machine learning (ML) to create personalized experiences for our millions of buyers around the world.

How Spotify is unleashing ML Innovation with Ray and GKE

How Ray is transforming ML development at Spotify.

How Ordaōs Bio takes advantage of generative AI on GKE

Ordaōs Bio, one of the leading AI accelerators for biomedical research and discovery, is finding solutions to novel immunotherapies in oncology and chronic inflammatory disease.

GKE from a growing startup powered by ML

How Moloco, a Silicon Valley startup, harnessed the power of GKE and Tensor Flow Enterprise to supercharge its machine learning (ML) infrastructure.

Ray on GKE

Use this Terraform template to run Ray on GKE.

Set up Ray on GKE with TPUs

Use this Terraform sample to deploy Ray on a GKE cluster with a 2x2x1 topology.

ML training workloads on GKE using the JobSet API

Run distributed machine learning training workloads on GKE using the JobSet API.

JAX 'Hello World' on GKE

Run a simple JAX program using NVIDIA GPUs A100-80GB on a GKE cluster.