Comprehensive Guide to Running Large Language Models on Google Cloud Platform

Introduction
Understanding LLMs and Cloud Infrastructure
Google Cloud’s LLM Ecosystem
Core GCP Services for LLM Deployment
On-Device LLM Inference
Private LLM Deployment on GCP
High-Performance LLM Serving with GKE
Building LLM Applications on Google Workspace
Best Practices for LLM Operations
Resources and Further Learning

Introduction

Large Language Models (LLMs) have revolutionized artificial intelligence and are now integral to modern application development. However, deploying and managing LLMs at scale presents significant technical challenges. Google Cloud Platform (GCP) offers a comprehensive suite of tools and services specifically designed to address these challenges, from development and training to production deployment and monitoring.

This comprehensive guide explores how to leverage Google Cloud’s infrastructure, services, and best practices to successfully run, deploy, and manage large language models. Whether you’re building a private AI assistant, deploying high-performance inference servers, or integrating LLMs into existing applications, GCP provides the necessary tools and flexibility to meet your requirements.

Understanding LLMs and Cloud Infrastructure

What Are Large Language Models?

Large Language Models are deep neural networks trained on tens of gigabytes of data that can perform a wide range of tasks including text generation, summarization, translation, and question-answering.[5] A language model is fundamentally a machine learning model designed to predict and generate plausible language, with autocomplete being a common example of a simpler language model.[8]

The power of LLMs lies in their ability to understand context, maintain coherence over long passages, and perform complex reasoning tasks. However, this power comes with significant computational requirements, making cloud infrastructure essential for most practical applications.

Why Cloud Infrastructure Matters

Deploying LLMs locally can be limiting due to hardware constraints. Cloud platforms like GCP provide:

Scalable compute resources with CPUs, GPUs, and specialized AI accelerators
Flexible storage solutions for large model files and training data
Managed services that handle infrastructure complexity
Cost optimization through pay-as-you-go pricing models
Enterprise-grade security and compliance features

Google Cloud’s LLM Ecosystem

Google Cloud has developed a comprehensive ecosystem specifically designed for working with large language models. This ecosystem includes both managed services for rapid development and lower-level tools for fine-grained control.

Vertex AI: The Core Platform

Vertex AI serves as Google Cloud’s unified platform for machine learning and generative AI applications. The platform includes several key components:

Vertex AI Agent Builder - Allows developers to rapidly build and deploy chatbots and search applications in minutes, even for early-career developers.[5]
Generative AI Studio - Provides tools for prompt engineering and model experimentation
Model Garden - Offers access to various open-source and proprietary models

Generative AI Document Summarization

One practical application built on Vertex AI is Generative AI Document Summarization, which demonstrates how to build complete LLM pipelines. This solution establishes a pipeline that:

Uses Cloud Vision Optical Character Recognition (OCR) to extract text from uploaded PDF documents in Cloud Storage
Creates summaries from the extracted text using Vertex AI
Stores searchable summaries in BigQuery for later retrieval and analysis[5]

This architecture showcases how multiple GCP services integrate to create powerful document processing workflows.

Core GCP Services for LLM Deployment

Cloud Run: Serverless Container Deployment

Cloud Run enables you to deploy containerized LLM applications without managing underlying infrastructure. Key advantages include:

Auto-scaling from zero to thousands of instances based on demand
Pay-per-request pricing model
No infrastructure management required
Easy integration with other GCP services

Cloud Run is particularly useful for embedding services and API endpoints that process user requests and interact with LLMs.[1]

Pub/Sub: Message-Driven Architecture

Pub/Sub acts as the glue that binds different applications together in an LLM pipeline, allowing you to message queue large embeddings and send data to different destinations.[1] This asynchronous messaging system enables:

Decoupled architecture where services operate independently
Scalable data processing for large document volumes
Reliable message delivery with retry mechanisms
Cost optimization by processing data during off-peak hours

For example, each question/answer thread from an LLM interaction can be piped to BigQuery for training data collection, while simultaneously providing responses to users.[1]

Cloud Storage: Data Management

Cloud Storage provides a scalable, durable solution for storing large model files, training datasets, and processed documents. Integration with Pub/Sub allows you to:

Trigger processing workflows when files are uploaded
Store embeddings and processed data for quick retrieval
Manage large document collections efficiently
Implement data retention policies for compliance

BigQuery: Data Warehousing and Analytics

BigQuery serves as the data warehouse for your LLM operations, enabling you to:

Store and analyze question/answer pairs from user interactions
Create training datasets for model fine-tuning
Run analytics on model performance and user behavior
Export data for further analysis or model improvement

Vertex AI Workbench: Development Environment

Vertex AI Workbench (user-managed) provides a Jupyter notebook environment where you can develop and experiment with LLMs. Key benefits include:

Ubuntu-based compute instances with flexible configuration
Hardware accelerator support including GPUs and specialized CPU architectures
Pre-configured Python environments for machine learning
Integration with other GCP services[3]

You can spin up compute instances with more CPUs and RAM than available locally, significantly accelerating model development and testing.

On-Device LLM Inference

Google AI Edge: LLM Inference API

For applications requiring on-device inference, Google AI Edge provides the LLM Inference API, which lets you run large language models completely on-device.[2] This approach is valuable for:

Privacy-sensitive applications where data shouldn’t leave the device
Offline functionality without requiring cloud connectivity
Reduced latency with no network round-trips
Cost savings by eliminating API calls

Configuration Options

The LLM Inference API provides several configuration options for fine-tuning performance:

Option	Description	Default Value
`modelPath`	Path to the model within the project directory	N/A
`maxTokens`	Maximum tokens (input + output) the model handles	512
`topK`	Number of top tokens to sample from	Configurable
`temperature`	Controls randomness in output (0-1)	Configurable
`randomSeed`	Seed for reproducible results	Configurable
`loraPath`	Path to LoRA fine-tuned model weights	Optional

Model Conversion and LoRA Fine-tuning

After training on a prepared dataset, you obtain an adapter_model.safetensors file containing fine-tuned LoRA (Low-Rank Adaptation) model weights. This file serves as the LoRA checkpoint used in model conversion to TensorFlow Lite format for on-device deployment.[2]

Example configuration in Kotlin:

// Set the configuration options for the LLM Inference task
val options = LlmInferenceOptions.builder()
    .setModelPath('<path to base model>')
    .setMaxTokens(1000)
    .setTopK(40)
    .setTemperature(0.8)
    .setRandomSeed(101)
    .setLoraPath('<path to LoRA model>')
    .build()

// Create an instance of the LLM Inference task
llmInference = LlmInference.createFromOptions(context, options)

Private LLM Deployment on GCP

Building a Private LLM Assistant

For organizations requiring complete data privacy and control, you can deploy private LLM instances on Google Cloud. GPT4All is an ecosystem of open-source chatbots and LLM models that enables building private AI assistants.[3]

Getting Started with GPT4All

The implementation process involves:

Local evaluation using the GPT4All GUI application available for Windows, macOS, and Linux
Model selection from open-source options like GPT-J and Llama
Downloading trained models (typically 3.5GB to 7.5GB, requiring stable internet)
Python client integration for better control and configuration

Infrastructure Setup

To deploy a private LLM on GCP, follow this infrastructure approach:

Create a Vertex AI Workbench instance based on Ubuntu operating system
Select appropriate hardware including:
- CPUs with sufficient cores for parallel processing
- RAM adequate for model loading and inference
- GPU accelerators (NVIDIA T4, L4, or A100) for faster inference
- Specialized CPU instruction set extensions (AVX-512, etc.)
Configure networking for secure access to your LLM instance
Set up storage for model files and data

This approach provides better control and configuration options compared to fully managed services, while maintaining the benefits of cloud infrastructure.[3]

High-Performance LLM Serving with GKE

Introduction to GKE Inference Gateway

For production deployments requiring high performance, scalability, and reliability, Google Kubernetes Engine (GKE) with the new GKE Inference Gateway offers a robust and optimized solution for high-performance LLM serving.[4]

Architecture Benefits

The GKE Inference Gateway approach provides:

Kubernetes-native deployment for industry-standard container orchestration
Automatic load balancing across multiple inference server pods
Dynamic scaling based on request volume
High availability with failover and redundancy
Advanced traffic management and routing policies

Prerequisites and Setup

Before deploying LLMs on GKE, ensure your Google Cloud environment is properly configured:

# Set your project ID
export PROJECT_ID="your-project-id"
gcloud config set project $PROJECT_ID

# Install required tools
gcloud components install kubectl

# Enable necessary APIs
gcloud services enable \
  container.googleapis.com \
  compute.googleapis.com \
  networkservices.googleapis.com \
  monitoring.googleapis.com \
  logging.googleapis.com \
  modelarmor.googleapis.com \
  --project=$PROJECT_ID

Deploying vLLM on GKE

vLLM is a popular high-performance LLM serving framework that integrates seamlessly with GKE. The deployment process includes:

Creating a Kubernetes Secret to securely store your Hugging Face token for model downloads:

# Create Hugging Face Token Secret
kubectl create secret generic hf-secret \
  --from-literal=hf_api_token=$HF_TOKEN \
  --dry-run=client -o yaml | kubectl apply -f -

Defining a Kubernetes Deployment for vLLM server pods with proper resource allocation
Configuring critical metadata including:
- metadata.labels.app - Used by InferencePool to discover pods
- spec.template.spec.containers.resources - GPU allocation matching your node pool (e.g., nvidia.com/gpu: "1" for one L4 GPU)
Routing traffic through the Inference Gateway to distribute requests across pods

Performance Optimization

Key considerations for high-performance LLM serving on GKE:

GPU selection based on your model size and latency requirements (L4 for cost-efficiency, A100 for maximum throughput)
Batch size tuning to maximize GPU utilization
Memory management to prevent out-of-memory errors
Request queuing with appropriate timeout settings
Monitoring and alerting for production reliability

Building LLM Applications on Google Workspace

Integration with Google Workspace

Google Cloud enables developers to build LLM-powered solutions that integrate deeply with Google Workspace applications including Gmail, Google Docs, Google Sheets, and Google Meet.

Model Context Protocol (MCP) for Workspace Development

A Model Context Protocol (MCP) is a standardized open protocol that provides context to LLMs and AI agents so they can return better quality information in multi-turn conversations.[6]

Google Workspace provides an MCP server that gives LLMs access to developer documentation and tools. This enables:

Better code generation for Google Workspace API calls
Accurate documentation references in LLM responses
Multi-turn conversations with maintained context
Improved solution quality through access to latest API information

Using the Workspace Developer Tools

To add the Google Workspace developer tools to Gemini CLI:

gemini extensions install https://github.com/googleworkspace/developer-tools

Then add rules to your GEMINI.md file to instruct the LLM to use these tools:

Always use the `workspace-developer` tools when using Google Workspace APIs.

Use Cases for Workspace Integration

Code generation for calling Google Workspace APIs
Building solutions based on latest developer documentation
Command-line and IDE integration for seamless development
Troubleshooting Google Workspace API issues
Documentation-aware LLM responses

Best Practices for LLM Operations

LLMops: Large Language Model Operations

LLMops (Large Language Model Operations) refers to the data engineering required to make LLMs actually useful on a day-to-day basis.[1] This emerging field encompasses:

Data Pipeline Architecture

A robust LLM application requires a well-designed data pipeline:

Data ingestion through multiple channels (API uploads, Cloud Storage, Pub/Sub messages)
Document processing using services like Unstructured for parsing complex file formats
Chunking and embedding to create vector representations for semantic search
Storage and retrieval using vector databases and BigQuery
Monitoring and feedback collection for continuous improvement

Embedding Service Architecture

A scalable embedding service should:

Receive raw files from user commands or Cloud Storage events
Process through Unstructured service to create Langchain Documents
Chunk documents appropriately (handling 1000s of chunks per large PDF)
Send chunks separately via Pub/Sub for parallel processing
Auto-scale Cloud Run based on demand, scaling to zero when idle

Example Flask application for handling Google Chat integration:

from flask import Flask, request, jsonify
import gchat_help
import logging

app = Flask(__name__)

# Send immediate "Thinking..." message and send chat to PubSub
@app.route('/gchat/<vector_name>/message', methods=['POST'])
def gchat_message(vector_name):
    event = request.get_json()
    logging.info(f'gchat_event: {event}')
    
    if event['type'] == 'MESSAGE':
        gchat_help.send_to_pubsub(event, vector_name=vector_name)
        space_id = event['space']['name']
        user_name = event['message']['sender']['displayName']
        logging.info(f"Received from {space_id}:{user_name}")

Monitoring and Observability

Implement comprehensive monitoring for production LLM systems:

Request latency tracking for performance optimization
Token usage monitoring for cost management
Error rate tracking for reliability
Model performance metrics for quality assurance
Resource utilization to optimize infrastructure costs

Resources and Further Learning

Official Google Cloud Documentation

Generative AI Beginner’s Guide - Introduction to core technologies of generative AI
Introduction to Large Language Models - Foundational concepts and terminology
LLM Inference Guide - Google AI Edge - On-device LLM deployment
Google Workspace LLM Development - Building LLM solutions for Workspace

GCP Services and Tools

Vertex AI Documentation - Comprehensive platform for ML and generative AI
Large Language Models on Google AI - Overview of GCP’s LLM offerings
GKE Inference Gateway Guide - Production-scale LLM serving

Learning Platforms

Google Cloud Skills Boost - Official training courses including LLM and generative AI specializations
YouTube: Introduction to Large Language Models - Video introduction to LLM concepts

Open-Source Tools and Communities

GPT4All - Ecosystem of open-source chatbots and LLM models
vLLM - High-performance LLM serving framework
Langchain - Framework for building LLM applications
Hugging Face Model Hub - Repository of open-source models

Best Practices Guides

Cloud-based LLM Deployment Guide - Step-by-step approach to cloud LLM deployment
Running LLMs on GCP - Detailed implementation patterns and architecture

Conclusion

Google Cloud Platform provides a comprehensive, flexible ecosystem for deploying and managing large language models at any scale. Whether you’re building a private AI assistant using GPT4All on Vertex AI Workbench, deploying high-performance inference servers with GKE, or integrating LLMs into Google Workspace applications, GCP offers the necessary infrastructure, managed services, and tools.

The key to successful LLM deployment on Google Cloud lies in understanding your specific requirements—whether you need maximum privacy with on-device inference, scalability with Kubernetes, or rapid development with managed Vertex AI services. By following the architectures and best practices outlined in this guide, and leveraging GCP’s extensive documentation and learning resources, you can build production-ready LLM applications that are secure, scalable, and cost-effective.

As the field of LLMops continues to evolve, Google Cloud remains committed to providing cutting-edge tools and services that simplify LLM deployment and management, enabling developers and organizations to focus on building innovative AI-powered solutions rather than managing complex infrastructure.

Table of Contents#

Introduction#

Understanding LLMs and Cloud Infrastructure#

What Are Large Language Models?#

Why Cloud Infrastructure Matters#

Google Cloud’s LLM Ecosystem#

Vertex AI: The Core Platform#

Generative AI Document Summarization#

Core GCP Services for LLM Deployment#

Cloud Run: Serverless Container Deployment#

Pub/Sub: Message-Driven Architecture#

Cloud Storage: Data Management#

BigQuery: Data Warehousing and Analytics#

Vertex AI Workbench: Development Environment#

On-Device LLM Inference#

Google AI Edge: LLM Inference API#

Configuration Options#

Model Conversion and LoRA Fine-tuning#

Private LLM Deployment on GCP#

Building a Private LLM Assistant#

Getting Started with GPT4All#

Infrastructure Setup#

High-Performance LLM Serving with GKE#

Introduction to GKE Inference Gateway#

Architecture Benefits#

Prerequisites and Setup#

Deploying vLLM on GKE#

Performance Optimization#

Building LLM Applications on Google Workspace#

Integration with Google Workspace#

Model Context Protocol (MCP) for Workspace Development#

Using the Workspace Developer Tools#

Use Cases for Workspace Integration#

Best Practices for LLM Operations#

LLMops: Large Language Model Operations#

Data Pipeline Architecture#

Embedding Service Architecture#

Monitoring and Observability#

Resources and Further Learning#

Official Google Cloud Documentation#

GCP Services and Tools#

Learning Platforms#

Open-Source Tools and Communities#

Best Practices Guides#

Conclusion#

Table of Contents