Private LLM Deployment Strategies for Enterprises Using AWS, Azure, and On-Premise Solutions

Do you want to know the LLM deployment strategies of AWS, Azure, or On-Premise solutions? Or are you confused about whether to choose cloud or local LLM deployment? No need to worry. Here we have prepared a detailed blog that covers all aspects of LLM deployment. Let’s explore.

Before we move on to exploring strategies, we first need to understand why businesses are moving to Private LLMs.

What is a Private LLM & Why are Businesses Choosing it?

Private LLMs are large language models that operate entirely under your organisation’s environmental control. The infrastructure is not shared, your inputs are not processed by third-party models, and there’s no ambiguity in data handling.

In Public LLMs, your data leaves the environment, but that’s not the case in Private LLMs. These LLMs ensure that your data does not cross an explicitly defined boundary and lives in your data centre, cloud account owned by you, or your VPC.

Reasons Businesses Choose Private LLM

• Data Security: Complete control over data which is stored in your organisation’s infrastructure.
• Compliance: Full adherence is ensured as it can be tailored according to industry standards.
• Customisation: Based on business needs and data requirements, you get complete customisation options.
• Performance Control: With dedicated resources, you get a more reliable and predictable performance.

In this competitive era, where private LLMs are gaining their significance, it is necessary to go for LLM deployment strategies that align with your enterprise needs. Two of such are Cloud Deployment and On-Premise Deployment.

In this blog, we will compare cloud deployments like AWS-Based Private LLM Deployment, Azure-Based Private LLM Deployment, and On-Premises Infrastructure Deployment.

Enterprise Deployment Models: A Brief Comparison

Let’s understand some key differentiations between all those deployment models mentioned earlier.

AWS Cloud

• Best For: Scalable AI Workloads
• Advantage: Flexibility & Ecosystem
• Challenge: Cost Optimisation

Azure Cloud

• Best For: Microsoft-Centric Enterprises
• Advantage: Seamless Enterprise Integration
• Challenge: Vendor Dependency

On-Premise

• Best For: Highly Regulated Industries
• Advantage: Maximum Data Control
• Challenge: Industry Complexity

Cloud-Based Private LLM Deployment Strategies

Let’s first explore Cloud-Based Private LLM Deployment strategies, which will be further classified into AWS and Azure strategies.

Why Choose Cloud LLM Deployment?

• No need for hardware investment upfront, which helps you skip a major capital investment and go for a pay-as-you-use model.
• Ability to scale endlessly and automatically during busy times and slow times.
• Quickly launch products and go-to-market using trained models, along with pre-built APIs.
• Access to the latest and greatest models at any time, as well as services that continuously improve.

Cloud LLM deployment is an ideal choice for enterprises looking for rapid AI adoption and agility.

Deploying Private LLMs using AWS

AWS provides various ways to host Private LLMs, depending on just how much You’d like to control versus how much control AWS will have.

Amazon Bedrock

This is likely the easiest path for most organisations. It provides you access to architectural models (e.g., Anthropic Claude, Llama, Titan) within your AWS account without needing to train the actual models on your training data and with a level of data isolation.

Most enterprise security considerations relate to the architectural model, so it’s a natural mapping to both security and privacy for enterprise-level security considerations.

Amazon SageMaker

If you’d like to have more control, then SageMaker provides that (at the expense of convenience). With SageMaker, you control the end-to-end inference workflow, can host your model, and perform any fine-tuning you require.

This option is ideal if you need custom inference logic for your LLM or if Bedrock does not provide enough choice in terms of available models. If you understand AWS Lambda, you should find integrating this option into your total architecture fairly easy, enabling you to create pipelines.

EC2 with GPU Instances

The EC2 instance is the highest level in terms of operational overhead and offers the most freedom. You manage the configuration of your architecture and your serving stack.

EKS (Elastic Kubernetes Service)

EKS will manage aspects of auto-scaling, orchestration, and rollouts below most of the serious AWS LLM deployments.

Deployment Guide

• LLM selection: Choose a private LLM based on business requirements and compliance standards.
• Model Training/Fine-tuning: Tailor the LLM using enterprise data in a secure environment to maximise its effectiveness.
• Deployment: Deploy the LLM in a private AWS Virtual Private Cloud to guarantee that the model is confined to run in an isolated setting within the organisation.
• Monitoring: Monitor and measure system latency, speed, and other performance metrics.
• Scaling Infrastructure: Employ Kubernetes or Amazon EKS for infrastructure scaling.

Deploying Private LLMs on Azure

Now, another cloud platform, where you can reliably deploy your private LLMs it also provides different services matching your requirements and needs.

• Azure OpenAI Service: In this service, you get GPT-4 along with various other OpenAI models in your Azure subscription. It has a private endpoint configuration that keeps your traffic off the public internet. Here they also follow Microsoft’s data and privacy commitments, under which they don’t use your prompts to train models, and you have control over content filtering and access policies.
• Azure Machine Learning: It is Azure’s equivalent of Amazon SageMaker. You get a model registry, a full MLOps pipeline, experiment tracking, and managed endpoints.
• AKS (Azure Kubernetes Service): If your enterprise is running on Microsoft’s identity infrastructure, Azure Kubernetes Service is something you need. It manages orchestration with close integration into Azure AD for controlling access.

Deployment Guide

• Incorporate Enterprise Data: From document files to database servers and all business-related information should be linked together for AI model processing purposes.
• Private Endpoint Configuration: A secure and restricted connection should be made through configuring private AI endpoints for enhanced data protection and restricted access.
• Model Customisation: Enhance models’ contextual knowledge and business relevance based on enterprise data.
• Deploy Models on Microsoft Azure AI Infrastructure: Deploy tailored models onto Azure’s AI Infrastructure using Microsoft Cloud Computing Services.
• Governance Monitoring: In order to create effective AI governance, AI governance will need continuous Model Performance Monitoring and Compliance Monitoring.

On-Premise LLM Deployment Strategies

Now, we are on another form of private LLM deployment, and that is on-premises LLM deployment.

Why Choose On-Premise LLM Deployment?

• All of your data remains in your private network and is not shared or processed by third-party cloud providers
• Your organisation does not have a risk of data exfiltration through multi-turn cloud prompt chaining.
• On-premise setup complies strictly with regulations like HIPAA, GDPR, and CCPA, preventing cross-border data transfers and unauthorised access.
• Complete control over model weights that allows you to individually adapt smaller, specialised open-source models for distinct industry-specific processes, without depending on general-purpose cloud solutions.

Deploying Private LLMs on-Premises

Let’s explore.

GPU Clusters and Hardware Specs for LLMs

The first step in implementing on-premises is to decide on your hardware needs:

• 7 Billion Parameter Models: One NVIDIA A100 will support both development and moderate production needs.
• 70 Billion Parameter Models: Require multi-GPU configuration with 8 GPUs/server, typically A100 or H100.
• Storage: NVMe SSD to hold model parameters; the 70B model will require approximately 140GB of model weights (FP16).
• Network: InfiniBand or 100GbE for node-to-node communication
• CPUs/RAM: Should have enough capacity to prevent preprocessing from being limited in the future.

Storage, Networking and High-Throughput Data Flow.

To support simultaneous read requests from multiple inference workers, rapid model weight loading on cold start, and high-volume vector retrieval, the storage layer must be designed with these three capabilities in mind.

While there may be a latency advantage in an on-premise configuration, you also control the physical proximity of the vector store and inference endpoints and are not subject to many of the unpredictable delays present in a cloud environment due to network latency; thus, you control this factor.

Data Pipeline, like any other latency-sensitive system, must receive rigorous structure in each of these areas; strict management of serialisation overhead, asynchronous processing, and connection pooling are key to creating the absence of latency in your data pipelines.

Kubernetes-Based On-Premise LLM Deployment

Kubernetes acts as the orchestration layer by default because of:

• Allocation of GPU resources to inference pods
• Rolling upgrades with no downtime
• Autoscale based on queue depth
• Pod self-healing and health checks
• Isolation at the namespace level for multiple tenants

Optimisation of Inference and Deployment of Models

When it comes to optimising inference and deployment of models, two major frameworks stand out:

• vLLM: Offers the most flexibility, supports PagedAttention, continuous batching and streaming, as well as all vendors on the hardware side.
• TensorRT-LLM: While it only supports NVIDIA, it is much faster. With kernel fusion and quantisation, you can get a reduction of 30-50% on your latency when using H100 hardware.

When it comes to processing workloads with high volumes and low latency, TensorRT-LLM is generally preferred over vLLM for automating generative AI workflows at scale because of its throughput advantages. However, for anything else, vLLM is typically your best option.

Security & Access Control of On-Prem AI Systems

Total control requires intentionality. To achieve this, the baseline parameters include:

• mTLS between all internal services
• Role-Based Access Control over inference endpoints
• Secrets management for model weights/credentials
• Immutable audit logs for each inference call
• Network segmentation of your GPU cluster

The prompt-accepting endpoint is a point of attack and should be treated as such.

Monitoring / Logging / Performance Management

To effectively monitor your system, you should be able to track:

• GPU utilization by each node
• Latency for p50/95/99 on inference
• Queue depth
• Memory usage
• Percentage of errors

Data Sovereignty & Full Control Benefits

As briefly discussed earlier, we know that with on-premises infrastructure, your data is not shared with third-party clouds. For industries and services like finance, healthcare, and defence, this is a necessity.

Final Thoughts: Whom to Choose?

It is very simple to determine whom to choose.

• If you are an organization that’s working on AI and looking for scalable workload handling, then you can go for the AWS cloud platform.
• However, if you are a Microsoft-centric enterprise, then Azure is an ideal option.
• And if you are an organisation serving regulated industries like healthcare, finance, defence, and more, then on-premise LLM development is what you should choose.

For reliable LLM development, you need to partner with LLM experts who understand its core.