Complete Guide to Local AI Deployment: From Hardware Selection to Private Deployment of Inference Engines

Project Source

Original Author/Maintainer: DamienBecherini Source Platform: GitHub Original Title: ia-on-prem-vault Original Link: https://github.com/DamienBecherini/ia-on-prem-vault Update Time: 2026-06-06T07:15:53Z

Core Content Overview

This guide is a comprehensive knowledge base for local AI deployment, covering hardware selection (GPU/CPU/network), inference engine selection (vLLM/TensorRT-LLM, etc.), deployment architecture design (single-node/distributed), operation monitoring, and security compliance. It helps users build private large language model infrastructure to meet data privacy, cost optimization, and customization needs.

The driving forces for local AI deployment include:

1. Data Privacy & Security: Sensitive data (finance/medical/government) does not need to be sent to third-party clouds, avoiding compliance risks;
2. Cost-effectiveness: In high-frequency application scenarios, self-built infrastructure is more economical than cloud API pay-as-you-go;
3. Controllability & Customization: Full control over model configuration, updates, and optimization without restrictions from cloud service providers.

The ia-on-prem-vault project was created as a comprehensive knowledge base to meet these needs.

GPU Architecture & Selection

• VRAM Capacity: A 70B parameter model requires at least 40GB VRAM; super-large models need multi-card configurations;
• Compute Power (TFLOPS): Affects inference speed; stronger computing power is needed for low-latency scenarios;
• Memory Bandwidth: Avoids GPU computing unit idling;
• Multi-card Interconnection: NVLink/InfiniBand supports high-speed VRAM sharing.

CPU & System Configuration

• PCIe Bandwidth: PCIe4.0 x16 as the base; channel allocation needs to be considered for multi-card setups;
• System Memory: 128GB+ recommended, 256GB+ for production environments;
• Storage: NVMe SSD is a basic requirement; memory caching is needed for high-frequency scenarios;
• Cooling & Power Supply: Multi-card systems require 2000W+ power supply and effective cooling.

Network Infrastructure

• InfiniBand vs Ethernet: The former is suitable for distributed training, while the latter with 10Gbps+ meets inference needs;
• RDMA Support: Reduces CPU overhead for cross-node communication.

Mainstream Inference Engine Comparison

• vLLM: Open-source high-throughput engine, PagedAttention improves GPU memory utilization;
• TensorRT-LLM: NVIDIA deep-optimized engine with extreme performance (NVIDIA GPU only);
• llama.cpp: Lightweight C++ implementation supporting multiple quantization formats, suitable for edge devices;
• Ollama: Simplifies model download/operation, suitable for prototyping;
• TGI: Hugging Face Inference Server with friendly ecosystem integration.

Quantization Techniques

• INT8: Small precision loss, memory usage halved;
• INT4/AWQ/GPTQ: Aggressive compression (1/4 of original size), suitable for resource-constrained scenarios;
• Dynamic Quantization: Dynamic conversion during inference, flexible but with computational overhead.

Single Node Deployment

• Single GPU: Runs 7B-13B parameter models, suitable for development and testing;
• Multi-GPU: Connected via NVLink, supports 70B+ parameter models, requires PCIe channel planning and cooling.

Distributed Deployment

• Model Parallelism: Super-large models (100B+ parameters) distributed across multiple GPUs/nodes, high communication overhead;
• Pipeline Parallelism: Model layers allocated to devices, improves throughput but increases latency;
• Tensor Parallelism: Intra-layer parallel computing, suitable for low-latency scenarios.

High Availability Architecture

• Load Balancing: Distributes requests to multiple instances, improves throughput and availability;
• Failover: Standby instances switch automatically to ensure service continuity;
• Auto-scaling: Adjusts instance count based on load to optimize resource usage.

Performance Monitoring

• GPU Utilization: Compute/memory utilization to identify bottlenecks;
• Inference Latency: End-to-end latency to ensure SLA;
• Throughput: Requests per second to evaluate processing capacity;
• Error Rate: Tracks inference errors and timeouts.

Model Management

• Version Control: Model file versioning supports rollback;
• A/B Testing: Gray release of new models to verify performance;
• Caching Strategy: Balances memory usage and loading time.

Security & Compliance

• Access Control: API authentication, network isolation, audit logs;
• Data Protection: TLS encrypted transmission, static encrypted storage, data desensitization.

The ia-on-prem-vault project provides comprehensive knowledge resources for local AI deployment, covering hardware, inference engines, deployment architecture, operation, and security.

• Technical Decision-makers: Can understand the pros and cons of different options and make decisions aligned with organizational needs;
• Technical Implementers: Obtain detailed guides and best practices to avoid common pitfalls.

Local deployment is a feasible solution for data privacy, cost optimization, or deep customization needs. This open-source knowledge base lowers deployment barriers and promotes AI democratization.