Understanding Local AI Infrastructure

Educational resources exploring GPU provisioning, model deployment, and privacy-first AI concepts.

This is an educational demonstration site showcasing local AI infrastructure concepts.

Educational Topics

Understanding GPU Infrastructure

Learn about GPU servers optimized for AI inference and training

Educational overview of how GPU infrastructure is provisioned and maintained for LLM inference. Learn about hardware deployment concepts, monitoring systems, and automatic scaling architectures.

  • Latest-generation GPU accelerators
  • Alternative compute architectures for cost-effective inference
  • Apple Silicon clusters for edge deployment
  • Automatic failover and load balancing concepts
  • Real-time monitoring and alerting systems
  • High-speed interconnect networking

LLM Deployment & Optimization

Deploy and optimize any open-source language model

Learn the complexity of model deployment. Understand quantization, optimization techniques, and how to run models efficiently on hardware with maximum throughput and minimal latency.

  • Llama 3, Mistral, Mixtral, Falcon support
  • Custom fine-tuned model deployment
  • GGUF, GPTQ, AWQ quantization
  • vLLM, TensorRT-LLM optimization
  • Multi-model serving
  • OpenAI-compatible API endpoints

Edge AI Deployment

Run AI models at the edge for ultra-low latency

Explore how AI models can be deployed where data lives. Learn about edge computing concepts for retail, manufacturing, hospitals, and remote facilities. Understand local data processing with sub-10ms latency.

  • Edge compute accelerator deployment
  • Edge-optimized model variants
  • Offline inference capability
  • Fleet management dashboard
  • Over-the-air model updates
  • Edge-to-cloud synchronization

Private Fine-Tuning

Train custom models on your proprietary data

Learn how to fine-tune foundation models on proprietary data without sending it to third parties. Understand the process of training domain-specific models that outperform generic APIs.

  • LoRA and full fine-tuning
  • RLHF and DPO training
  • Multi-GPU distributed training
  • Dataset preparation and cleaning
  • Model evaluation and benchmarking
  • Version control and rollback

RAG & Knowledge Base

Enterprise knowledge management with vector search

Discover how retrieval-augmented generation systems ground LLMs in company knowledge. Learn about ingesting documents, wikis, and databases into searchable vector stores.

  • Weaviate, Qdrant, Milvus support
  • Document ingestion pipelines
  • Semantic search and reranking
  • Multi-modal embedding models
  • Hybrid search (vector + keyword)
  • Real-time knowledge updates

Compliance & Security

HIPAA, GDPR, and SOC 2 compliance support

Understand complex regulatory requirements for AI systems. Learn about HIPAA business associate agreements, GDPR data processing agreements, and compliance frameworks.

  • HIPAA-compliant infrastructure
  • GDPR data residency guarantees
  • SOC 2 Type II audit support
  • Encryption at rest and in transit
  • Audit logging and compliance reports
  • Air-gapped deployment options

Common Deployment Models

On-Premise Deployment

Hardware installed in organization data centers

Advantages:
  • ✓Maximum control
  • ✓Zero network latency
  • ✓Air-gap capable
Ideal for:
Healthcare, finance, government

Colocation Model

Dedicated servers in secure third-party facilities

Advantages:
  • ✓Professional infrastructure
  • ✓Lower upfront costs
  • ✓Managed hardware
Ideal for:
Startups, scale-ups, SaaS companies

Hybrid Architecture

Edge devices + central GPU cluster combination

Advantages:
  • ✓Best of both worlds
  • ✓Scalable
  • ✓Cost-effective
Ideal for:
Retail, manufacturing, IoT

Supported Models

Llama

Best overall performance

7B - 70B
parameters

Mistral / Mixtral

Excellent for code & reasoning

7B - 8x22B
parameters

Falcon

Strong multilingual support

7B - 180B
parameters

GPT-J / GPT-NeoX

Legacy compatibility

6B - 20B
parameters

StarCoder / CodeLlama

Specialized for code

7B - 34B
parameters

Custom Fine-Tuned

Your proprietary models

Any size
parameters

Plus any custom fine-tuned models or proprietary architectures. If it runs on PyTorch or HuggingFace, we can deploy it.

Our Technical Stack

Inference Engines

  • • vLLM (high-throughput serving)
  • • GPU-optimized inference engines
  • • llama.cpp (CPU inference)
  • • Text Generation Inference (TGI)
  • • Ray Serve (distributed inference)

Vector Databases

  • • Weaviate (GraphQL API)
  • • Qdrant (REST API)
  • • Milvus (high-scale)
  • • pgvector (PostgreSQL extension)
  • • Chroma (embedded option)

Training Frameworks

  • • PyTorch + DeepSpeed
  • • HuggingFace Transformers
  • • Axolotl (fine-tuning)
  • • TRL (RLHF training)
  • • Ludwig (declarative ML)

Observability

  • • Prometheus metrics
  • • Grafana dashboards
  • • OpenTelemetry tracing
  • • Custom latency monitoring
  • • GPU utilization tracking

Typical Deployment Journey

1

Requirements Analysis

Initial phase

Understanding use cases, data requirements, and compliance needs.

2

Infrastructure Planning

1-2 days

Designing GPU configurations optimized for specific workloads and budgets.

3

Hardware Deployment

1-2 weeks

How servers are provisioned, racked, and connected in data centers.

4

Model Selection & Optimization

3-5 days

Benchmarking models, applying quantization, and optimizing for hardware.

5

Integration & Testing

2-3 days

How API endpoints are configured and tested with applications.

6

Production & Monitoring

Ongoing

Production deployment concepts with monitoring and support systems.

Explore Local AI Concepts

Learn more about privacy-first AI infrastructure and on-premise deployment strategies.

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