LLMs & Large Language Models

What Are Large Language Models

Large Language Models are transformer-based neural networks with billions of parameters trained on diverse text corpora. They excel at natural language understanding, generation, and reasoning tasks without task-specific training.

Core Capabilities

• Text Generation: Producing coherent, contextually appropriate text
• Question Answering: Extracting or synthesizing information from context
• Code Generation: Writing and explaining code across multiple languages
• Translation: Converting text between languages with nuanced understanding
• Summarization: Condensing long documents while preserving key information
• Reasoning: Multi-step logical inference and problem-solving

How LLMs Work

LLMs use the transformer architecture with self-attention mechanisms to process text tokens in parallel. During training, they learn statistical patterns and relationships in language, enabling them to predict the next token in a sequence. At inference time, this prediction capability enables text generation, completion, and other language tasks.

Model Architectures and Families

Deployment Options

Cloud APIs

Hosted services from providers like OpenAI, Anthropic, and Google. Trade-offs include convenience and scalability against cost per token, data privacy concerns, and vendor lock-in.

Self-Hosted (On-Premise)

Running models on your own infrastructure provides data sovereignty and cost predictability. Requires hardware investment and technical expertise. See our LLM On-Premise guide for detailed information on local deployment.

Hybrid Approaches

Combining cloud APIs for occasional high-complexity tasks with local models for routine operations. Balances cost, performance, and data sensitivity requirements.

Open-Source LLM Landscape

The open-source LLM ecosystem provides alternatives to proprietary models with varying licenses and capabilities.

Leading Open Models

• Llama 2 & 3 (Meta): High-quality foundation models from 7B to 70B+ parameters
• Mistral & Mixtral: Efficient models using mixture-of-experts architecture
• Qwen (Alibaba): Strong multilingual capabilities, various sizes
• Gemma (Google): Lightweight models optimized for efficiency
• Phi (Microsoft): Small but capable models (1B-3B parameters)

Licensing Considerations

Open-source LLMs use various licenses (Apache 2.0, custom commercial licenses). Review terms carefully for commercial use, especially regarding training data and derivative works.

Implementation Considerations

Hardware Requirements

LLM inference requires significant compute resources. A 7B parameter model needs ~14GB VRAM at FP16 precision, or ~7GB with 8-bit quantization. Larger models scale accordingly. See our Hardware guide for specific recommendations.

Performance vs. Cost Trade-offs

Larger models generally perform better but cost more to run. Consider your specific use case: smaller models often suffice for focused tasks, while complex reasoning may require larger parameter counts.

Prompt Engineering

Effective LLM use requires carefully crafted prompts. Techniques include few-shot examples, chain-of-thought reasoning, and structured output formatting. Quality prompts significantly impact output quality and consistency.

Fine-Tuning and Adaptation

Models can be adapted to specific domains through fine-tuning, LoRA (Low-Rank Adaptation), or RAG (Retrieval-Augmented Generation). Each approach has different resource requirements and use cases.

Resources and Further Reading

On AI-Radar

• Latest LLM news and updates
• LLM On-Premise deployment guide
• Frameworks for LLM development
• Hardware for LLM inference

Key Topics