Where can I learn the basic LLMs and local LLMs concepts?

2026-03-21 · Source: Machine Learning ML & Generative AI News · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

This content outlines a common challenge for newcomers to Large Language Models (LLMs) and local LLMs: understanding fundamental concepts and terminology. The user specifically seeks resources to learn about terms such as prompt processing, reasoning, quantization, inference, tokens, context, and coherence. They also inquire about specific technical distinctions like MLX 4-bit versus Q4 quants, MLX versus GGUF, PF16 versus BF16 versus Q4, and advanced architectures like Mixture of Experts (MoE) and tools like Semantic Router. The request highlights a need for accessible articles or videos that explain these core and advanced LLM concepts.

Key takeaway

For AI Students and Machine Learning Engineers seeking to grasp foundational and advanced LLM concepts, prioritize resources that clearly explain terms like quantization, inference, and prompt processing. Focus on understanding the practical implications of different data types (e.g., PF16, BF16, Q4) and model formats (MLX, GGUF) for local deployment and performance optimization. Your learning path should include both conceptual overviews and practical guides for tools like Semantic Router.

Key insights

Understanding LLM terminology like quantization, inference, and prompt processing is crucial for new practitioners.

Principles

• Quantization reduces model size.
• Inference is model execution.
• Tokens are text units.

In practice

• Explore MLX for Apple Silicon.
• Investigate GGUF for CPU inference.
• Research MoE for sparse models.

Topics

• Large Language Models
• LLM Quantization
• LLM Inference
• Prompt Engineering
• Mixture-of-Experts

Best for: AI Student, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning ML & Generative AI News.