Tom’s Hardware explains key AI concepts for local model users
Tom’s Hardware breaks down technical terms for users running AI models locally, focusing on text-based LLMs. The article defines core concepts like weights—the numerical values determining token relationships—and quantization, where high-precision weights are rounded to reduce memory use. It explains how context windows act like RAM for sessions, storing tokens from prompts, replies, and…
Key points
- Quantization reduces weight precision to save VRAM, with frontier models using BF16 and open-weight models offering INT4 or INT8 variants
- KV cache reuses token relationships between queries to avoid recalculating them, critical for long conversations on limited hardware
- MoE splits models into sub-experts for token patterns, activating only relevant parts to balance speed and accuracy
Other topics include KV cache, a reusable data structure that speeds up repeated queries by avoiding repeated calculations, and Mixture-of-Experts (MoE), where sub-models specialize in token patterns rather than topics. The piece also covers inference, split into prefill (parallel processing of input) and decode (sequential token generation), and emerging techniques like speculative decode and multi-token prediction to accelerate responses. While aimed at developers and hardware enthusiasts, the primer avoids jargon-heavy explanations, using analogies like GPS precision to illustrate trade-offs between accuracy and performance.
A primer on key AI LLM concepts — KV cache, weights, quantization, and how inference actually works
Tom's Hardware · 30 October 2026
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This text was published by Tom's Hardware and written by Bruno Ferreira. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
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