A Primer on Essential AI LLM Concepts and Terminology
As consumer hardware for running local AI models moves to the forefront, a new set of acronyms and technical definitions continues to emerge across the industry.
Radwan AbdulhadiEditor and technology writer
Understanding AI Models and Weights
As hardware for running local AI models continues to move to the forefront, such as Nvidia's RTX Spark, a new set of acronyms, definitions, and terminology is popping up. For those looking to familiarize themselves with these concepts, a detailed <a href="https://www.tomshardware.com/tech-industry/artificial-intelligence/a-primer-on-key-ai-llm-concepts-kv-cache-weights-quantization-and-how-inference-actually-works">Tom's Hardware</a> guide breaks down the nomenclature. Further details are available from Tom's Hardware in the original source material.
A model is essentially the Large Language Model (LLM) itself, computed from training sets of mostly text data on the scale of terabytes to petabytes. This training information is gathered from the internet, books, video and audio transcripts, and other varied sources.
The model acts roughly as a set of highly advanced layers of statistical analysis regarding the relationships between tokens. Tokens can be words, parts of words, computer code, punctuation, or other symbols. While the tokens are not part of the model itself, they are packaged along with it and generated in its outputs.
The computing layers within an AI model require corresponding weights to function. Weights represent how strong the connection is between sections of tokens, functioning similarly to a heavily traveled pathway between neurons. These values establish how tokens relate to each other when the model runs, and they cannot change without retraining the model or parts of it.
Parameters and Open-Weight Models
When reviewing LLM specifications online, users frequently encounter designations like "4B", "8B+", or "20B". These figures indicate how many parameters, or weights, the model utilizes. Consumer-grade models are generally measured in billions, while frontier models like GPT, Claude, and Kimi scale into the trillions.
Having a higher number of weights does not automatically make a model smarter. Much like CPU clock speeds, achieving a higher number is not the sole objective; those weights must remain meaningful for the output.
The industry also distinguishes between open-weight models and closed models. Open-weight models, such as Llama, DeepSeek, and Kimi, are effectively free-to-use because their creators publish the weights online. Users can tweak these weights to build custom iterations, provided they possess adequate hardware. However, downloading a free model does not reveal its training methodology or how the weights were calculated.
Quantization and Data Precision
All software programs utilize numbers of varying width and precision depending on their purpose. For instance, a basic money counter may only require a 32-bit integer, whereas precise scientific measurements demand 64-bit floating-point arithmetic.
Because memory is a strictly finite resource, developers aim to use the smallest units possible, a factor of paramount importance in the VRAM-limited environment of GPUs. AI training labs typically apply high precision for weights during training, but maintaining that high precision in a final release yields a prohibitively large and slow LLM.
Quantization solves this challenge by serving as a clever rounding process. After training, developers pass through the data multiple times to transform highly precise weights, utilizing proprietary techniques unique to each AI lab. Frontier models predominantly use relatively high precision, such as BF16, to preserve response quality, nuance, and reasoning.
Conversely, open-weight models often feature multiple versions with varying quantization levels to suit specific hardware capabilities. For example, Llama 3.3 70B can run in full BF16 or quantized INT4 formats. Lower precision allows models to fit on consumer hardware like Mac Studios or graphics cards by trading response accuracy for reduced file size.
Context Windows and Session Limits
The context window represents another heavily discussed metric, functioning as an indicator of session length before model performance begins to degrade. Measured in tokens—with one million being common for frontier models—the context window is nearly analogous to computer RAM.
This thinking space must contain everything associated with an active session, including the current question, previous questions, replies, system-level prompts, uploaded files, and the content of looked-up links. To the LLM, all these distinct concepts are reduced to a single large pile of tokens within the context window.
Because LLMs operate by running a statistical program over all accumulated tokens to predict subsequent outputs, larger context windows prove beneficial despite requiring additional VRAM. Nevertheless, algorithms retain limitations; expanding the window too far can trigger the "lost in the middle" effect, where the model attends closely to the beginning and end of a conversation while forgetting details situated in the middle.
Sources
- Tom's HardwareA primer on key AI LLM concepts — KV cache, weights, quantization, and how inference actually works
Continue chronologically
Related entity coverage
Related stories
Mistral Large 4 Trails Some Chinese Open Models in Tests
Independent benchmarking ranks Mistral's new trillion-parameter Large 4 model as a top non-US and non-China AI model, though it trails certain Chinese open alternatives.
FairStress Benchmark Measures LLM Demographic Fairness
Discover FairStress, a new benchmark designed to evaluate demographic fairness and hidden asymmetries in large language model decision-making.
Tom's Hardware Premium Details TP-Link and AI Hardware
Explore the latest insights from Tom's Hardware Premium, covering TP-Link's regulatory hurdles, FADU's new SSD architecture, and AI updates.
AI-Generated Taipei GTA Clone Hits 1.2M Players in 3 Days
A new browser-based crime game set on the streets of Taipei has captured widespread attention, pulling in millions of players shortly after its release.
