A
- AI Agent
- A system that completes multi-step tasks autonomously: plans, uses tools, makes decisions, iterates. Unlike a chatbot, an agent acts.
- API
- Application Programming Interface. A way for software to talk to other software. AI models are often accessed via API — you send text, you get text back.
C
- Context window
- How much text a model can "see" at once. Measured in tokens. A 1M context window means the model can process roughly 750,000 English words in one go.
D
- Deep learning
- The core technology behind modern AI. Layered systems loosely modeled on the brain (called neural networks) that learn patterns from large amounts of data.
- Deployment
- Putting a model into production so real users or systems can use it. Not the same as training.
F
- Fine-tuning
- Partial retraining of an open-weight model on a small, specific dataset. Changes how the model behaves (tone, format, terminology) without changing its core capabilities.
- Frontier model
- The latest, most capable AI models. Closed-source, accessed only via API. Examples: Claude, GPT, Gemini, Grok.
G
- GPU
- Graphics Processing Unit. The hardware that runs AI models. Originally designed for video games, now the backbone of AI. Key models: NVIDIA H100, H200, B200; AMD MI300X.
- Grounding
- Connecting a model's answers to real documents or data. The opposite of hallucination.
H
- Hallucination
- When a model generates plausible-sounding but factually incorrect information. RAG and grounding are the primary defenses.
- Hosted dedicated
- A deployment mode where your model runs on private GPUs in a professional data center, managed by a provider. Not shared with other customers.
I
- Inference
- The act of using a trained model: you give it input, it generates output. Every AI interaction is an inference event. In 2026, inference represents the majority of all AI compute.
L
- LLM
- Large Language Model. An AI system trained on massive text datasets that can understand and generate human language. ChatGPT, Claude, Llama, and DeepSeek are all LLMs.
- LoRA
- Low-Rank Adaptation. A technique for fine-tuning models efficiently by training a small add-on instead of retraining the whole model. Fast, cheap, and portable.
M
- MSP
- Managed Service Provider. A company that operates and maintains IT systems for clients. Prime TPS operates AI infrastructure and software as an MSP.
- Multimodal
- A model that can process multiple types of input: text, images, audio, video. Gemini and GPT are multimodal; many open-weight models support vision too.
O
- On-premises (on-prem)
- AI infrastructure and models running on hardware physically located in the client's facility. Data never leaves the building.
- Open-weight
- A model whose trained parameters ("weights") are publicly available for download. Anyone can run it, modify it, and deploy it on their own infrastructure. Examples: Llama, Gemma, DeepSeek, Qwen, Mistral.
P
- Parameters
- The numerical values inside a model that determine its behavior. More parameters generally means more capability — and more hardware required. A 7B model fits on a consumer GPU. A 685B model needs a server cluster.
- Prompt
- The text input you give to a model. Prompt engineering is the practice of crafting inputs to get better outputs.
Q
- QLoRA
- Quantized LoRA. A version of LoRA that uses 4-bit precision to reduce memory requirements by ~4x. Enables fine-tuning large models on a single GPU.
- Quantization
- Reducing the numerical precision of model weights (e.g., from 16-bit to 8-bit or 4-bit). Saves memory and increases speed with minimal quality loss.
R
- RAG
- Retrieval-Augmented Generation. A technique that connects a model to a document library. Before answering, the model retrieves relevant documents and uses them as context. Answers come with sources. The primary defense against hallucination.
- Reasoning
- A model's ability to think through problems step by step before answering. Reasoning models (like Claude's adaptive thinking) spend more compute on hard problems and produce better results.
S
- Serving
- Running a model in production so users and applications can send it requests. Specialized software handles this efficiently at scale.
- Sovereign AI
- AI deployed within a specific jurisdiction, on infrastructure controlled by the organization using it. Data, models, and operations stay inside the legal boundary. The opposite of sending data to a foreign cloud.
T
- Token
- The basic unit of text a model processes. Roughly: 1 token ≈ ¾ of an English word. Models charge by the token.
- Training
- The process of creating a model by feeding it massive amounts of data. Training happens once and produces the base model. Fine-tuning is a lighter version done later.
V
- VRAM
- Video RAM. The memory on a GPU that holds model weights during inference and training. The primary hardware constraint for AI. A 70B parameter model needs ~140GB VRAM at full precision.
W
- Weights
- The millions or billions of numbers a model learns during training — in effect, they are the model. “Open-weight” means these numbers are published, so you can run the model on your own hardware.