AI Inference: The Complete Guide
A language model with roughly 1–15B parameters, small enough to run on a laptop, phone or single GPU while handling narrow tasks well. A small language model trades broad world knowledge for low latency, low cost and the ability to run privately on local hardware. This guide pulls together everything on Onexial tagged ai inference — 19 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind AI Inference
Before wiring anything together, the vocabulary has to be precise. These 8 definitions cover the terms that show up in almost every AI Inference discussion — each one links to a full entry with an example and its own connections inside the graph.
Small Language Model (SLM)
A language model with roughly 1–15B parameters, small enough to run on a laptop, phone or single GPU while handling narrow tasks well.
Local LLM
A language model run on your own machine or server instead of a cloud API, so prompts and data never leave your infrastructure.
Reasoning Model
An LLM trained to spend extra compute thinking through a problem internally before answering, improving accuracy on math, code and planning.
Prompt Caching
Reusing the processed form of a repeated prompt prefix so later requests are cheaper and faster.
Semantic Cache
A cache that returns a stored LLM answer when a new question means the same thing as an earlier one, even if worded differently.
LLM Gateway
A proxy between your app and model providers that centralises keys, routing, fallbacks, logging and spend limits.
Knowledge Distillation
Training a small model to imitate the outputs of a large one, keeping most of the quality on a target task at a fraction of the cost.
AI Red Teaming
Deliberately attacking an AI system — jailbreaks, prompt injection, data leaks — to find failures before real users do.
Workflows: how AI Inference runs end to end
Concepts only matter once they become a repeatable process. Below are 4 documented workflows that apply AI Inference to a concrete problem, with the steps, the tools involved and the variations worth testing.
Run an LLM Locally
Set up a private, offline language model on your own computer in under an hour.
Cut LLM Inference Costs
A step-by-step method to reduce model spend without degrading output quality.
Route Requests Between Models
Send each request to the cheapest model that can handle it, escalating only when needed.
Red Team an AI App
A repeatable process for attacking your own AI product before launch.
The AI Inference tool stack
A stack is a set of tools chosen for one job, not a list of favourites. These 2 stacks show which combinations hold up in production for AI Inference, and what each layer is actually responsible for.
Local AI Stack
Open-source tools to run chat, retrieval and agents entirely on your own hardware.
LLM Cost Optimization Stack
The layers that keep AI spend predictable as usage grows.
Trade-offs and comparisons
Most AI Inference decisions are trade-offs rather than right answers. These 2 comparisons break down the real differences, when each option wins, and the recommendation for the common case.
Local LLM vs Cloud API
Local models win on privacy and fixed cost; cloud APIs win on quality, scale and zero maintenance.
Reasoning Model vs Standard LLM
Reasoning models are more accurate on multi-step problems but slower and pricier; standard calls suit simple, fast tasks.
Prompts you can reuse
Prompts are reusable components. Each of these 2 prompts is written to be dropped into a AI Inference workflow with minimal editing, including the context it expects and an example output.
Model Routing Classifier Prompt
A cheap-model prompt that labels request difficulty so it can be routed to the right model.
Red Team Attack Plan Prompt
Generates a tailored list of adversarial tests for your own AI feature.
Real applications of AI Inference
Finally, 1 applied use cases: the situation, the system used to solve it, and the outcome. This is the layer that turns AI Inference from an idea into leverage.
Frequently asked questions
- What is Small Language Model (SLM)?
- A small language model trades broad world knowledge for low latency, low cost and the ability to run privately on local hardware. SLMs are strongest on bounded tasks — classification, extraction, routing, short summaries — especially after fine-tuning on domain data. They are weaker at long multi-step reasoning and open-ended knowledge questions.
- What is an example of Small Language Model (SLM)?
- A support team runs a 3B model on-premise to tag every incoming ticket by intent and urgency, and only sends the hard 10% to a frontier model.
- Why does Small Language Model (SLM) matter?
- A language model with roughly 1–15B parameters, small enough to run on a laptop, phone or single GPU while handling narrow tasks well.
- What should I learn after Small Language Model (SLM)?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
- What is Local LLM?
- Running an LLM locally means downloading open weights and serving them with a runtime such as Ollama, llama.cpp or vLLM. The benefits are data privacy, no per-token fees and offline use; the costs are hardware, maintenance and lower quality than the best hosted models. Quantized weights make 7–14B models usable on consumer GPUs and Apple Silicon.
- What is an example of Local LLM?
- A law firm summarises confidential contracts with a quantized 14B model on a workstation, so client documents are never sent to a third party.
- Why does Local LLM matter?
- A language model run on your own machine or server instead of a cloud API, so prompts and data never leave your infrastructure.
- What should I learn after Local LLM?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.