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The AI computer field guide · October 16, 2026

Your next computer.
Your team’s
next brain.

One great desk. A smarter team. Or a whole company thinking together. Here’s the hardware behind each—and what it can actually do.

Find your setup Start with the essentials
05 complete builds01 practical starting point0 paid links active
THE CONNECTED WORKSPACEFIG. 01
A desk connected to a shared AI serverConceptual illustration of a widescreen computer, local AI computer, and a shared network server. Not a photograph of specific hardware. YOUR WORK, WITH CONTEXT. 3 SOURCES ONE SHARED BRAIN.MANY PLACES TO WORK.
LOCAL COMPUTE / SHARED KNOWLEDGEConceptual illustration

01 Buy for the work.

02 Memory sets the ceiling.

03 A team hub is a system.

THE FIVE-MINUTE FOUNDATION

Start with the job.
Then buy the machine.

A bigger computer isn’t automatically a better agent. Choose where the model runs, how much information it needs, and how many people will ask at once.

Cloud agents

The model runs at a provider. Your computer handles the browser, apps, files, and local tools. A good everyday computer is enough for many serious workflows.

LESS HARDWARE · ONGOING SERVICE COST

Local models

The model’s weights and inference run on your machine. Memory, bandwidth, context length, and software compatibility matter more than a “powerful computer” label.

MORE CONTROL · MORE RESPONSIBILITY

A shared AI hub

People connect through a browser or business application. The hub combines a model server, permission-aware document retrieval, authentication, and reliable operations.

ONE SERVICE · MULTIPLE USERS

CHOOSE YOUR STARTING POINT

Five ways to
put AI to work.

Complete desk setups at the personal tiers. Shared infrastructure at the business tiers. Every build has a purpose—and a ceiling.

Select a build to explore

Loading the five verified build sheets…

U.S. planning budgets before tax, shipping, installation, and recurring costs unless listed. Exact configurations and quotes control the purchase. Hardware illustrations are conceptual.

THE SIDE-BY-SIDE VIEW

The right scale.
At a glance.

Model capacity is not the same as response speed. These are purchasing targets, not measured throughput promises.

Compare the five computer and shared hub setups

OUR PRACTICAL STARTING POINT

Most people should start
with the balanced desk.

Around $5,000 buys a capable daily workspace with meaningful room for local AI. Move up when a specific model, workload, or team need justifies it.

Explore the balanced build

FROM A COMPUTER TO A COMPANY RESOURCE

A second brain.
With a front door.

A shared model is only one part of a useful business hub. The system must know who is asking, which documents they may see, and when a human needs to decide.

CLICK ANY PART TO SEE ITS JOB

Conceptual architecture. The complete service needs implementation and testing.

01 / 05

A familiar place to ask.

Staff use a browser or an approved application. Their laptops don’t need enough memory to run the model; the shared hub does the inference.

Start with a clear use case, such as finding an approved procedure or drafting from a shared knowledge base. Give people a way to inspect sources and flag a bad answer.

A shared answer should never bypass a document’s permissions.

Apply access controls during retrieval, preserve them as files change, and test with employees who have different roles.

Retrieval is not training.

A knowledge hub usually searches approved documents and supplies the relevant passages to the model. That is retrieval-augmented generation (RAG). Adding a PDF does not retrain the model or guarantee a correct answer. Require source links and check important outputs.

RIGHT-SIZE THE SHARED SERVICE

Count the requests.
Not just the people.

Twenty staff members may ask one question at a time—or all at once. Longer documents and agent loops use more capacity. This planning aid changes the guidance, not a fabricated performance score.

4
1 request24 requests

No throughput benchmarks are implied. Test the exact model, quantization, context, runtime, and target response time before purchasing for production.

FROM EXPLORING TO ORDERING

A clear list.
A considered purchase.

Download the specs, confirm the configuration with the seller, and budget for the work that turns a machine into a useful system.

01

Use the actual build sheet.

Each sheet includes components, cost allowances, model limits, vendor links, and the setup work to plan for. The CSV is an editable shopping list.

Download all five specs
02

Get the configuration confirmed.

Memory and GPU choices change what a system can run. Request a written quote for the exact configuration. For shared hubs, ask for a workload demonstration and support terms.

Open the recommended build
03

Plan beyond the box.

Include authentication, document permissions, backups, monitoring, software maintenance, and a named operator. For enterprise hardware, confirm power, cooling, noise, rack space, and network needs before ordering.

Review the hub architecture
Sources, price checks & methodology

Source links support product specifications, model licensing and sizes, serving software, and buying guidance. Planning allowances are estimates. A model fitting in memory does not establish useful performance.

Model sizes depend on the specific quantization. Additional memory is required for the runtime, operating system, and context cache. Model limits and license terms must be checked for the exact version you deploy.