AI lab equipment list: how to configure the three tiers

An AI lab equipment list is really a three-row table that has to line up: scenario, course, device. The most common mistake is turning it into a pile of product names. The reviewer's first question is what these devices have to do with our courses, and the second is why they cost that much.

Three configuration tiers

Foundation tier: making the class runnable

The goal is that a class can actually run. The Beijing Commercial School award notice lists its content in detail: an intelligent data training platform, a data annotation and training platform, AI trainer certification suites, desktop workstations, precision air conditioning, AV control, speakers, amplifiers, teaching software and a server-room management system. Note the scope of the word equipment: room infrastructure and AV gear are mandatory line items, not options. Leave any of them out and the review will question whether the budget is complete.

Standard tier: compute and content

This tier answers whether experiments can actually be done. Compute can be staged: inference for teaching can run on cloud capacity or an all-in-one appliance, with local GPU servers deferred to a second phase. Content is harder to defer. In the university project above, virtual simulation software costs RMB 279,500 per suite, and course resources often matter more to teaching quality than hardware at the same price. Listing courseware, experiment cases and teacher training as separate items is easier to defend than burying them in a platform price.

Embodied tier: robots and workstations

Here the unit of procurement shifts from device to workstation: real-machine data-collection workstations, joint-motor assembly workstations and whole-body test workstations. The Beijing technician college notice buys data-collection workstations for humanoid, wheeled and robotic-arm forms at once and requires delivery within 45 days; the Guangxi base was awarded at RMB 2,638,500 to the yuan. Workstation count follows how many students are in the room at the same time, not how impressive the catalogue looks.

Seven fields every line item needs

1. Name and specification, including verifiable interfaces and protocols
2. Use scenario
3. Mapped course or training task
4. Quantity and its relation to workstation count
5. Whether software licences and upgrade years are included
6. Whether installation, commissioning and integration are included
7. Whether training and support-response years are included

Sizing from seats, not from ambition

Start with semester hours and class size, derive the maximum number of people in the room, and only then decide unit counts. One person per machine means 40 seats for a class of 40; high-value robots are usually rotated in groups, where three to six units support a full class cycle. Writing students × hours × seat-occupancy time into the plan turns quantity into arithmetic instead of guesswork.

Four items most often cut

• GPU servers: cloud capacity or an appliance first, dedicated servers in phase two
• Content and courseware: virtual simulation software at RMB 279,500 per suite shows the weight of this line
• Site works: electrical, precision air conditioning, floors and ceilings are separate construction scope and must be listed
• Training and support: state the years and response time, or nobody will be able to operate the lab after acceptance

A practical rule for list writing: after every platform or suite, add one sentence describing what it includes. Line items without that sentence cannot be compared across bids, and reviewers cannot tell whether the configuration is duplicated.