ModelSpec: A Blueprint for AI Model Intent

One of the things we learned while building and publishing AI infrastructure checklists is that most production issues aren’t caused by missing knowledge — they’re caused by missing assumptions. Teams generally know what they should be thinking about:

• hardware constraints

• batching and sequence limits

• latency targets

• scaling behavior

• observability

• governance and compliance

Checklists help surface those questions. But they don’t answer a harder one: Where do these assumptions actually live?

The Problem: Model Intent Is Scattered

In most AI teams today, model intent is fragmented:

• Model identity lives in a README

• Runtime constraints live in Helm values

• Batching behavior lives in code

• Scaling assumptions live in dashboards

• Governance rules live in policy docs

• Ownership lives in Slack threads

Individually, none of these are wrong. Collectively, they make it very hard to answer simple questions like:

• What model is this, exactly?

• What was it designed to run on?

• What constraints were assumed when it was reviewed?

• What does “production-ready” mean for this deployment?

When systems drift — and they always do — teams end up debugging symptoms instead of intent.

Why Checklists Aren’t Enough on Their Own

Checklists are excellent at answering: “What should we be thinking about?”

They are less effective at answering: “What did we decide?”

This is where design reviews stall. Not because teams disagree — but because assumptions are implicit, undocumented, or remembered differently. At a certain level of system complexity, reasoning alone stops scaling. You need a place where intent can be written down.

Introducing ModelSpec

ModelSpec is a small, declarative specification for describing:

• what a model is

• what it expects from the runtime

• how it is intended to be operated

Nothing more. It is:

• not a deployment tool

• not an orchestrator

• not a scheduler

• not an enforcement system

ModelSpec exists for one purpose: To make model intent explicit, reviewable, and auditable.

ModelSpec as a System of Record

One way to think about ModelSpec is as a system of record for models. Much like:

• OpenAPI describes API intent without enforcing it

• Terraform configs often start as documentation

• Architecture diagrams capture decisions without executing them

ModelSpec can be used simply as:

• structured documentation

• a design review artifact

• a shared reference across ML, infra, SRE, and security

• an onboarding aid

• a compliance input

No automation required. That alone turns out to be surprisingly powerful.

What Goes Into a ModelSpec

A ModelSpec can be as small or as rich as a team needs. At its simplest, it might capture:

• model identity

• hardware requirements

As teams mature, it can grow to include:

• batching and sequence constraints

• serving interfaces

• scaling targets

• observability expectations

• model-to-model dependencies

• governance and retention rules

The key idea is not completeness — it’s intentionality.

From Reasoning to Explicit Intent

Checklists help teams reason about AI systems. ModelSpec helps teams record the outcome of that reasoning. That distinction matters.

Once intent is explicit:

• reviews become concrete

• assumptions can be challenged early

• drift becomes detectable

• operational ownership becomes clearer

And later — when teams are ready — that intent can be validated against reality.

Open-Sourcing ModelSpec

We’ve open-sourced ModelSpec to make this approach available to any team dealing with production AI systems. The repository includes:

• documentation

• a progression of examples, from minimal to full production

• guidance on how to adopt ModelSpec incrementally

You don’t need to adopt any tooling to use it. You don’t need to change how you deploy models. You just need a place for intent to live.

👉 ModelSpec on GitHub: https://github.com/paralleliq/modelspec

Or visit documentation, examples and use cases:

👉 ModelSpec on Paralleliq site: http://www.paralleliq.ai/modelspec

Closing Thought

Most AI infrastructure failures are not caused by bad decisions. They’re caused by undocumented ones. Checklists help teams ask better questions. ModelSpec is one way to capture the answers.