FitLLM

FitLLM provides a free, auditable calculator that determines whether a local large language model (LLM) can fit within the VRAM of NVIDIA GPUs or the unified memory of Apple Silicon Macs. It accounts for model weights, quantization, KV‑cache size, attention types, and runtime overhead, offering accurate memory estimates for various architectures and configurations.

FitLLM publishes 2 API contracts indexed on the APIs.io network, including Agent API API, and 1 more. Tagged areas include Artificial Intelligence, LLM, Calculator, GPU, and Memory.

The FitLLM catalog on APIs.io includes 1 Spectral governance ruleset.

FitLLM’s developer surface includes changelog, API reference, documentation, getting-started guide, and 9 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

24.0/100 emerging Agent 16/100 agent aware Full breakdown ↓
scored 2026-11-13 · rubric v0.28.0 · Show this score on your site →

1 published contract

Artificial Intelligence LLM Calculator GPU Memory

Kin Score

Kin Score How this is scored →
scored 2026-11-13 · rubric v0.28.0

Composite quality — 24.0/100 · emerging

Contract Quality 7.3 / 21

Developer Ergonomics 4.9 / 17

Access Clarity 0.0 / 17

Operational Transparency 2.1 / 11

Contract Governance 1.9 / 10

Discoverability 5.6 / 9

Regulatory Posture 0.7 / 15

Agent readiness — 16/100 · agent aware

Machine-Readable Contract 18 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 8 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship Ask about this →
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author Ask about this →
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey) Ask about this →
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented) Ask about this →
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0 Ask about this →
Rate-Limit Signaling +7 pts — OpenAPI responses document X-RateLimit-* / RateLimit headers (verified), else a rate_limits artifact or RateLimits link (documented) Ask about this →

11 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Access Clarity scores 0.0 — up to +20.0 composite points Ask about this →
Contract Quality scores 34.3 — up to +16.4 composite points Ask about this →
Developer Ergonomics scores 28.6 — up to +14.3 composite points Ask about this →

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

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Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/fitllm-run/apis.yml: open an issue on github.com/api-evangelist/fitllm-run to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Standards implemented 1

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

OpenAPI

measured — observed by APIs.io (published)

APIs 2

Individual APIs this provider publishes, each with its own machine-readable definition.

FitLLM Agent API

FitLLM provides a free calculator to determine if a local LLM fits within GPU VRAM or Apple Silicon memory.

FitLLM Agent API API

Will this local LLM fit this hardware? Auditable, architecture-aware memory estimates (MLA / sliding-window / hybrid-linear / MoE), with documented runtime/OS reserves. No auth,...

Spectral Rules 1

Spectral governance rulesets for linting and validating these APIs. Counts are the severity each rule declares, not results of running them.

FitLLM API Rules

32 rules · 30 error-severity 1 warn-severity 1 info-severity

SPECTRAL

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

FitLLM Domain Security

TLSv1.3 · HSTS

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

GettingStarted

GettingStarted

Documentation 2

Reference material describing how the API behaves

APIReference

APIReference

Documentation

Documentation

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Design & Contract 3

Pagination, idempotency, versioning, errors, and events

Spectral

Spectral

ErrorCatalog

ErrorCatalog

Conformance

Conformance

Build 1

SDKs, sample code, and the tooling you integrate with

SourceCode

SourceCode

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

ChangeLog

ChangeLog

Company 1

The organization behind the API

Website

Website

Other 2

Properties that don't map to a standard resource type

Hosts

Hosts

Vendors

Vendors

Source (apis.yml)

aid: fitllm-run
name: FitLLM
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - security
  generated: '2026-11-13'
  method: derived
image: https://fitllm.run/api/og
description: FitLLM provides a free, auditable calculator that determines whether a local large language model (LLM) can fit
  within the VRAM of NVIDIA GPUs or the unified memory of Apple Silicon Macs. It accounts for model weights, quantization,
  KV‑cache size, attention types, and runtime overhead, offering accurate memory estimates for various architectures and configurations.
url: https://artifacts.apis.io/fitllm-run/apis.yml
x-type: company
x-source: harvest:mcp-registry
x-tier: profiled
x-tier-reason: enrichment pass 2026-11-08 (modular-v1; was harvest)
specificationVersion: '0.20'
created: '2026-11-08'
modified: '2026-11-08'
tags:
- Artificial Intelligence
- LLM
- Calculator
- GPU
- Memory
tags_raw:
- AI
- LLM
- Calculator
- GPU
- Memory
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Spectral
  url: rules/fitllm-run-rules.yml
- type: ErrorCatalog
  url: errors/fitllm-run-problem-types.yml
- type: Conformance
  url: conformance/fitllm-run-conformance.yml
- type: LLMsTxt
  url: llms/fitllm-run-llms.txt
- type: Hosts
  url: hosts/fitllm-run-hosts.yml
- type: Vendors
  url: vendors/fitllm-run-vendors.yml
- type: ChangeLog
  url: https://github.com/click6067-ship-it/fitllm-engine/releases/tag/v2.22.0
- type: DomainSecurity
  url: security/fitllm-run-domain-security.yml
- type: Website
  url: https://fitllm.run/
- type: APIReference
  url: https://fitllm.run/openapi.json
- type: Documentation
  url: https://fitllm.run/can-i-run/why-naive-vram-calculators-are-wrong
- type: GettingStarted
  url: https://fitllm.run/can-i-run/why-naive-vram-calculators-are-wrong
- type: SourceCode
  url: https://github.com/click6067-ship-it/fitllm-engine
apis:
- name: FitLLM Agent API
  description: FitLLM provides a free calculator to determine if a local LLM fits within GPU VRAM or Apple Silicon memory.
  humanURL: https://fitllm.run
  baseURL: https://fitllm.run
  properties: []
  aid: fitllm-run:fitllm-agent-api
- aid: fitllm-run:fitllm-agent-api-api
  name: FitLLM Agent API API
  description: 'Will this local LLM fit this hardware? Auditable, architecture-aware memory estimates (MLA / sliding-window
    / hybrid-linear /

    MoE), with documented runtime/OS reserves. No auth, no key. Engine: MIT (github.com/click6067-ship-it/fitllm-engine) ·
    data: CC0 (fitllm.run/data).'
  humanURL: https://fitllm.run/openapi.json
  baseURL: https://fitllm.run
  properties:
  - type: OpenAPI
    url: openapi/_original/fitllm-run-api-openapi.json
x-enrichment:
  date: '2026-11-08'
  status: enriched
  artifacts_added: 15
  pass: modular-v1
  engine: identity=local,contract=local,surfaces=local,docs=local,regulatory=local

FitLLM API — frequently asked questions

Does FitLLM have an API?

Yes. APIs.io lists 2 APIs from FitLLM, including FitLLM Agent API and FitLLM Agent API API.

Is there an OpenAPI specification for the FitLLM API?

Yes. APIs.io indexes 1 OpenAPI specification for FitLLM, each linked for download from this page.

Is the FitLLM API ready for AI agents?

FitLLM has a Kin Score of 24.0 out of 100 (emerging) and an Agent Readiness score of 15.5 (agent-aware), scored by API Evangelist on 2026-11-13.

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