MatX website screenshot

MatX

MatX is an AI-hardware company designing specialized semiconductors optimized for large language model workloads. Its flagship MatX One chip targets high-throughput, low-latency training, inference, and reinforcement learning for large dense and Mixture-of-Experts models, storing weights in SRAM to cut latency and reaching over 2,000 output tokens per second on large models. MatX is a hardware and research company with no public developer API surface; this profile captures its identity, research/blog output, open-source codebases, and domain-security posture. Backed by Jane Street, Spark Capital, and Situational Awareness LP.

MatX is profiled on the APIs.io network. Tagged areas include Company, Semiconductors, Artificial Intelligence, Hardware, and Machine-Learning.

MatX’s developer surface includes engineering blog and 3 more developer resources.

🌐 Visit website 📡 Source on GitHub
5.8/100 minimal ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0
0 APIs
CompanySemiconductorsArtificial IntelligenceHardwareMachine-LearningLLMAI AcceleratorInference

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0
Composite quality — 5.8/100 · minimal
Contract Quality 0.0 / 25
Developer Ergonomics 0.5 / 20
Access Clarity 0.0 / 20
Operational Transparency 0.3 / 13
Contract Governance 0.0 / 12
Discoverability 5.0 / 10
Agent readiness — 0/100 · human only
Machine-Readable Contract 0 / 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 0 / 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
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.
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 github.com/api-evangelist/matx: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Security Posture 1

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

Matx Domain Security

TLSv1.3 · HSTS · DNSSEC · DMARC

SECURITY

Resources

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization
GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity
DomainSecurity

Company 2

The organization behind the API

Website
Website
 Blog
Blog

Source (apis.yml)

apis.yml Raw ↑ 
aid: matx
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/matx.png
name: MatX
description: MatX is an AI-hardware company designing specialized semiconductors optimized for large language model workloads.
  Its flagship MatX One chip targets high-throughput, low-latency training, inference, and reinforcement learning for large
  dense and Mixture-of-Experts models, storing weights in SRAM to cut latency and reaching over 2,000 output tokens per second
  on large models. MatX is a hardware and research company with no public developer API surface; this profile captures its
  identity, research/blog output, open-source codebases, and domain-security posture. Backed by Jane Street, Spark Capital,
  and Situational Awareness LP.
url: https://raw.githubusercontent.com/api-evangelist/matx/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- spark-capital
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Semiconductors
- Artificial Intelligence
- Hardware
- Machine-Learning
- LLM
- AI Accelerator
- Inference
tags_raw:
- Company
- Semiconductors
- AI
- Hardware
- Machine Learning
- LLM
- AI Accelerator
- Inference
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: DomainSecurity
  url: security/matx-domain-security.yml
- type: Website
  url: https://matx.com
- type: Blog
  url: https://matx.com/research
- type: GitHubOrganization
  url: https://github.com/MatX-inc
x-enrichment:
  date: '2026-07-20'
  status: minimal
  artifacts_added: 1
  pass: local-v1

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