FuriosaAI
FuriosaAI is a South Korean semiconductor company that designs data-center AI inference chips and the software stack that runs on them. Its second-generation NPU, RNGD, implements a Tensor Contraction Processor architecture on TSMC 5nm and targets LLM, multi-modal and vision inference. The developer surface is software you run yourself rather than a hosted API: Furiosa-LLM ships an OpenAI-compatible server exposing chat, completions, responses, embeddings, score, rerank, models, tokenizer and Prometheus metrics endpoints, and the earlier Furiosa Model Server implements the KServe v2 Predict Protocol and the Triton Model Repository extension over REST and gRPC. Around that sit Python, Go and Rust packages, a furiosa-smi device CLI, container images, an APT/RPM package repository, and a Kubernetes cloud-native toolkit (device plugin, DRA driver, feature discovery, metrics exporter, NPU operator).
FuriosaAI publishes 5 API contracts indexed on the APIs.io network, including Health API, Models API, Predict API, and 2 more. Tagged areas include Artificial Intelligence, Machine Learning, Inference, Semiconductors, and NPU.
FuriosaAI’s developer surface includes documentation, API reference, getting-started guide, support, engineering blog, changelog, signup flow, and 27 more developer resources.
Kin Score
Markets this provider is in provisional
The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.
| Market | Position | Depth | Registered MCP server | Declares compatibility |
|---|---|---|---|---|
| MLOps and AI/ML Infrastructure (LF AI & Data, MLPerf, Kubeflow) | standard | 17.2 (thin) | - | - |
Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.
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.
APIs 5
Individual APIs this provider publishes, each with its own machine-readable definition.
Furiosa-LLM OpenAI-Compatible Server
The HTTP server started by `furiosa-llm serve
FuriosaAI Health API
The Health API from FuriosaAI — 2 operation(s) for health.
FuriosaAI Models API
The Models API from FuriosaAI — 3 operation(s) for models.
FuriosaAI Predict API
The Predict API API from FuriosaAI — 1 operation(s) for predict api.
FuriosaAI Repository API
The Repository API from FuriosaAI — 3 operation(s) for repository.
Pricing Plans 1
Published pricing tiers and plan structures.
Rate Limits 1
Documented rate limits and quota policies.
Furiosa Rate Limits
RATE LIMITSOpenAPI Overlays 2
Overlays applied on top of this provider's contracts. Each card says who wrote it: a document the provider publishes, or one API Evangelist derived or generated.
Furiosa Model Repository V2 Overlay
GENERATEDFuriosa Predict V2 Overlay
GENERATEDSecurity Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Get Started 4
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 2
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 5
Pagination, idempotency, versioning, errors, and events
Build 4
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 8
Status, limits, changes, and where to get help
Scroll for all 8
Commercial 3
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
Other 2
Properties that don't map to a standard resource type
Source (apis.yml)
Work with this as data
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