Inferensys

Glossary

Redfish API

An open industry-standard RESTful interface for out-of-band management of modern server hardware, enabling automated monitoring, firmware updates, and provisioning of GPU nodes.
SRE continuously monitoring AI systems on multiple screens, real-time dashboards visible, dark mode NOC setup.
OUT-OF-BAND MANAGEMENT STANDARD

What is Redfish API?

The Redfish API is an open industry-standard RESTful interface for the out-of-band management of modern server hardware, enabling automated monitoring, firmware updates, and provisioning of GPU nodes.

The Redfish API is a secure, multi-node capable replacement for legacy IPMI, using a RESTful interface over HTTPS with JSON-encoded responses. It provides a hypermedia-centric data model for managing modern server hardware, including GPUs, storage, and network interfaces, independently of the host operating system state.

Designed by the DMTF for scalable data center automation, Redfish enables infrastructure directors to programmatically discover, provision, and update GPU clusters at scale. Its standardized schema supports firmware versioning, thermal monitoring, and power capping, making it essential for maintaining a sovereign AI infrastructure with strict hardware integrity controls.

OUT-OF-BAND MANAGEMENT

Key Features of Redfish API

The Redfish API is a modern, secure, and extensible standard for managing scale-out server hardware. It replaces legacy IPMI with a RESTful, JSON-based interface designed for the automation demands of composable and disaggregated infrastructure.

01

RESTful & JSON-Based Architecture

Redfish is built on a modern RESTful (Representational State Transfer) interface, using HTTPS for transport and JSON for data serialization. This makes it inherently human-readable and easily scriptable with standard tools like curl or Python requests, unlike the binary protocols of legacy IPMI. Every resource—from a chassis to a GPU—is addressable via a unique URI, enabling programmatic discovery and management of the entire hardware fleet.

OData v4
Schema Standard
02

Secure by Design

Security is a foundational requirement, not an afterthought. Redfish mandates TLS 1.2 or higher for all communication, ensuring encryption in transit. Authentication is handled through a centralized Session Management model using standard HTTP Basic or Token-based auth. Role-Based Access Control (RBAC) allows administrators to define granular privileges, restricting a monitoring tool to read-only access while granting firmware update permissions only to a provisioning service.

03

Comprehensive Hardware Modeling

The data model is organized around physical and logical computer systems. Key resource types include:

  • ComputerSystem: The server itself, including CPU, memory, and GPU inventory.
  • Chassis: The physical enclosure, power supplies, and thermal sensors.
  • Manager: The Baseboard Management Controller (BMC) providing the Redfish service.
  • UpdateService: A standardized endpoint for orchestrating firmware updates across multiple components. This model allows a single API to manage everything from a single GPU to an entire rack.
04

Event-Driven Telemetry & Alerts

Redfish eliminates the need for constant polling through its Event Service. Clients can subscribe to a stream of asynchronous events pushed via Server-Sent Events (SSE) or WebSockets. This enables near-real-time alerting for critical hardware faults, such as a GPU exceeding its thermal threshold or a redundant power supply failure. The MetricReport resource provides standardized telemetry data, allowing integration with monitoring platforms like Prometheus for historical trend analysis.

05

Automated Provisioning & Composability

The API is designed for infrastructure-as-code workflows. Using the CompositionService, administrators can dynamically assemble a server from disaggregated pools of compute, GPU accelerators, and storage. A single POST request can attach a specific GPU model to a blade and boot it with a predefined BIOS configuration. This programmatic composability is essential for bare-metal orchestration platforms like MAAS or Metal3, enabling rapid repurposing of GPU nodes for different AI training jobs.

06

Vendor-Neutral Extensibility

Managed by the DMTF (Distributed Management Task Force), Redfish is an open industry standard with broad adoption across all major server OEMs. While the core schema ensures interoperability, vendors can publish their own OEM extensions to expose unique hardware features—such as NVIDIA's GPU-specific telemetry or custom liquid cooling pump status—without breaking the standard model. This provides a single, unified management interface across a heterogeneous fleet of AI infrastructure.

REDFISH API EXPLAINED

Frequently Asked Questions

Clear, technical answers to the most common questions about the Redfish API and its role in modern infrastructure management.

The Redfish API is an open industry-standard RESTful interface for out-of-band management of modern server hardware. It works by exposing a secure, HTTPS-based JSON data model that allows administrators and automation tools to perform operations independently of the server's operating system or power state. Unlike legacy protocols, Redfish uses a hypermedia-driven design, where a root service entry point (/redfish/v1/) links to all available resources, making it self-describing and discoverable. It communicates with a Baseboard Management Controller (BMC) to monitor sensors, update firmware, configure BIOS settings, and provision operating systems, all through standard GET, POST, PATCH, and DELETE HTTP methods.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.