Skip to content

doc: fix dead links #989

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jun 16, 2025
Merged

doc: fix dead links #989

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions site-src/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ The following specific terms to this project:
inference workloads.
- **Inference Scheduler**: An extendable component that makes decisions about which endpoint is optimal (best cost /
best performance) for an inference request based on `Metrics and Capabilities`
from [Model Serving](/docs/proposals/003-model-server-protocol/README.md).
from [Model Serving](https://github.com/kubernetes-sigs/gateway-api-inference-extension/tree/main/docs/proposals/003-model-server-protocol/README.md).
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

xref #998 for the need to move design proposals to the docs site.

- **Metrics and Capabilities**: Data provided by model serving platforms about
performance, availability and capabilities to optimize routing. Includes
things like [Prefix Cache] status or [LoRA Adapters] availability.
Expand All @@ -33,8 +33,8 @@ Gateway API Inference Extension optimizes self-hosting Generative AI Models on K
It provides optimized load-balancing for self-hosted Generative AI Models on Kubernetes.
The project’s goal is to improve and standardize routing to inference workloads across the ecosystem.

This is achieved by leveraging Envoy's [External Processing](https://www.envoyproxy.io/docs/envoy/latest/configuration/http/http_filters/ext_proc_filter) to extend any gateway that supports both ext-proc and [Gateway API](https://github.com/kubernetes-sigs/gateway-api) into an [inference gateway](../index.md#concepts-and-definitions).
This extension extends popular gateways like Envoy Gateway, kgateway, and GKE Gateway - to become [Inference Gateway](../index.md#concepts-and-definitions) -
This is achieved by leveraging Envoy's [External Processing](https://www.envoyproxy.io/docs/envoy/latest/configuration/http/http_filters/ext_proc_filter) to extend any gateway that supports both ext-proc and [Gateway API](https://github.com/kubernetes-sigs/gateway-api) into an [inference gateway](#concepts-and-definitions).
This extension extends popular gateways like Envoy Gateway, kgateway, and GKE Gateway - to become [Inference Gateway](#concepts-and-definitions) -
supporting inference platform teams self-hosting Generative Models (with a current focus on large language models) on Kubernetes.
This integration makes it easy to expose and control access to your local [OpenAI-compatible chat completion endpoints](https://platform.openai.com/docs/api-reference/chat)
to other workloads on or off cluster, or to integrate your self-hosted models alongside model-as-a-service providers
Expand Down