Self-hosted LLM serving and load balancing

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

Organizations deploying large language models need to serve them reliably at scale on their own infrastructure, but existing solutions involve complex deployments with many moving parts that are difficult to manage and maintain.

Solution concept

A simplified LLM/VLM load balancer and serving platform built around the ggml ecosystem that reduces operational complexity through fewer moving parts and simpler deployments, while supporting both CPU and GPU execution for flexible infrastructure requirements.

Who has it

Engineering teams and organizations self-hosting language models and vision models, ranging from small companies to enterprises that need on-premise or private cloud deployments.

How they handle it today

Today they use or evaluate alternatives like llm-d, Docker Model Runner, and other serving platforms that require managing multiple components for load balancing, model serving, and infrastructure orchestration.

Why now

The rapid adoption of large language models and vision models has created urgent demand for on-premise serving solutions, particularly among organizations with privacy, data sovereignty, or latency requirements that prevent cloud API use.

How it could charge

Inference as a service; support and managed hosting tiers for self-hosted deployments; enterprise features for monitoring, security, and multi-model orchestration.