Self-Hosted LLMs for Enterprise: Running Open Models in Your Own Infrastructure

Two years ago, running a capable large language model on your own hardware meant accepting a serious quality gap. That gap has largely closed. Open-weight models now handle the majority of enterprise tasks at a quality that makes self-hosting a serious default rather than a compromise.

Why Self-Host at All

Self-hosting gives you three things a hosted API cannot: complete data control, fixed cost independent of usage, and freedom from model deprecation and rate limits. For regulated workloads, the first reason alone is decisive, as we explain in Private AI vs Cloud AI.

The Open Model Landscape

Families like Llama, Mistral, and Qwen offer a wide range of sizes, from small models that run on a single GPU to large ones that rival proprietary frontier models on many tasks. The right choice depends on the job, not the leaderboard. A well-chosen small model often beats a large one once you account for latency and cost.

Match the Model to the Task

Most enterprise tasks, such as classification, extraction, summarization, and retrieval-augmented answering, do not need a frontier model. Reserve the largest models for genuinely hard reasoning, and use smaller, faster models for high-volume work. Many production systems run a mix and route each request to the smallest model that can handle it.

Serving and Performance

Modern inference servers deliver high throughput through techniques like continuous batching and efficient memory use. With the right serving layer, a single server handles meaningful concurrent load. The infrastructure to run all of this is covered in our on-premise deployment guide.

Fine-Tuning vs Retrieval

Teams often reach for fine-tuning when retrieval would serve them better. For most knowledge tasks, retrieval-augmented generation over your own documents beats fine-tuning: it is cheaper, easier to keep current, and keeps source data auditable. Fine-tune when you need to change behavior or style, not just to add knowledge.

Powering Agents on Private Models

Self-hosted models are the foundation for private AI agents that can act on your systems without exposing data to third parties. The model is the engine, but the value comes from the tools and workflows you connect it to.

Getting Started

Pick one workload, choose the smallest model that handles it well, serve it properly, and measure. The skills and infrastructure transfer directly to every model you host afterward. To design a self-hosted stack around your workloads, get in touch.