# Self-hosted LLM

**Source:** https://promtable.com/glossary/self-host-llm

> A self-hosted LLM runs entirely on infrastructure you control — your GPUs, your servers, your data residency — versus calling a cloud API.

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A self-hosted LLM runs entirely on infrastructure you control — your GPUs, your servers, your data residency — versus calling a cloud API.

Self-hosted LLM deployments in 2026 use open-weight models (Llama 4, Qwen 2.5, Mistral, DeepSeek-R1) on inference engines (vLLM, TGI, sglang) running on cloud GPUs (RunPod, Lambda, Vast.ai, CoreWeave) or on-prem hardware. The motivations: data sovereignty (regulated industries, EU residency), cost at very high scale, model customisation (fine-tuning), and zero per-token API cost. The trade-offs: ops burden (updates, scaling, GPU management), capability lag vs frontier APIs, and engineering team requirement. For most teams in 2026 cloud APIs win on TCO; for compliance-heavy + high-volume teams self-hosted wins.

## When to use

- Regulated industries with data residency requirements.
- Very high inference volume where API costs dominate.
- Custom fine-tuned models.

## Common mistakes

- Underestimating ops burden — GPU management is real work.
- Choosing self-host for cost without modelling TCO including engineering time.

## Related terms

- [local-llm](https://promtable.com/glossary/local-llm)
- [openrouter](https://promtable.com/glossary/openrouter)
- [vector-database](https://promtable.com/glossary/vector-database)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/self-host-llm
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