Self-Hosting LLMs is the deployment of large language models on your own infrastructure (on-premise servers, private cloud, or dedicated GPU instances) rather than using third-party API services — providing maximum control over data privacy (data never leaves your network), predictable costs at scale (hardware lease vs. per-token metering), and the ability to customize model internals (fine-tuning, quantization, custom decoding), at the cost of significant infrastructure complexity and upfront GPU investment.

What Is Self-Hosting?

• Definition: Running LLM inference (and optionally training/fine-tuning) on infrastructure you control — using open-source models (Llama, Mistral, Mixtral, Qwen, Gemma) deployed through serving frameworks (vLLM, TGI, TensorRT-LLM) on GPU hardware you own or lease.
• Data Sovereignty: The primary motivation for self-hosting — data never leaves your VPC/network, eliminating concerns about third-party data retention, training on your data, or compliance violations for regulated industries (healthcare, finance, government).
• Cost Crossover: Self-hosting becomes cheaper than APIs at high throughput — the crossover point is typically millions of tokens per day, where the fixed cost of GPU hardware is amortized over enough requests to beat per-token API pricing.
• Model Freedom: Self-hosting enables using any open-source model, applying custom fine-tuning, modifying decoding strategies, and running quantized models — flexibility impossible with closed API providers.

Self-Hosting Stack

• Models: Llama 3 (8B-70B), Mistral/Mixtral, Qwen 2.5, Gemma 2, DeepSeek — open-weight models with permissive licenses for commercial use.
• Serving Frameworks: vLLM (PagedAttention, continuous batching), TGI (Hugging Face), TensorRT-LLM (NVIDIA optimized), Ollama (local development) — each optimized for different deployment scenarios.
• Infrastructure: NVIDIA A100/H100 GPUs, Kubernetes for orchestration, Ray Serve for scaling — or cloud GPU instances (AWS p4d/p5, GCP a3, Azure ND).
• Optimization: Quantization (GPTQ, AWQ, GGUF) reduces memory requirements 2-4× — enabling larger models on fewer GPUs or smaller models on consumer hardware.

Self-Hosting vs. API

Self-hosting LLMs is the infrastructure strategy for organizations that need maximum data control and cost efficiency at scale — deploying open-source models on owned or leased GPU infrastructure through optimized serving frameworks, trading operational complexity for data sovereignty, customization freedom, and predictable economics at high throughput volumes.