Decoupling the AI stack moves local LLMs from hobbyist tools into enterprise‑ready infrastructure. The SOLV Stack splits the system into a Presentation Layer for chat, a Governance Layer that routes, logs, and authenticates, and an Inference Layer that runs models on GPUs. This separation eliminates vendor lock‑in and scales with demand.

At the heart of the inference layer sits vLLM, chosen for its PagedAttention that maximizes GPU utilization and supports high‑throughput batching. The gateway layer, LiteLLM, normalizes every request to the OpenAI API format, letting client apps—whether OpenWebUI, a React front‑end, or a VS Code plug‑in—talk to any backend without code changes. This hybrid routing lets routine work stay on‑premise while complex reasoning can spill over to GPT‑4.

Deploying the SOLV stack on a local server with an RTX 5090 turns a team’s VS Code extensions into a Copilot‑like assistant that never leaves the firewall. Developers point Continue or Cline to http://your‑server:8080/llm/v1, and the gateway routes calls to the local model or the cloud as configured. The open‑source repo on GitHub ships Docker‑Compose files, model‑download scripts, and RAG pipelines, inviting teams to experiment and iterate.