Build Self-Hosted LLM Pipeline

I’m ready to stand up a fully self-hosted language model that is fast, secure, and production-ready. Your job is to spin up a compact model with vLLM or llama.cpp, wrap it in a clean CUDA/Docker stack, and layer in the essentials—streaming responses, smart caching, guardrails, and real-time metrics.

Here’s what success looks like:
• Working inference service running on my hardware or cloud instance, exposed through a simple REST or gRPC API.
• End-to-end streaming in place, delivering low p50/p95 latency and stable throughput under load.
• Guardrails that strip or mask PII, with compliant logs saved in a separate, safe location.
• Basic eval harness and prompt templates so I can compare future checkpoints quickly.
• Clear latency, memory, and throughput report plus API documentation.

I’ll start with a paid test: deploy any quantized 7-13B model, enable streaming, then share an infra diagram and benchmark numbers (latency, memory footprint). Nail that and we’ll move on to caching, guardrails, and deeper evaluation.

Show me links to prior self-hosting work and a quick sketch of your proposed setup. If you thrive in low-level inference, CUDA kernels, and containerized MLOps, let’s talk.

price to be decided - please propose - fixed (1k to 1.5k) + hourly (12 18 $/hr)