Local Ollama / AI / LLM Infrastructure Expert — Open-Source Models

We are looking for an experienced AI/LLM infrastructure expert to consult on and potentially build a local, self-hosted AI environment for our business. Objective We currently rely heavily on LLM APIs for tools like OpenClaw and others and are expecting high token costs and API/rate limits. We want to evaluate and implement a local solution using hardware such as an Apple M3/M4 machine or NVIDIA GPU server or others and open-source models. Scope * Recommend the best hardware/configuration for our needs and budget. * Compare Apple Silicon vs. NVIDIA GPU solutions or others. * Set up and optimize local LLM inference. * Deploy models such as Llama and other appropriate open-source models. * Evaluate performance, scalability, context windows, and concurrent usage. * Recommend tools/frameworks such as Ollama, vLLM, or similar. * Help integrate the local models with our existing applications/APIs. * Provide a realistic cost comparison vs. OpenAI/Anthropic/API usage. * Design the solution so we can minimize dependence on external API limits and recurring token costs. Ideal Candidate * Strong experience deploying LLMs locally/on-premise. * Experience with open-source models. * Strong NVIDIA/CUDA and/or Apple Silicon experience. * Experience with Ollama, vLLM, Docker, APIs, and AI infrastructure. * Able to advise on both the architecture and actual implementation. We are open to starting with a paid consultation/architecture assessment and then moving to implementation if the proposed solution makes sense. Please provide examples of local LLM environments you have built, including the hardware, models, and approximate performance/cost savings.