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LOCAL CAPACITY + CLOUD

Infrastructure is part of the solution.

We connect private servers and cloud services according to workload, data and the control each project needs.

Conceptual illustration of LOCAL CAPACITY + CLOUD
Conceptual visualization of the area of work.

Private inference

A pretrained model can work on your own infrastructure: querying documents, summarizing, transcribing or assisting workflows. PCDoctor.ai designs and validates the server for the agreed use.

AMD, Intel and NVIDIA

AMD is a preferred option where its memory and architecture fit the workload. Intel can support coordination and services; NVIDIA is considered where CUDA or software requires it. Compatibility is tested before capacity is promised.

The role of the cloud

Model providers, Google Cloud and Cloudflare can add reasoning, publishing and connectivity. Their use depends on permissions, availability, costs and project requirements.

Continuity and recovery

Design considers backups, power, connectivity and recovery. Redundancy is considered verified once failure and recovery have been tested.

More technical detail and project context

Primary Intel Node

Coordination services, browser/build/test work, fallback execution, MCP access, Open WebUI integration and daily operational services.

AMD Local AI Node

Local-first AI capacity for coding, heavy reasoning and private model experiments, with vLLM/ROCm direction and rollback-aware model registry.

Ralfi IA MCP

Shared bridge for ChatGPT, Codex, Cursor, Open WebUI and agents. It exposes tools, state, runbooks, documents and operational actions under governance.

Cloud + Edge

Cloud Run, Cloudflare, Google Cloud, Alibaba Cloud and external model providers extend reach when public access, scale or advanced models are needed.

Primary local server

The primary local environment carries coordination services, browser/build/test work, operational APIs, MCP access, Open WebUI integration and day-to-day services.

AMD local AI server

The AMD node is the strategic local AI brain for heavier coding and reasoning workloads, local model experiments, private inference and future vLLM/ROCm routing.

MCP interconnection

Ralfi IA MCP is the bridge: it lets ChatGPT, Codex, Cursor, Open WebUI and agents share tools, state, runbooks and operational actions.

Credit savings

Local-first execution avoids spending cloud credits on every coding, reasoning, summarization or automation task. The system should know when local is enough.

Cloud escalation

When a task needs stronger models, public hosting, serverless scale, crawler data, or provider-specific tools, the system can escalate to OpenAI, Gemini, Claude, Qwen providers, Google Cloud, Cloudflare, Alibaba Cloud, Bright Data or other services.

Evidence and fallback

Model routing should expose selected node, selected model, backend, reason, health and fallback status so operators can see where work actually ran.

THE NEXT STEP STARTS WITH A CONVERSATION

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to make possible?

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