GMTekAI designs and deploys local or hybrid AI systems around your actual workflow, hardware, and business needs — from the first runtime decision to the first live agent.
Prompts, responses, and agent conversations stay on your hardware. Client data, health info, legal records — nothing touches a third-party cloud inference server.
Cloud inference bills grow as your usage grows. Local deployment converts recurring cost into one-time hardware investment — and keeps it there as your operation scales.
No rate limits, no API outages, no vendor dependency. Your inference stack runs when you need it — nights, weekends, and under load — without cold starts or throttling.
We help you choose the right hardware for your workload — or deploy to machines you already have.
This is our own production setup — one example of what a local AI deployment looks like. The exact model and runtime pairing we recommend depends on your workload, not benchmark obsession.
Runs all client-facing agents — phone receptionist, lead triage, scheduling, ops coordination, market intelligence. The primary thinking engine for your business.
Writes n8n workflow logic, builds automation scripts, handles code-heavy tasks while the primary model handles conversations. Two brains, no context switching.
Deployment is matched to the job. — We select the right model, runtime, and hardware tier based on your actual workflow, not a fixed template.
The runtime and model are just one layer. OpenClaw connects everything above it — your tools, workflows, memory, and agent logic.
OpenClaw handles tool routing, agent memory, skill execution, and workflow logic. It's the connective layer between your inference runtime and your actual business operations.
For specialized, compliance-sensitive, or multi-layered environments, GMTekAI can extend the OpenClaw stack with NemoClaw capabilities where appropriate.
OpenClaw is open source. View on GitHub →
PagedAttention-powered GPU inference. This is what we run on the tower — Qwen3-14B-AWQ on GPU 0, Qwen2.5-Coder-7B on GPU 1. Maximum throughput for production multi-agent systems.
The fastest way to run models locally. One command, model downloaded, chat running. Perfect for development, testing architectures, and client demos before committing to production.
Pure C++ inference engine. Runs full models on CPU when GPU isn't available. Our go-to for client deployments on standard hardware — no GPU required, still fast enough for production workloads.
We spec the hardware, set up the models, wire the agents, and hand you the keys. Most deployments are scoped quickly and rolled out in phases based on hardware, integrations, and workflow complexity.