Why local AI
For most software teams, cloud AI assistants are an easy yes. For hardware teams, they often aren’t: RTL, schematics, and firmware are the company’s crown-jewel IP, and NDAs, export control, and defense or medical compliance rules can flatly forbid sending design data to a third-party cloud service. The result is engineers locked out of the biggest productivity shift in a decade — not because the tools don’t work, but because the data can’t travel.
Local AI removes the conflict. Open-weight models now deliver serious coding and reasoning capability, and they run on hardware you own: every prompt, every file, and every generated line stays inside your infrastructure. We set that up as a service — scoped to your team, your models, and your security posture.
What we set up
- Self-hosted model serving. Open-weight coding and reasoning models — GLM 5.2 (currently the most popular open-weight model), DeepSeek, Qwen, and Llama — served with tools like Ollama or vLLM on hardware sized to your team.
- Private agentic tooling. Coding assistants and agent frameworks pointed at your local endpoints, so engineers get the same AI-assisted development workflows with zero design data leaving the building.
- Retrieval over your documentation. Local search and RAG over internal datasheets, design documents, and coding standards, so the AI answers with your context instead of guessing.
- Guardrails and rollout. Usage policies, review checklists, and a rollout plan so local AI lands as a durable engineering practice, not a science project.
Scoped to your environment
Every setup is sized to the team and the constraints:
- Single workstation. A compact coding model on one engineer’s GPU, or a unified-memory machine like AMD’s Strix Halo (Ryzen AI Max+, up to 128 GB shared between CPU and GPU), NVIDIA’s DGX Spark, or a Mac Studio (up to 256 GB) that runs mid-size models without a discrete graphics card — the smallest useful footprint, often the right pilot.
- Shared inference server. A dedicated multi-GPU box serving the whole engineering group, with larger models and concurrent users.
- Air-gapped and restricted networks. Models, serving stack, and tooling installed fully offline — standard practice in defense, aerospace, and regulated industries.
Hardware recommendations come first in the engagement: which models fit your use cases, what GPU memory they need at usable quantizations, and what that costs — before you buy anything.
Stronger together with training
Infrastructure is half the value; the other half is engineers who know how to use it. Our agentic AI-assisted FPGA training teaches the workflows — prompt engineering for RTL, agent loops, skills and hooks, review discipline — and pairing it with a local AI setup means your team learns on the exact private infrastructure they’ll use every day. For what those workflows look like on real projects, see our notes on AI tooling in FPGA flows.
Frequently asked questions
Why run AI locally instead of using cloud AI tools?
RTL, schematics, and firmware are core IP, and many hardware companies work under NDAs, export control, or defense and medical compliance rules that forbid sending design data to third-party cloud services. Local, on-premise AI gives engineers the same agentic workflows while every prompt and file stays inside your infrastructure.
What does a local AI setup include?
Self-hosted serving of open-weight models such as GLM 5.2, DeepSeek, Qwen, and Llama (via Ollama or vLLM), GPU and hardware sizing, private endpoints for agentic coding tools, retrieval over internal documentation, and usage guardrails with a rollout plan — scoped from a single workstation to a shared inference server for the whole engineering group.
What hardware do we need to run LLMs locally?
It depends on the models and how many engineers use them concurrently: compact coding models run on a single workstation GPU; unified-memory platforms such as AMD Strix Halo (Ryzen AI Max+), NVIDIA DGX Spark, or an Apple silicon Mac Studio (up to 256 GB unified memory) fit mid-size models without a discrete GPU; and larger open-weight models or multi-user serving call for a dedicated multi-GPU inference server. We size the hardware against your team, models, and budget as the first step of the engagement.
Does local AI work in air-gapped environments?
Yes. Models, serving stack, and tooling can be installed fully offline, and the workflow runs without any external connectivity — a common requirement in defense, aerospace, and regulated industries.
Do our engineers need training to use local AI effectively?
The infrastructure is only half the value. Pairing local AI setup with our agentic AI-assisted FPGA training gets teams productive on the workflows — prompt engineering for RTL, agent loops, and review discipline — using the private infrastructure from day one.
Ready to bring AI inside your network? Tell us your team size, current hardware, and constraints — we’ll come back with a concrete model, hardware, and rollout proposal.