Short answer
Local AI agent deployment means putting the three things an agent needs โ model, knowledge base and orchestration โ inside your own machine or intranet, so neither your material nor the conversations leave the building. Three approaches are genuinely workable: โ single-machine pilot; โก local knowledge base + cloud model (material stays inside, only the question goes out); โข containerised private deployment (shared inside the company, with permissions and audit). Which one you pick depends on two things: data sensitivity and how many people will use it.
What it actually means: three "locals"
Many setups that call themselves local have done only a third of it:
| Layer | What it is | What local means here |
|---|---|---|
| Model | The model that writes the answer | Answers are computed on your hardware; speed depends on it |
| Knowledge | Your documents and the retrieval index | Not one byte of material leaves the intranet (the layer that matters most) |
| Orchestration | The layer that lets the agent call tools and follow a process | Process and logs stay with you, no third-party platform |
To test whether a solution is truly local, ask where the knowledge layer lives. Model moved inside but material still on a public cloud means nothing was localised.
What you need: three hardware tiers
The resource hog is the model, not the agent framework. Retrieval is cheap.
| Tier | For whom | Rough spec | What it enables |
|---|---|---|---|
| Single machine | Prove it works | 16GB+ RAM, no GPU | Local knowledge base + retrieval; answers via a cloud model |
| Small team | 3โ20 daily users | 32โ64GB RAM + one 24GB GPU | Mid-size local model; material and logs stay inside |
| Company intranet | Dozens+, needs permissions and audit | Server + GPU or an internal inference service | Separate libraries per department, access control, logging |
Three approaches, and how to run them
A โ Single-machine pilot (half a day to a day)
Goal: verify that a local knowledge base can answer your real questions before buying hardware.
- One machine with 16GB+ RAM running a self-hostable knowledge base and retrieval service;
- Load a handful of real documents, then ask your most common questions;
- Generate answers with a cloud model first, to get the pipeline right.
Trap: the usual failure is not technical โ it is that the documents themselves contradict each other.
B โ Local knowledge base + cloud model (what we deliver most)
- Documents, vector store, logs and backups all stay inside;
- Retrieval happens locally; only the few retrieved passages plus the question go to the model;
- Original material is never transmitted.
Trap: bad chunking sends the wrong passages, and the model will state wrong answers confidently. Having a human review the chunks is the quality dividing line.
C โ Containerised private deployment (shared inside the company)
- Package knowledge base, model service and portal with containers, deploy once, use in many places;
- Add permissions: who sees which library, who may edit which document;
- Add operations: a fallback when the model service hiccups โ never leave the service page blank.
Trap: without a fallback, one wobble in the model service kills the whole thing.
The four traps enterprises hit most
- Fake localisation โ model moved in, knowledge base still on a public cloud.
- Nobody checks the chunks โ fully automatic ingestion, errors nobody notices.
- No fallback โ one hiccup and staff stop using it after the second try.
- No owner โ nobody updates the knowledge; three months later every answer is stale.
How to choose
| Your situation | Suggested approach |
|---|---|
| Just want to know if it is feasible | A โ one machine, lowest cost |
| Sensitive material (client lists, records, process know-how) | B โ material never leaves; only the question does |
| Dozens of users, permissions and audit needed | C โ containers + access control |
| No operations staff at all | Start with B, assign one person to own knowledge updates |
What we do
ๅๅฐ็ซ SavantCat helps small businesses turn their own knowledge into something AI can use โ and be trusted with: enterprise knowledge-base localisation and AI customer-service rollout, deployed privately by default, data never leaves your machine; sensitive scenarios do not use overseas APIs.
- Small-business knowledge-base localisation from CNY 5,000, annual maintenance CNY 2,000โ3,000;
- More: Enterprise knowledge base ๏ฝ AI customer service ๏ฝ Website GEO;
- Use our endpoints directly: two MCP interfaces.
Frequently asked questions
What does local AI agent deployment mean?
It means keeping the three things an agent needs โ the model, the knowledge base and the orchestration layer โ inside your own machine or intranet, instead of using a vendor's cloud service. The point is the data boundary: your material, the passages retrieved from it, and the conversation logs all stay with you.
Do I need a GPU?
It depends on the part. Retrieval and the knowledge base need no GPU โ a normal CPU server is fine. A GPU is needed only when the answer is generated by a local model. With a hybrid setup (local knowledge base + cloud model) only the question text leaves the machine, so no GPU is required at all.
Is a local model worse than a cloud model?
At equal parameter counts, yes โ that is the honest answer. A more practical compromise for business: keep the material inside (retrieval happens locally) and send only the few retrieved passages plus the question to a cloud model for generation.
How do I verify data really never leaves?
Check four things, all of them: โ where the model that generates answers runs; โก where the knowledge base and vector store live; โข where access logs and conversation records live; โฃ where backups and sync go. Many 'local deployments' moved the model inside while the knowledge base still sits on a public cloud.
What does a local deployment cost?
Two lines: hardware is a one-off (your own server or a machine you already have); delivery and maintenance are an annual service fee. Our small-business knowledge-base localisation starts at CNY 5,000, with annual maintenance of CNY 2,000โ3,000.
We only have a few PCs โ is local deployment possible?
Yes. The lightest form is a single-machine deployment: one machine with 16GB+ RAM running the knowledge base and retrieval service, with the model still on a cloud API, so your material never leaves the intranet.