Question 1

What is private LLM deployment?

Accepted Answer

Locally deployable models, inference, enterprise knowledge, access controls, and logs run on servers, private cloud, or isolated infrastructure governed by the organization. The main differences from cloud APIs are data boundaries, cost structure, and customization depth.

Question 2

Which organizations benefit most?

Accepted Answer

Organizations with sensitive data, stable high workloads, deep integration needs, or strict internal-network and audit requirements. Cloud APIs are often more flexible for low-volume experiments without sensitive data.

Question 3

Can DeepSeek, Qwen, GLM, and Llama all be deployed?

Accepted Answer

Select a specific model whose license permits the intended use and whose runtime is supported. Brand and parameter count are insufficient; verify license, precision, context, memory, concurrency, and business quality.

Question 4

How does an AI appliance relate to private deployment?

Accepted Answer

An appliance is one delivery form with pre-integrated hardware and software. Private deployment can also use existing servers, private cloud, or multi-node clusters. Expansion, integration, and operations determine the better form.

Question 5

What hardware is required?

Accepted Answer

Validation can start on a desktop-class or single-server platform. Production commonly uses multi-GPU servers or multi-node clusters. CPU, memory, storage, network, power, and cooling are also capacity constraints.

Question 6

How many GPUs are needed?

Accepted Answer

Start with model weights, then add KV cache, runtime overhead, and safety margin. Load-test real context and concurrency. Selecting GPUs only from parameter count or aggregate memory risks poor performance or idle capacity.

Question 7

Can we deploy without our own data center?

Accepted Answer

Options include dedicated private cloud, compliant colocation, or hybrid deployment. Evaluate data location, remote access, egress, backup and recovery, and responsibility boundaries together.

Question 8

Can domestic Chinese accelerators be used?

Accepted Answer

Some models support platforms such as Ascend and Hygon, but operators, runtime, quantization formats, and performance differ. Complete compatibility and workload testing before procurement.

Question 9

How long does delivery take?

Accepted Answer

It depends on hardware readiness, air-gap requirements, knowledge preparation, permissions, integrations, and acceptance load. A proof of concept may take days; production delivery is usually staged over weeks and confirmed after assessment.

Question 10

How can data remain in-domain?

Accepted Answer

Keep models, documents, vectors, and logs in the designated environment; restrict egress by default; verify allowlists, identity, access control, and audit logs. Air-gapped sites also need governed model and patch import.

Question 11

What should multinational companies in China consider?

Accepted Answer

Identify personal information, important data, and cross-border access first, then have legal counsel confirm obligations. Common technical controls include China-based storage, minimized transfers, segmented access, and auditable use; these do not replace legal advice.

Question 12

Can it connect to WeCom, OA, and document systems?

Accepted Answer

Yes. A model gateway and APIs can preserve identity, authorization, rate limits, and audit. Inventory interfaces, identity systems, document sources, and update mechanisms before integration.

Question 13

Can employees see every document?

Accepted Answer

They should not. Enterprise RAG should inherit identity, department, and document permissions and enforce authorization around retrieval. Rules, anomalous access, and departed-user accounts belong in acceptance.

Question 14

What is different for Hong Kong operations?

Accepted Answer

Define data location, user access, and cross-border flows separately for Shenzhen and Hong Kong. Hong Kong privacy requirements differ from mainland rules; confirm the legal boundary before choosing deployment and access paths.

Question 15

How much does private deployment cost?

Accepted Answer

There is no universal price without a workload. Total cost includes compute and facilities, software and licenses, delivery and integration, and ongoing operations. Build a written estimate from model, context, concurrency, availability, and service term.

Question 16

Private deployment or cloud API—which costs less?

Accepted Answer

APIs often suit low-volume or short experiments. Stable high loads, sensitive data, or deep integration justify a private assessment. Compare with your own tokens, peak concurrency, power, staffing, and service horizon.

Question 17

Can we pilot before scaling?

Accepted Answer

Yes, and it is usually safer. Validate quality, latency, permissions, and interfaces with real data, then expand from measured results. Keep model APIs and data flows consistent between pilot and production where possible.

Question 18

How should a project be accepted?

Accepted Answer

Cover function, performance, security, and operations: business quality, first-token latency, output rate, concurrency, permissions, logs, monitoring, backup, upgrade, and rollback. Agree on metrics before implementation.

Enterprise private LLM deployment FAQ

Questions customers ask first

Can we deploy without owning a server?

Can you deliver on site in Shenzhen?

Can you connect WeCom, CRM and OA?

Will the AI see every document?

Equipment, models and timing

Which models can you deploy?

How long does a project take?

Cost, acceptance and operations

What does private deployment cost?

How is acceptance performed?

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