AI Workstation Solutions
A deliverable local compute path for private inference, fine-tuning, team development, and stable enterprise use.
If you need a working local AI environment, the real issue is not only the GPU. It is whether the whole delivery path is supportable.
Boao Intelligent workstation solutions are designed for teams that want inference, knowledge systems, content generation, internal development, or private AI workloads to run reliably. We focus on proper recommendation, complete delivery, and long-term maintainability rather than shipping a box without follow-through.
Who should consider a workstation first
Once the question shifts from “should we use AI?” to “where should this run reliably?”, a workstation usually becomes a serious option.
Enterprise private AI use cases
Best for companies that want knowledge assistants, Q&A, reporting, or customer-facing AI to run inside a controlled internal environment.
R&D and algorithm teams
Useful for teams that need local inference, fine-tuning, experimentation, preprocessing, and stable iteration workflows.
Content and multimedia teams
Suitable for image, video, digital human, design assistance, and other high-frequency content generation pipelines.
Organizations with stronger compliance needs
A strong fit for education, government, and enterprise buyers that care about data control, offline capability, and long-term maintainability.
Common reasons teams start procurement
These are usually signals that local compute is no longer optional but part of the actual delivery requirement.
Public model API cost is increasing and the team wants to bring high-frequency workloads back to local compute.
Business data cannot be uploaded to external platforms, so inference must run in a private or internal environment.
The team already knows it needs local knowledge, inference, fine-tuning, or media-generation capability, but lacks a stable hardware path.
Procurement needs a supportable business-grade solution instead of a loose DIY machine assembled without delivery accountability.
Typical use cases
Different workloads create very different VRAM, concurrency, thermal, and expansion requirements, so recommendation should start from the scenario instead of budget alone.
Local knowledge and Q&A
Supports internal knowledge retrieval, policy lookup, project material search, and service-assist use cases.
Fine-tuning and experimentation
Useful for LoRA tuning, inference deployment, dataset preparation, evaluation, and technical validation.
Image, video, and digital human production
Supports visual generation, video workflows, digital presenters, media enhancement, and creative collaboration.
AI application development and testing
Suitable for agents, RAG, workflow automation, and business copilot development in internal environments.
Recommended configuration tiers
We recommend thinking in terms of workload stage, not just raw component numbers.
AI Station Basic
Best for validation and smaller teams
Suitable for local inference, lighter fine-tuning, knowledge-base Q&A, and regular content generation.
AI Station Pro
Best for stable production delivery
Suitable for formal enterprise projects, dual-GPU workloads, higher concurrency, and more demanding content pipelines.
AI Station Ultra
Quoted by scenario
Suitable for high-concurrency inference, training experiments, rack deployment, or localization requirements including domestic alternatives.
Delivery scope and buying logic
Enterprise buyers usually care less about a single parts list than whether the machine can be deployed, used, and supported with confidence.
Hardware recommendation and full-machine delivery
System environment and dependency installation
Driver, framework, and common toolchain configuration
Baseline testing, stability validation, and delivery documentation
User onboarding and common-problem guidance
Follow-up support, capacity expansion, and scenario advice
Why not just stay in the cloud?
If workloads are frequent, long-running, or data-sensitive, local compute often makes more sense for controllability, data boundaries, and long-term cost.
Why not build it in-house from parts?
For enterprise buyers, the real issue is compatibility, stability, delivery ownership, after-sales support, and future expansion rather than one-time component pricing.
When do you need a custom build?
If you need higher concurrency, more VRAM, stronger cooling, rack-room deployment, or localization constraints, a custom path is usually the better fit.
Delivery process
From workload confirmation to installation and post-launch support, the goal is to give procurement, IT, and business teams a clear path.
Scenario confirmation
We start by confirming whether the primary goal is knowledge retrieval, fine-tuning, inference service, content generation, or internal R&D.
Configuration recommendation
We recommend a standard or custom path based on budget, VRAM requirement, concurrency, deployment environment, and future growth.
Delivery and deployment
We complete full-machine delivery, environment setup, software installation, baseline testing, and onboarding.
Launch support
As workloads grow, we continue with expansion planning, migration support, and operational optimization.
Budget guidance by procurement tier
A better buying discussion usually starts with workload stage, not just parts. These ranges help business, IT, and procurement teams align on the right starting tier.
Entry validation tier
Usually procured in the RMB 30k to 60k range
Best for local inference, knowledge systems, lighter tuning, and PoC work where the team wants a fast starting point.
Production delivery tier
Usually procured in the RMB 80k to 200k range
Best for multi-user access, dual-GPU workloads, and formal business delivery, often including setup, validation, and onboarding.
Custom expansion tier
Quoted by VRAM, rack, and localization requirements
Best when concurrency, multi-GPU design, rack deployment, or specialized compliance requirements drive the buying decision.
FAQ
Q1 How should an enterprise decide which workstation tier to buy first?
The key variables are primary scenario, VRAM demand, concurrency level, and budget. Entry systems are often enough for local inference and PoC work. Professional systems are more suitable for formal projects and shared team use. Custom systems are better when concurrency, rack deployment, or localization requirements are higher.
Q2 Do AI workstations support private deployment?
Yes. That is one of the main reasons companies buy them. The workstation becomes a controlled environment for models, knowledge assets, and business data.
Q3 Does delivery include environment setup and onboarding?
Yes. We do not only deliver hardware. We also help with baseline environment setup, common toolchains, testing validation, and onboarding support.
Q4 Can the system be expanded later as demand grows?
Yes. We consider expansion during the initial recommendation stage so storage, GPUs, or the overall deployment path can evolve as business demand increases.
Tell us the primary workload and we can help narrow the right starting point.
If you bring expected VRAM demand, team size, deployment environment, and budget range, we can usually recommend a more practical path much faster.