AI appliance or an existing GPU server: which deployment fits your team?
Compare an AI appliance with SI deployment on your existing GPU servers, including hardware readiness, integrations, installation requirements, and operational support.
An internal AI system can arrive as an appliance combining hardware and software, or be deployed through systems integration on GPUs and servers you already own. An appliance is worth considering when you need GPU infrastructure or want to prepare the main components through one adoption process. Existing-server deployment can make sense when your organization already has suitable equipment and an operating team.
Owning a GPU does not establish that the full system is ready. Check model execution, document processing, retrieval, updates, and incident ownership together.
Begin with the workflow and the document collection
Define the questions the system needs to answer before deciding what equipment to install. Finding a component specification, comparing contract clauses, and drafting from previous reports require different inputs and review methods.
Describe representative file formats, document length, tables and scans, initial ingestion volume, update frequency, and concurrent usage. File count alone does not distinguish a large drawing set from short notes. Capacity planning also needs to cover preprocessing, indexing, search, and storage alongside the model.
Compare the two deployment paths
| Area | AI appliance | SI deployment on existing GPUs |
|---|---|---|
| Hardware | Confirm proposed configuration and delivery scope | Confirm actual equipment and available resources |
| Software | Confirm included models, runtimes, and administration | Assess OS, driver, and package compatibility |
| Installation | Prepare space, power, network, and security conditions | Assess changes to the environment and other services |
| Integrations | Distinguish included connections from additional work | Agree access to internal repositories and accounts |
| Support | Identify hardware and software support contacts | Divide responsibilities between IT and the provider |
| Expansion | Confirm additional resources and reconfiguration options | Assess shared capacity and upgrade conditions |
Wissly-in-a-Box combines a standard AI appliance with Wissly. SI deployment on existing infrastructure is scoped around your GPUs, servers, and operating environment. In either case, agree the configuration and support responsibilities for the actual workflow.
Information to gather about an existing server
Prepare more than the hardware model name:
- GPU model, GPU memory, and number of GPUs available for the workload
- CPU, RAM, storage, and currently available capacity
- Operating system, drivers, and container runtime
- Other services using the server and their resource needs
- NAS or file-server access methods and account policies
- Internet access, package import, and update procedures
Even when total GPU memory appears sufficient, competing services affect usable capacity. Record whether tests run on an otherwise idle server or alongside normal workloads. A result without that context is difficult to use for planning.
Clarify what an appliance quote includes
“Integrated AI hardware” does not by itself define document connections, custom development, or ongoing support. Request separate line items for equipment, model and software installation, repository connections, custom integrations, training, and maintenance.
Also confirm installation space, power, networking, and equipment admission procedures. Obtain both the hardware configuration and the software support specification. That makes the incident route clearer if the problem could involve either component.
Evaluate the workflow, not just the equipment name
Use the same representative documents and questions to observe:
- Initial connection and preparation for search.
- Answers and their original document locations.
- Retrieval after a document changes.
- Waiting and response behavior as usage grows.
- System status and responsibility when an error occurs.
Record measured results with the document and user conditions. Performance from a different environment, or a general model benchmark, does not establish performance for your company's workflow.
The RAG PoC evaluation guide provides a way to compare outcomes consistently. Before procurement, connect that evaluation to the enterprise AI security checklist so hardware, data movement, and operational responsibility are assessed as one system.