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Keep sensitive documents and workflows inside infrastructure you control.
AI compute infrastructure
Bring compute
closer to the work.
Custom AI workstations and servers designed around a defined model, dataset, user count and operational goal—with project boundaries made clear before hardware is specified.
Consultation-ledHardware, setup, validation and support scope are defined for each project.
Why local AI?
Keep control closer to the business.
Cloud AI is useful, but it is not automatically the right answer for every workload. Local infrastructure can make sense when privacy, control, steady usage or direct access to the hardware matters.
For sustained use, owned compute can reduce dependence on usage-based cloud billing.
Run supported workloads without sending every request and document across the internet.
Choose models, runtimes, storage and upgrade timing instead of inheriting a provider’s roadmap.
Local is not always better. Bursty workloads and early experiments may belong in the cloud. Framesmith helps determine whether local, cloud or a hybrid approach fits the actual use case.
Possible systems
Local AI is not one product.
The right platform depends on what must fit in memory, how many people will use it, how quickly responses are needed and what data must remain under your control.
Individual AI workstation
Dedicated local experimentation, development, content or analysis for one primary user.
Shared inference server
A network-accessible platform sized for multiple users, concurrent requests and managed workloads.
Private document AI
Compute and storage planning for search, retrieval and generation across an organization’s own document collection.
Evaluation platform
A controlled system for testing models, runtimes and requirements before committing to a larger deployment.
Sizing inputs
The GPU is only part of the system.
Model fit matters, but so do context, concurrency, data handling, storage, power, cooling, networking and the software boundary of the project.
Model & memory
Model family, parameter count, quantization, context requirements and expected runtime overhead establish memory needs.
Users & throughput
Interactive latency, simultaneous requests and total throughput determine whether an individual workstation or shared server makes sense.
Data & privacy
Data sensitivity, document volume, governance requirements, connectivity and cloud tolerance shape local, cloud or hybrid tradeoffs.
Infrastructure & growth
Networking, storage, power, cooling, rack or tower format, expansion and ongoing ownership must be planned together.
Clear project boundaries
Hardware is defined first. Everything else is scoped.
Operating-system setup, drivers, inference runtimes, model loading, networking, retrieval systems, application integration, documentation and support can vary substantially. The proposal will state exactly what Framesmith is delivering directly and where another specialist may be involved.
AI projects
Start with the use case—not a GPU list.
Bring a model, a document collection, an expected user count, an existing proposal or simply the problem you are trying to solve.
Discuss an AI project