AI Systems and Hardware
AI systems / hardware planning
Design the system around the workload
TBW can discuss the building blocks of a private AI deployment—from a developer workstation to shared inference infrastructure. This is a planning framework, not a product catalog or a statement of available inventory.
Eight connected layers
Each layer depends on the workload and operating environment. The illustrations are concepts, not specific equipment offered for sale.
AI Workstations
Individual development and evaluation where local access and desk-side operations are appropriate.
GPU Servers
Shared compute for multiple workloads, subject to facility, capacity and access design.
Private AI Appliance concepts
An integrated deployment concept, not an available product or published specification.
Inference Infrastructure
Serving, routing, monitoring and capacity planning around approved models.
Storage
Model artifacts, retrieval corpora, snapshots and retention need separate capacity planning.
Networking
Consider ingress, east-west traffic, segmentation and data movement together.
Power/Cooling
Electrical and thermal limits can constrain placement and expansion.
Secure Deployment
Identity, encryption, patching, logging and recovery are part of the system.
What goes into a sizing conversation?
Start with representative use cases and measured acceptance criteria; a component list alone cannot establish capacity, performance or suitability.
- Workload discovery: tasks, data sensitivity, deployment boundaries and owners
- Model size and quantization: candidate model families and accuracy/quality evaluation
- Context length: prompt, document and conversation sizes plus memory impact
- Concurrent users: peak demand, scheduling and acceptable queueing
- RAG requirements: corpus size, ingest cadence, retrieval and data permissions
- Voice requirements: capture, transcription, latency sensitivity and consent
- Agent workloads: tools, parallel steps, approval gates and failure recovery
- VRAM and RAM: model residency, context, batching and host overhead
- Storage: artifacts, embeddings, logs, backups, retention and growth
- Network: user access, data movement, isolation and site connectivity
Configuration classes, if discussed, require stated assumptions and validation on the actual workload. No throughput, latency or cost estimate is implied here.
Local deployment
Equipment under your physical and administrative control may simplify certain data-residency requirements, but adds responsibility for facilities, patching, resilience, access controls and support. Local does not automatically mean private or secure.
Private cloud
Dedicated or logically isolated hosted resources can offer flexible capacity and managed operations, depending on contract and architecture. Verify data location, tenant boundaries, encryption, provider access, egress and incident responsibilities before choosing.
Security belongs in the architecture
Define who may access models and source data, how tools are authorized, where logs reside and how systems are updated and recovered. Review threat models and operational responsibilities alongside capacity.
Explore private AI, AI security and the existing AI infrastructure overview.
Future hardware offerings: Coming Soon
No SKU, stock status, price, lead time, warranty, support level or fulfillment terms are published here. “Request a Quote” describes a future sales path only; no quote form or automated request is active on this page.