Define the workload
Specify model version, quantisation, input and output lengths, concurrent users and latency targets.
AI / REFERENCE ARCHITECTURE
Local AI Inference
Plan a testable local inference configuration around model size, context and concurrency.
Hardware and application reference imageryBUILD AROUND THE WORKLOAD
Work through the technical requirements
Allocate resources
Estimate weights and KV cache, then check GPU support, system RAM, model-loading space and network access.
Validate experience
Measure time to first token, output rate, concurrency and failures under fixed models and request conditions.
Plan operations
Define access, model licensing, updates, logging and backups; local deployment does not automatically resolve every privacy concern.
REFERENCE WORKFLOW
Define the workflow before the hardware.
- 01Workload
- 02Compute
- 03Data & links
- 04Validate & operate
This is an architecture planning approach, not a completed case study or performance commitment. Agree software, equipment and service scope per project.
What do we need next?
Applications and models, data scale, user or node counts, latency and throughput goals, existing resources, budget and site constraints. Existing measurements help narrow the shortlist.
Turn requirements into a reviewable plan
Discuss selection, bills of materials, samples and procurement on a project basis. Model availability, implementation, installation and validation scope require agreement after requirements review.
Discuss your technical requirements ↗LET’S BUILD YOUR NEXT CONFIGURATION
Discuss your requirements ↗