Define the method
Distinguish pretraining, full-parameter fine-tuning and LoRA/QLoRA approaches. Model, sequence length and batch size change requirements.
AI / REFERENCE ARCHITECTURE
Fine-Tuning & Training Nodes
Let data, training methods and scaling strategy guide the hardware.
Hardware and application reference imageryBUILD AROUND THE WORKLOAD
Work through the technical requirements
Memory & compute
Estimate weights, gradients, optimiser state and activations; assess mixed precision, gradient accumulation and checkpointing trade-offs.
Data & interconnect
Assess preprocessing, storage throughput and distributed communication to avoid stalls. Multi-GPU scaling is usually not linear.
Reproducible validation
Test throughput, peak memory and recovery with representative data, recording environments, parameters and checkpoints.
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 ↗