Add 9 new Green AI patterns to the catalogue

[russelltrow]

Summary

This PR contributes 9 new green software patterns focused on AI and ML workloads, authored by Naveen Balani. The patterns cover the full AI lifecycle — from development decisions through to runtime operations — and are structured according to the GSF pattern template.

New patterns

Development

• right-sized-energy-efficient-ai-models.md — Select and optimize AI models appropriately sized for the task to reduce compute, memory, and energy consumption during training and inference.
• data-handling/optimize-data-storage-ai-training.md — Use efficient storage formats, compression, and indexing strategies for AI datasets and embeddings to reduce storage footprint, data transfer, and retrieval compute.
• pre-trained-transfer-learning.md — Fine-tune existing pre-trained models instead of training from scratch to dramatically reduce the compute, energy, and time required for model development.
• select-efficient-ml-frameworks-inference-runtimes.md — Choose ML frameworks and inference runtimes that best match your hardware and workload to reduce compute overhead and improve energy efficiency across training and production inference.
• optimize-agent-orchestration-reduce-model-calls.md — Design agentic AI workflows to minimise redundant model invocations and unnecessary compute through caching, conditional logic, and efficient orchestration patterns.

Architecture

• system-topology/run-ai-models-edge.md — Deploy AI inference on edge devices or local infrastructure to reduce data transfer, network energy use, and reliance on centralised cloud compute.
• system-topology/efficient-hardware-ai-workloads.md — Match AI workloads to the most energy-efficient hardware accelerator or instance type to improve utilisation and reduce energy consumption per inference or training run.
• system-topology/on-demand-execution-ai-agent-workloads.md — Trigger AI and agent workloads only when needed using serverless or event-driven platforms to eliminate idle compute and reduce unnecessary energy consumption.

Operations

• operations/carbon-aware-ai-scheduling.md — Reduce the carbon impact of AI workloads by running them in cloud regions with lower grid carbon intensity and scheduling deferrable jobs during periods of high renewable energy availability.

Pattern structure

Each pattern follows the standard GSF template and includes:

• ## Description — problem context and motivation
• ## Solution — actionable guidance
• ## SCI Impact — mapping to the E, I, M, and R factors of the SCI equation
• ## Cost Impact — compute, infrastructure, and trade-off considerations
• ## Assumptions — preconditions for the pattern to apply
• ## Considerations — trade-offs and caveats
• ## References — citations and further reading

All patterns include a description field in YAML front matter for catalogue indexing, and personas are aligned to the official GSF persona list.

Note on patterns 4A and 4B

The ML frameworks and agent orchestration patterns were originally scoped as a single pattern. Following a review with Naveen Balani, they were split into two focused patterns — one covering execution engine selection (4A) and one covering workflow design for agentic systems (4B) — as the two decisions are made by different personas at different points in development.

Test plan

• Review YAML front matter is valid for all 9 files
• Confirm section order matches GSF template: Description → Solution → SCI Impact → Cost Impact → Assumptions → Considerations → References
• Verify patterns render correctly in the Docusaurus site
• Confirm lifecycle category assignments (Development / Architecture / Operations) are correct for each pattern
• Confirm all personas are drawn from the official GSF persona list
• Review SCI Impact mappings for accuracy against the SCI specification

🤖 Generated with Claude Code

[russelltrow]

@LiyaMath @franziska-warncke @navveenb here are the new AI patterns on the staging website:

https://russelltrow.github.io/gsf-patterns/personas/ai-ml-engineer/