Generated from prompt:

Create a 5-slide executive research infrastructure comparison deck. Slide 1 — Executive Framing (Why Ranking Exists) - One-sentence key message: Platforms are not interchangeable; each is optimized for different computational abstractions (AI at scale, cyber-physical experimentation, or batch HPC), directly impacting feasibility, credibility, and reproducibility. - Show a high-level ranking table as a summary (not evidence). Slide 2 — LLM Capability Comparison (Evidence-Based) Title: LLMs & Foundation Models (Training / Fine-tuning / Inference) Table columns: Platform | Native GPU Access | LLM Scale Feasible | Evidence Rows: - NAIRR Pilot | A100/H100 via partners | Large-scale pretraining & fine-tuning | NSF NAIRR explicitly targets foundation models and AI at scale - Jetstream2 | A100 GPUs | Fine-tuning, multimodal inference | Jetstream2 designed for GPU-native cloud workflows - CloudBank | Depends on CSP | Limited by credits | CloudBank is a broker, not infrastructure - ACCESS | Fragmented GPU nodes | Small to medium ML | ACCESS primarily HPC-focused - FABRIC | Minimal GPU focus | Not intended | FABRIC optimized for networking, not AI - OSG Consortium | Very limited | Not feasible | OSG optimized for CPU HTC workloads Speaker note: If LLM novelty is claimed but run on OSG or generic ACCESS queues, reviewers will question feasibility and scale. Slide 3 — GPS & Cyber-Physical Experimentation (FABRIC Strength) Title: Cyber-Physical / GPS / Network-Level Experiments Table columns: Platform | Network Control | Timing Fidelity | Physical-layer Realism | Evidence Rows: - FABRIC | Full topology control | High | Yes (bare metal) | Enables reproducible cyber-physical experiments - NAIRR Pilot | Limited | Medium | Partial | AI-focused, not network emulation - Jetstream2 | VM-level only | Medium | No | Standard cloud abstraction - ACCESS | Minimal | Low | No | Batch HPC model - CloudBank | CSP-dependent | Low | No | Public cloud constraints - OSG Consortium | None | Low | No | Opportunistic HTC Speaker note: Without FABRIC-like control, GPS spoofing or timing-attack threat models are unrealistic. Slide 4 — Cybersecurity & Trust Research Alignment Title: Cybersecurity, Trust, and Adversarial Research Table columns: Platform | Security Focus | Adversarial Testing | Policy Alignment | Evidence Rows: - NAIRR Pilot | AI safety & trust | Yes | White House OSTP | Explicitly supports trustworthy AI - FABRIC | Network security | Yes | NSF CISE | Designed for attack/defense experiments - Jetstream2 | General-purpose | Limited | NSF | Not cyber-focused - ACCESS | Minimal | No | NSF | Compute-first - CloudBank | CSP tools | Limited | NSF | Vendor-dependent - OSG Consortium | Minimal | No | DOE/NSF | HTC-oriented Slide 5 — Final Ranking (Not Negotiable) Title: Why the Final Ranking Stands Table columns: Platform | LLMs | GPS/Cyber-Physical | Cybersecurity | GPUs | Experimental Control | Primary Evidence Rows: - NAIRR | 5 | 4 | 5 | 5 | 3 | NSF NAIRR, OSTP - FABRIC | 2 | 5 | 5 | 2 | 5 | FABRIC arXiv, NSF - Jetstream2 | 4 | 2 | 3 | 4 | 3 | NSF Jetstream2 - ACCESS | 2 | 1 | 1 | 2 | 1 | ACCESS Docs - CloudBank | 3 | 1 | 1 | 3 | 1 | CloudBank NSF - OSG | 1 | 1 | 1 | 1 | 1 | OSG Docs Tone: executive, evidence-driven, reviewer-aware. Design: clean tables, subtle emphasis on FABRIC and NAIRR strengths.