Research Platform Rankings: Fit Matters

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.

5-slide deck ranks NAIRR, FABRIC, Jetstream2, ACCESS, CloudBank, and OSG for LLMs, cyber-physical, and cybersecurity research. Evidence shows platforms are specialized—not interchangeable—impacting fe

February 5, 20265 slides
Slide 1 of 5

Slide 1 - Executive Framing: Why Ranking Exists

The slide titled "Executive Framing: Why Ranking Exists" emphasizes that platforms are not interchangeable. Its subtitle highlights optimizations for AI scale, cyber-physical systems, or HPC, which impact feasibility, credibility, and reproducibility.

Platforms Are Not Interchangeable

Optimized for AI Scale, Cyber-Physical, or HPC—Impacting Feasibility, Credibility, Reproducibility

Source: Research Infrastructure Comparison Deck

Speaker Notes
Introduce the key message: Platforms are not interchangeable; each optimized for AI at scale, cyber-physical experimentation, or batch HPC—impacting feasibility, credibility, reproducibility. Preview high-level ranking table summary on next slides.
Slide 1 - Executive Framing: Why Ranking Exists
Slide 2 of 5

Slide 2 - LLMs & Foundation Models (Training / Fine-tuning / Inference)

This slide table compares platforms for LLMs and foundation models, evaluating native GPU access, feasible LLM scales, and evidence. NAIRR Pilot and Jetstream2 excel for large-scale pretraining, fine-tuning, and inference with strong GPU support, while CloudBank, ACCESS, FABRIC, and OSG Consortium offer limited or unsuitable capabilities for such workloads.

LLMs & Foundation Models (Training / Fine-tuning / Inference)

PlatformNative GPU AccessLLM Scale FeasibleEvidence
NAIRR PilotA100/H100 via partnersLarge-scale pretraining & fine-tuningNSF NAIRR explicitly targets foundation models and AI at scale
Jetstream2A100 GPUsFine-tuning, multimodal inferenceJetstream2 designed for GPU-native cloud workflows
CloudBankDepends on CSPLimited by creditsCloudBank is a broker, not infrastructure
ACCESSFragmented GPU nodesSmall to medium MLACCESS primarily HPC-focused
FABRICMinimal GPU focusNot intendedFABRIC optimized for networking, not AI
OSG ConsortiumVery limitedNot feasibleOSG optimized for CPU HTC workloads
Speaker Notes
If LLM novelty is claimed but run on OSG or generic ACCESS queues, reviewers will question feasibility and scale.
Slide 2 - LLMs & Foundation Models (Training / Fine-tuning / Inference)
Slide 3 of 5

Slide 3 - Cyber-Physical / GPS / Network-Level Experiments

The slide table compares platforms like FABRIC, NAIRR, Jetstream2, ACCESS, CloudBank, and OSG for cyber-physical, GPS, and network-level experiments, evaluating them on network control, timing fidelity, physical-layer realism, and evidence. FABRIC excels with full control, high timing fidelity, and bare-metal realism for reproducible experiments, while others range from limited (NAIRR) to none (OSG) in these areas.

Cyber-Physical / GPS / Network-Level Experiments

PlatformNetwork ControlTiming FidelityPhysical-layer RealismEvidence
FABRICFull controlHighYes (bare metal)Reproducible experiments
NAIRRLimitedMediumPartialAI-focused
Jetstream2VM-levelMediumNoCloud
ACCESSMinimalLowNoBatch HPC
CloudBankCSPLowNoPublic cloud
OSGNoneLowNoHTC
Speaker Notes
Without FABRIC-like control, GPS spoofing or timing-attack threat models are unrealistic.
Slide 3 - Cyber-Physical / GPS / Network-Level Experiments
Slide 4 of 5

Slide 4 - Cybersecurity, Trust, and Adversarial Research

The slide table evaluates platforms like NAIRR, FABRIC, Jetstream2, ACCESS, CloudBank, and OSG on cybersecurity, trust, and adversarial research, highlighting their security focus, adversarial testing (yes/limited/no), policy alignment (e.g., OSTP, NSF), and supporting evidence. NAIRR and FABRIC show strong cybersecurity emphasis with adversarial testing, while others like Jetstream2, ACCESS, and OSG have limited or minimal focus.

Cybersecurity, Trust, and Adversarial Research

PlatformSecurity FocusAdversarial TestingPolicy AlignmentEvidence
NAIRRAI safetyYesOSTPTrustworthy AI
FABRICNetwork secYesNSF CISEAttack/defense
Jetstream2GeneralLimitedNSFNot cyber
ACCESSMinimalNoNSFCompute-first
CloudBankCSP toolsLimitedNSFVendor-dep
OSGMinimalNoDOE/NSFHTC
Speaker Notes
FABRIC and NAIRR align with cyber and trustworthy AI policies; others lack focus or testing capabilities.
Slide 4 - Cybersecurity, Trust, and Adversarial Research
Slide 5 of 5

Slide 5 - Why the Final Ranking Stands

The slide "Why the Final Ranking Stands" features a table ranking platforms like NAIRR, FABRIC, Jetstream2, ACCESS, CloudBank, and OSG across categories such as LLMs, GPS/Cyber, Cybersec, GPUs, and Control, with scores from 1-5 and supporting evidence from sources like NSF and OSTP. NAIRR tops the rankings with strong scores in LLMs, Cybersec, and GPUs (5s), while FABRIC excels in GPS/Cyber, Cybersec, and Control (5s), and others like OSG lag with mostly 1s.

Why the Final Ranking Stands

PlatformLLMsGPS/CyberCybersecGPUsControlEvidence
NAIRR54553NSF, OSTP
FABRIC25525arXiv, NSF
Jetstream242343NSF
ACCESS21121Docs
CloudBank31131NSF
OSG11111Docs
Speaker Notes
Final ranking reflects evidence-based strengths: NAIRR leads in LLMs & cybersec; FABRIC excels in GPS/cyber-physical control. Others lag in key areas.
Slide 5 - Why the Final Ranking Stands

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