Official Technical Resources: NVIDIA (page 15)
Official Technical Resources · NVIDIA: 264 entries in total; this page lists entries 211–225.
Providers v2
Use provider profiles to attach credentials, network policy, and refresh metadata to OpenShell sandboxes.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: AI Guardrails · LLM Observability and Tracing · Model Evaluation and Benchmarks
Link last checked: 2026-09-25
Verify the Contents of Published OpenShell Images
Read the SBOM attestation attached to published gateway and supervisor images to audit what each image contains.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: AI Guardrails · LLM Observability and Tracing · Model Evaluation and Benchmarks
Link last checked: 2026-09-25
NVIDIA Riva
Passed the general learning-value prescreen; awaiting item-by-item review of importance, timeliness and replacement relationships.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: Multimodal Models · LLM Inference Optimization · Model Deployment Options
Link last checked: 2026-09-25
NVIDIA Riva
Passed the general learning-value prescreen; awaiting item-by-item review of importance, timeliness and replacement relationships.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: Multimodal Models · LLM Inference Optimization · Model Deployment Options
Link last checked: 2026-09-25
Agents & Sandboxes
Every agent Tier 3 can drive and every environment it can run them in — Docker tuning, local-mode sandbox policy, workspace modes, and readiness checks.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: AI Agent · Tool Calling · LLM Inference Optimization
Link last checked: 2026-09-25
Tier 1: Static and Security Validation
Deterministic quality gates for skills — schema, quality, security, PII, license, code-integrity, Unicode, and script checks, plus optional LLM judging.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: Model Evaluation and Benchmarks · LLM Inference Optimization · Model Deployment Options
Link last checked: 2026-09-25
Tier 2: Semantic Deduplication
Detect redundant content within one skill and overlapping skills across a collection using embedding similarity.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: Model Evaluation and Benchmarks · LLM Inference Optimization · Model Deployment Options
Link last checked: 2026-09-25
Tier 3: Live Agent Evaluation
Run a real agent with and without your skill in a sandboxed environment, then measure Skill Lift across five dimensions.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: AI Agent · Tool Calling · LLM Inference Optimization
Link last checked: 2026-09-25
NVIDIA-Verified Agent Skills
Capability governance for portable AI agent skills: what skills do, how to discover them, and how verification works.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: AI Agent · Tool Calling · LLM Inference Optimization
Link last checked: 2026-09-25
A Trust Pipeline for Agent Skills
How scanning, evaluation, signing, and skill cards work together to make AI agent skills easier to review and safer to deploy.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: AI Agent · Tool Calling · LLM Inference Optimization
Link last checked: 2026-09-25
Evaluate Agent Skills Before Publication
How SkillEvaluator measures whether a skill improves an agent's output, what to ship so it can run, and how to read the result.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: AI Agent · Tool Calling · LLM Inference Optimization
Link last checked: 2026-09-25
Scan Agent Skills Before Installation
Use SkillSpector to detect vulnerabilities, malicious patterns, and policy risks in AI agent skills.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: AI Agent · Tool Calling · LLM Inference Optimization
Link last checked: 2026-09-25
Verify Signed Agent Skills
Use detached OMS signatures to verify the integrity and authenticity of published agent skills.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: AI Agent · Tool Calling · LLM Inference Optimization
Link last checked: 2026-09-25
Speed up inference with SOTA quantization techniques in TRT-LLM #
Passed the general learning-value prescreen; awaiting item-by-item review of importance, timeliness and replacement relationships.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: Model Deployment Options · LLM Inference Optimization · LLM Observability and Tracing
Link last checked: 2026-09-25
Scaling Expert Parallelism in TensorRT LLM (Part 1: Design and Implementation of Large-scale EP) #
Passed the general learning-value prescreen; awaiting item-by-item review of importance, timeliness and replacement relationships.
Evidence use: For understanding GPU computing, training, retrieval, inference serving, reliability or deployment decisions.
Limitations: Directly evidences only the implementation and claims of NVIDIA's technology stack. Recheck versioned interfaces and experimental capabilities before use.
Related concepts: Model Deployment Options · LLM Inference Optimization · LLM Observability and Tracing
Link last checked: 2026-09-25