Large Language Model Tier

Model assetModel ServingModelsarc:LargeLanguageModel

A frontier-scale model tier (e.g., ~405B parameters) that may exceed single-GPU memory and require sharding across interconnected GPUs.

Responsibility. Serves the most complex queries requiring maximum capability.

Also known as: Large model, Ultra-large model, Target Model, Frontier model, Target model

Variant of Foundation LLM abstract

When to choose. Choose for complex queries or premium users where the quality improvement justifies cost and sharding complexity.

Direct neighbourhood (hover for relationship types)

Relationships

deployed on structural

is evaluated by assurance

Token Predictability Analyzer Ch4.4

is optimized by lifecycle

Model Quantizer Ch4.4

alternative to variability

Design guidance

SHOULD be used only when quality gains justify sharding complexity and cost.
SHOULD be reserved for complex planning steps requiring multi-step reasoning and broad world knowledge.

Quantitative guidance

As stated by the sources; verify before use.

Receives ~5% of traffic in ensemble scaling (Ch1.8).
Models >80 GB require multi-GPU sharding (Ch1.8).
Large models: 2-4s P95 latency and $0.03-0.10/query; 8-15 accuracy point advantage on complex reasoning with 2-3x latency and 5-10x cost (Ch3.4).
GPT-4 pricing cited as $0.03/1K input and $0.06/1K output tokens (Ch3.4).

Classification

Patterns: Model sharding
Technologies: Llama-3-405BGPT-4-TurboGPT-4GPT-4 TurboClaude 3GPT-4oNemotron 405B
Quality attributes: Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)

Sources

Ch1.8: T. Nguyen, "Scalability and Production Deployment," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.8. ISBN: 9798244538229.
Ch3.4: T. Nguyen, "Tuning Model Parameters for Production Performance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.4. ISBN: 9798244538229.
Ch3.10: T. Nguyen, "Efficiency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.10. ISBN: 9798244538229.
Ch4.4: T. Nguyen, "Performance Profiling and Optimization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.4. ISBN: 9798244538229.
Ch4.7: T. Nguyen, "Scaling Strategies," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.7. ISBN: 9798244538229.
Ch6.5: T. Nguyen, "Production RAG Systems," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.5. ISBN: 9798244538229.
Ch6.6: T. Nguyen, "Query Decomposition and Adaptive Retrieval," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.6. ISBN: 9798244538229.
Ch8.3: T. Nguyen, "Token Economics and Architecture," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.3. ISBN: 9798244538229.
Ref7.15: "Advanced Agentic AI Optimization Techniques," unpublished reference note (15-Advanced-Agentic-Optimization.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
Ref8.05: "Cost Optimization and Resource Monitoring for Agent Systems," unpublished reference note (05-Cost-Optimization-Resource-Monitoring.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note