Model Serving · Software component

LLM Generation Backend

Software componentModel ServingModelsarc:LLMGenerationBackend

An inference backend wrapping an autoregressive LLM generation engine that manages its own iteration-level batching and KV cache internally.

Responsibility. Runs token-by-token LLM generation with engine-internal scheduling inside the inference server.

Also known as: vLLM backend, TensorRT-LLM backend

Variant of Inference Backend abstract

When to choose. Choose for large language model generation, where the engine's continuous or in-flight batching outperforms request-level batching.

Direct neighbourhood (hover for relationship types)

Relationships

deployed on structural

hosts structural

In-Flight Batch Scheduler Ch4.5 Ch4.6 +3
Foundation LLM abstract Ch4.5 Ch7.1A
KV Cache Manager Ch4.6 Ref4.01
Optimized Inference Engine abstract Ch4.6 Ref4.01 +1
Paged KV Cache Allocator Ref7.15
Speculative Decoder Ch7.1A Ref4.01 +3

alternative to variability

Tensor Framework Backend Ch4.5

Design guidance

MUST disable server-level dynamic batching (max batch size unset or 0) and rely on the engine's internal batching.
SHOULD shard models that exceed single-GPU memory across GPUs or nodes only with fast interconnect, accepting communication overhead.

Quantitative guidance

As stated by the sources; verify before use.

Tensor parallelism (TP=4) on a 70B model cut latency from 100 to 40 ms/token, raised throughput from 10 to 25 tokens/s and reduced memory from 140 GB to 35 GB per GPU (Ref7.15 worked example).

Classification

Patterns: Tensor parallelismPipeline parallelismModel sharding across GPUs/nodes
Technologies: vLLMTensorRT-LLMNVIDIA Triton Inference ServerTensorRT-LLM (tensorrtllm backend)
Risks mitigated: Conflicting batching control between server and engine

Sources

Ch4.5: T. Nguyen, "NVIDIA NIM and Triton Inference Server," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.5. ISBN: 9798244538229.
Ch7.1A: T. Nguyen, "Advanced Implementation with Nvidia NEMO Framework and Nvlink," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.1A. ISBN: 9798244538229.
Ref4.01: NVIDIA, "TensorRT-LLM," GitHub repository. Accessed: Sep. 27, 2026. [Online]. Available: https://github.com/NVIDIA/TensorRT-LLM
Ref4.03: M. Zhang, J. Wyman, I. M. Bhosale, and W. Tan, "Scaling LLMs with NVIDIA Triton and NVIDIA TensorRT-LLM using Kubernetes," NVIDIA Technical Blog, Oct. 22, 2024. [Online]. Available: https://developer.nvidia.com/blog/scaling-llms-with-nvidia-triton-and-nvidia-tensorrt-llm-using-kubernetes/
Ref7.04: NVIDIA, "NVIDIA NIM," NVIDIA Docs. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/nim/
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
Ref7.17: "Scaling Agentic AI Systems: Patterns and Strategies," unpublished reference note (17-Scalability-Patterns.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note