graph LR
subgraph 1["API & Orchestration Layer"]
1__1_1["API & Configuration Interface"]
1__1_2["Build & Compilation Engine"]
1__1_3["Distributed Runtime Orchestrator"]
1__1_4["Model Architecture Registry"]
1__1_5["Data Processing & Result Streamer"]
1__1_6["Memory & Cache Controller"]
1__1_1 -->|"Passes validated build parameters and hardware targets to initiate engine generation."| 1__1_2
1__1_2 -->|"Instantiates model layers and weights to construct the TensorRT network graph."| 1__1_4
1__1_1 -->|"Triggers the initialization of the execution environment and request scheduler based on runtime arg…"| 1__1_3
1__1_3 -->|"Requests page allocations and manages eviction policies for the KV cache during request execution."| 1__1_6
1__1_3 -->|"Forwards processed input tensors for execution and retrieves generation results for streaming."| 1__1_5
1__1_5 -->|"Provides the final processed tokens and streaming responses to the user-facing API endpoints."| 1__1_1
end
subgraph 2["Model Factory & Optimized Layers"]
2__2_1["Hardware-Optimized Layer Primitives"]
2__2_2["Generative & Encoder LLM Architectures"]
2__2_3["Advanced MoE & Multi-Modal Architectures"]
2__2_4["Speculative Decoding Framework"]
2__2_1 -->|"Provides the optimized Attention, MLP, and LinearMethod implementations used to construct standard …"| 2__2_2
2__2_1 -->|"Supplies specialized FusedMoEMethod and routing logic required for high-scale expert architectures …"| 2__2_3
2__2_2 -->|"Standard model architectures are utilized as 'Target Models' within the speculative decoding pipeli…"| 2__2_4
2__2_4 -->|"Often wraps or inherits from standard architectures to create specialized 'Draft' variants (e.g., E…"| 2__2_2
end
subgraph 3["Engine Builder & Graph Optimizer"]
3__3_1["Graph Rewriting Framework"]
3__3_2["Weight Conversion & Quantization Pipeline"]
3__3_3["Multimodal Engine Builder"]
3__3_4["Multimodal Model Adapters"]
3__3_3 -->|"invokes adapters to wrap components"| 3__3_4
3__3_3 -->|"triggers weight loading and quantization passes"| 3__3_2
3__3_3 -->|"applies optimization pipeline to the network graph"| 3__3_1
3__3_2 -->|"informs specific graph rewriting passes with metadata"| 3__3_1
end
subgraph 4["Request Serving & Scheduling"]
4__4_1["Intelligent Request Router"]
4__4_2["Micro-Batch Scheduler"]
4__4_3["Scheduling Policy & Resource Manager"]
4__4_1 -->|"Dispatches incoming requests to the specific scheduler instance of the selected server based on loa…"| 4__4_2
4__4_2 -->|"Queries for feasible batch configurations and requests block allocations to ensure the proposed bat…"| 4__4_3
end
subgraph 5["Runtime Executor"]
5__5_1["Generation Orchestrator"]
5__5_2["Runtime Engine Interface"]
5__5_3["Multimodal Pipeline Manager"]
5__5_3 -->|"Prepares multimodal features and prompt tokens, then delegates the iterative text generation proces…"| 5__5_1
5__5_1 -->|"Invokes the underlying TensorRT engine for forward passes and requests management of GPU memory buf…"| 5__5_2
end
subgraph 6["KV Cache Manager"]
6__6_1["Logical Cache Orchestrator"]
6__6_2["Physical Memory Backends"]
6__6_3["Asynchronous Execution & Resource Management"]
6__6_1 -->|"Maps logical slots to physical memory addresses and manages pool resizing."| 6__6_2
6__6_1 -->|"Schedules data movement (offloading/prefetching) and synchronization events."| 6__6_3
6__6_3 -->|"Executes memory operations (copies/clears) and synchronization on the underlying buffers."| 6__6_2
end
subgraph 7["Evaluation Harness"]
7__7_1["Evaluation Orchestrator"]
7__7_2["Model Runtime Adapters"]
7__7_3["Benchmark Task Suite"]
7__7_3 -->|"invokes orchestrator to start evaluation"| 7__7_1
7__7_1 -->|"instantiates and manages lifecycle of adapters"| 7__7_2
7__7_1 -->|"performs self-modification via patching"| 7__7_1
end
1 -->|"Configures and instantiates model structures based on user arguments."| 2
2 -->|"Provides the high-level computational graph for AOT optimization and compilation."| 3
3 -->|"Produces the serialized engine files required for execution."| 5
4 -->|"Dispatches scheduled micro-batches of requests for inference."| 5
5 -->|"Requests memory slot allocation and manages stateful KV caches during the generation loop."| 6
7 -->|"Triggers inference passes to collect metrics and validate model output."| 5
The TensorRT-LLM architecture follows a decoupled "Build-then-Run" pipeline designed for high-performance GPU inference. The flow begins with the API & Orchestration Layer and Model Factory, which define the computational graph using hardware-optimized Quantized Layers. This graph is then passed to the Engine Builder & Graph Optimizer for ahead-of-time (AOT) compilation into a serialized TensorRT engine. At runtime, the Request Serving & Scheduling component receives incoming requests and organizes them into micro-batches for the Runtime Executor. The executor performs iterative token generation, relying on the KV Cache Manager to maintain the critical GPU memory state (radix-tree based prefix caching) across generation steps. Finally, the Evaluation Harness provides a validation loop to measure the performance and accuracy of the deployed models.
Acts as the primary entry point for the library, handling high-level configuration, argument parsing, and the foundational orchestration of the build and execution workflows.
- API & Configuration Interface — Acts as the primary entry point for users, handling CLI arguments, YAML configurations, and providing an OpenAI-compatible REST interface.
- Build & Compilation Engine — Orchestrates the compilation pipeline that converts Python-defined models into optimized TensorRT engines.
- Distributed Runtime Orchestrator — Manages the execution of compiled engines across distributed GPU clusters.
- Model Architecture Registry — Provides the foundational abstractions and specific implementations for various LLM architectures (e.g., Llama, Qwen).
- Data Processing & Result Streamer — Manages the transformation of raw inputs into tensors and the asynchronous delivery of generation results.
- Memory & Cache Controller — A specialized component focused on GPU memory state management.
Contains the library of supported LLM architectures and the low-level, hardware-optimized primitives (Attention, MLP, Quantization) used to construct them.
- Hardware-Optimized Layer Primitives — Provides the fundamental building blocks of neural networks (Attention, MLP, Embedding) integrated with low-level quantization and routing logic.
- Generative & Encoder LLM Architectures — Contains the standard library of text-based model architectures, including causal decoders (Llama, Mistral) and encoder-only models (BERT).
- Advanced MoE & Multi-Modal Architectures — Handles complex, high-scale architectures including large-scale Mixture-of-Experts (DeepSeek, Qwen) and multi-modal systems (Vision-Language Models, Diffusion Transformers).
- Speculative Decoding Framework — Implements the specialized modeling logic required for speculative decoding.
Implements the AOT compilation pipeline, performing graph rewriting, weight conversion, and hardware-specific optimizations to produce executable TensorRT engines.
- Graph Rewriting Framework — Implements the core AOT graph transformation infrastructure using a pattern-matching system to identify subgraphs and replace them with optimized TensorRT primitives or fused kernels.
- Weight Conversion & Quantization Pipeline — Manages the ingestion and transformation of model weights from external formats into optimized layouts, including QKV splitting and quantization pipelines.
- Multimodal Engine Builder — Acts as the high-level orchestrator for the compilation process, managing the end-to-end flow from parsing CLI arguments to exporting the final TensorRT executable.
- Multimodal Model Adapters — Provides model-specific wrappers and optimization blocks for non-LLM components like vision and audio encoders to bridge them with the TensorRT-LLM builder.
Manages the lifecycle of inference requests in a multi-user environment, including load balancing, request routing, and micro-batch scheduling.
- Intelligent Request Router — Acts as the entry point for the subsystem, managing a pool of inference servers.
- Micro-Batch Scheduler — The core execution orchestrator within a single engine instance.
- Scheduling Policy & Resource Manager — Provides the algorithmic logic and memory management constraints for the scheduler, including GPU memory block allocation and enforcement of utilization policies.
The core execution engine responsible for loading built engines and managing the iterative token-by-token generation loop.
- Generation Orchestrator — Manages the iterative generation loop, including sampling strategies, stopping criteria, and KV cache state.
- Runtime Engine Interface — Provides the low-level bridge to the TensorRT engine.
- Multimodal Pipeline Manager — Orchestrates the end-to-end flow for multimodal models (e.g., LLaVA, CogVLM).
A specialized memory management layer that handles GPU slot allocation and prefix caching (radix tree) to optimize memory reuse during generation.
- Logical Cache Orchestrator — Manages the high-level lifecycle and hierarchy of KV cache units.
- Physical Memory Backends — Provides the hardware-specific memory pools and low-level primitives required to back the logical cache slots.
- Asynchronous Execution & Resource Management — Orchestrates asynchronous GPU operations and manages the lifecycle of transient resources like CUDA streams and events.
Provides integration with benchmarking frameworks to evaluate the accuracy and performance of the optimized models.
- Evaluation Orchestrator — The central control unit that manages the evaluation lifecycle, including environment initialization, runtime patching, and distributed execution coordination.
- Model Runtime Adapters — A translation layer that adapts TensorRT-LLM's optimized inference runtime to the expected API of benchmarking frameworks, encapsulating engine, tokenizer, and sampling logic.
- Benchmark Task Suite — A collection of specialized implementations for various benchmarking datasets, defining data processing, prompt templates, and scoring metrics.