AI Architecture & Systems
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§5.28
Serving Frameworks, Component Ecosystem, and Device Backends
Sources
Original serving outline; corpus mappings motivate backend-specific coverage checks rather than assuming every framework runs on every accelerator.
5.28.1
vLLM: Engine, Scheduler, Worker, and PagedAttention
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5.28.2
SGLang: Scheduler, RadixAttention, and Model Runner
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5.28.3
TensorRT-LLM and NVIDIA Triton Inference Server
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5.28.4
NVIDIA Dynamo, llm-d, and Distributed Inference Orchestration
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5.28.5
Ray Serve and Multi-Stage Serving Pipelines
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5.28.6
llama.cpp, MLX, and CPU/Local Inference
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5.28.7
FlashInfer, NIXL, and Reusable Inference Components
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5.28.8
The Backend Compatibility Matrix: Model, Precision, Kernel, Device, and SDK Version
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5.28.9
The Boundary Between Vendor SDK/Serving Entry Points and General Serving Frameworks
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5.27 Multi-Model, Multi-Tenant, and Adapter Serving
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5.29 RL Post-Training Infrastructure