AI Model Deployment Cheat Sheet

Updated 2026-04-28

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AI model deployment is the process of integrating trained machine learning models into production environments where they can serve predictions to end users, applications, or other systems. This critical phase bridges the gap between experimental modeling and real-world business value, transforming notebooks and research artifacts into scalable, reliable inference services. In 2026, LLM-specific inference engines such as vLLM and SGLang have become foundational infrastructure, while cloud platforms, edge devices, and disaggregated serving architectures expand the deployment landscape further. Successful deployment requires careful orchestration of model packaging, serving infrastructure, monitoring, and operational workflows, with modern practices emphasizing automation, observability, and resilience — treating models as first-class software artifacts that evolve through continuous integration and delivery pipelines.

What This Cheat Sheet Covers

This topic spans 17 focused tables and 128 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Model Serving FrameworksTable 2: Deployment PatternsTable 3: API Protocols & EndpointsTable 4: Containerization & OrchestrationTable 5: Cloud ML PlatformsTable 6: Model Optimization TechniquesTable 7: Inference ModesTable 8: Monitoring & ObservabilityTable 9: Deployment Strategies & WorkflowsTable 10: Model Serving InfrastructureTable 11: Security & AuthenticationTable 12: Model Packaging FormatsTable 13: LLM & High-Performance Inference OptimizationTable 14: Performance Optimization (General)Table 15: Edge & Mobile DeploymentTable 16: Model Drift & Data Quality MonitoringTable 17: Specialized Deployment Considerations

Table 1: Model Serving Frameworks

Framework	Example	Description
vLLM	`vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000`	• Most widely adopted LLM inference engine • uses PagedAttention for GPU memory efficiency and continuous batching for high throughput — up to 24x over naive HuggingFace Transformers.
SGLang	`python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B --port 8000`	• LLM inference engine with RadixAttention for automatic prefix caching across multi-turn and RAG workloads • 29% higher throughput than vLLM on 8B models; excels at structured outputs.
NVIDIA Triton Inference Server	`tritonserver --model-repository=/models`	• Multi-framework inference server (TensorFlow, PyTorch, ONNX, TensorRT) with dynamic batching, concurrent execution, and GPU optimization • renamed NVIDIA Dynamo Triton in 2025.
TensorFlow Serving	`docker run -p 8501:8501 tensorflow/serving --model_base_path=/models/my_model`	• Production-ready serving for TensorFlow models with gRPC/REST APIs, model versioning, and high throughput • optimized for the Google ecosystem.
TorchServe	`torchserve --start --model-store model_store --models resnet=resnet.mar`	Official PyTorch model server supporting multi-model serving, logging, metrics, and custom handlers with `.mar` model archives.
BentoML	`bentoml serve iris_classifier:latest`	Framework-agnostic platform for packaging ML models into production services with containerization, APIs, and cloud deployment integration.

Table 1: Model Serving Frameworks

Framework	Example	Description
vLLM	`vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000`	• Most widely adopted LLM inference engine • uses PagedAttention for GPU memory efficiency and continuous batching for high throughput — up to 24x over naive HuggingFace Transformers.
SGLang	`python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B --port 8000`	• LLM inference engine with RadixAttention for automatic prefix caching across multi-turn and RAG workloads • 29% higher throughput than vLLM on 8B models; excels at structured outputs.
NVIDIA Triton Inference Server	`tritonserver --model-repository=/models`	• Multi-framework inference server (TensorFlow, PyTorch, ONNX, TensorRT) with dynamic batching, concurrent execution, and GPU optimization • renamed NVIDIA Dynamo Triton in 2025.
TensorFlow Serving	`docker run -p 8501:8501 tensorflow/serving --model_base_path=/models/my_model`	• Production-ready serving for TensorFlow models with gRPC/REST APIs, model versioning, and high throughput • optimized for the Google ecosystem.
TorchServe	`torchserve --start --model-store model_store --models resnet=resnet.mar`	Official PyTorch model server supporting multi-model serving, logging, metrics, and custom handlers with `.mar` model archives.
BentoML	`bentoml serve iris_classifier:latest`	Framework-agnostic platform for packaging ML models into production services with containerization, APIs, and cloud deployment integration.