Distribution, Deployment, Edge, and On-Prem

Definition

Mechanisms and runtimes to serve models across cloud, on-prem, and edge environments with SLAs for latency, throughput, and availability.

Why It Matters

• Meets data residency, cost, and latency constraints
• Enables reliability and control (RBAC, DLP, observability)

2025 State of the Art

• Cloud serverless APIs for elastic burst; on-prem GPU clusters with Triton/TensorRT-LLM
• High-throughput servers (vLLM/TGI) with paged KV caching and batching
• Quantization (4/8-bit) and tensor parallelism for edge and cost

Key Players

• NVIDIA (Triton, TensorRT-LLM, NIM), vLLM, Hugging Face TGI
• Azure OpenAI, Google Vertex AI, AWS Bedrock

Challenges

• Efficient long-context memory and streaming
• Autoscaling with heterogenous workloads and tool calls
• Cost visibility and placement optimization

Reference Architectures

• API Gateway → Inference Server (vLLM/TGI/Triton) → Observability & Safety
• Hybrid: Cloud burst + on-prem steady-state
• Edge: Quantized models with on-device privacy controls

Opportunities

• Scheduling policies (batching, multiplexing, speculative)
• Memory tiering and KV cache placement
• Cost-aware routing and A/B per model family

Design Checklist & Acceptance Criteria

• Select runtime (vLLM/TGI/Triton) and quantify gains
• Implement structured logging, tracing, and redaction
• Validate autoscaling with load tests; test failure injection
• Define placement and region policies; quantify egress

References

• Title: vLLM URL: https://vllm.ai/ Publisher/Vendor: vLLM Project Accessed: 2025-08-14 Version_or_release: provider_reported
• Title: NVIDIA Triton Inference Server URL: https://developer.nvidia.com/nvidia-triton-inference-server Publisher/Vendor: NVIDIA Accessed: 2025-08-14 Version_or_release: provider_reported
• Title: TensorRT-LLM URL: https://developer.nvidia.com/tensorrt-llm Publisher/Vendor: NVIDIA Accessed: 2025-08-14 Version_or_release: provider_reported