AI_ENGINEERING2 Multi-file Components

vLLM High-Throughput Inference Server Setup

Serving high-concurrency LLMs handling hundreds of concurrent user requests with minimum latency.

Author: TRIHEX Architecture Lab

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License: MIT

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Version: 2.1.0

Inert Code Verified · Heuristic Static Security ScannerHeuristic Scanner: LOW Risk

Heuristic static security scanner audited 2 files (2520 bytes). External code. Review before execution.

v2.1.0

Skill Files (2)

Compatible Runtimes

AntigravityClaude CodeCursor

SKILL.md·markdown

# vLLM High-Throughput Inference Server Setup

PagedAttention GPU memory management, continuous request batching, multi-GPU tensor parallelism, and OpenAI-compatible API.

## Core Invariants & Rules
1. Launch vLLM with --gpu-memory-utilization 0.95 and --max-model-len to maximize throughput.
2. Verify input validation and security boundaries on all executions.
3. Keep configurations modular, typed, and auditable.

## Operational Workflow
1. Analyze requirements against existing codebase patterns.
2. Apply standardized implementation templates.
3. Execute verification suites before deployment.