A neutral, community-driven collection of deployment checklists, infrastructure best practices, runtime diagnostics, and governance frameworks for modern AI / LLM systems.
This repository exists to help teams build reliable, observable, scalable, and cost-efficient AI systems—from Day-0 model preparation, to Day-1 infrastructure setup, to Day-2 production operations.
Deploying AI systems—LLMs, diffusion models, embedding pipelines, or multimodal agents—is fundamentally different from deploying traditional microservices.
GenAI workloads introduce:
- Non-linear batching behavior
- GPU memory fragmentation & KV pressure
- Warmup cycles & cold-start latency
- Tail-latency sensitivity
- Parallelism configuration (TP/PP)
- Autoscaling complexity
- High and unpredictable cost curves
The PIQC Knowledge Base organizes this operational knowledge into clear, reusable, vendor-neutral standards, helping teams achieve:
- 🔧 Correctness
- 🚀 Performance & throughput
- ⚖️ Cost efficiency
- 🔍 Observability & diagnostics
- 🛡️ Security & governance alignment
- 🏗️ Production readiness
All content is:
- Framework-agnostic
- Runtime-neutral
- Cloud-agnostic
- High-level and safe for public discussion
- Designed for real-world teams (ML Eng, MLOps, SRE, Platform Eng, DevOps)
This repository is intentionally model-type agnostic and applies to:
- Large Language Models (LLMs)
- Diffusion and image generation models
- Embedding and retrieval pipelines
- Multimodal AI systems
- Audio, vision, and generative pipelines
The repository includes a top-level, model-agnostic readiness checklist designed for early-stage and pre-production validation.
📄 AI Model Deployment Checklist (v0.1)
📂 CHECKLIST.md
This checklist covers:
- Model identity and constraints
- Compute & GPU planning
- Performance objectives
- Routing and release strategy
- Autoscaling requirements
- Observability and reliability
- Security, compliance, and governance
- Operational ownership and metadata
Use the sections below to explore the full PIQC knowledge base.
The top-level, model-agnostic checklist for validating deployment readiness.
Production-oriented guidance for designing, deploying, and operating efficient, reliable, and cost-optimized AI inference infrastructure, with a focus on runtime behavior and system-level tradeoffs.
📂 ai-infrastructure-best-practices-and-playbooks/
A structured, vendor-neutral framework for evaluating compute health, networking, storage, reliability, scalability, and governance across AI/ML infrastructure environments.
📂 ai-infrastructure-audit-and-readiness-checklist/
A pragmatic compliance and governance framework covering AI accountability, data privacy, transparency, fairness, security, and regulatory readiness, including domain-specific extensions.
📂 ai-governance-and-compliance-checklist/
A structured, end-to-end framework for bringing up a bare-metal AI GPU cluster, covering hardware, networking, orchestration, runtime, observability, security, and operational readiness.
📂 ai-cluster-bringup-checklist/
Conceptual diagnostic categories used to evaluate the correctness, performance, scalability, and cost efficiency of deployed AI/LLM model services.
This checklist informs the future direction of PIQC Advisor diagnostics.
📂 ai-model-deployment-quality-checklist/
A Day-0 → Day-2, cross-functional readiness framework for deploying LLMs using vLLM on Kubernetes, aligned across ML Engineering, MLOps, SRE, Platform, and Security teams.
📂 llm-inference-production-readiness-checklist/
A public, vendor-neutral catalog of static and dynamic runtime signals required to analyze GPU efficiency, batching behavior, latency, autoscaling correctness, and runtime drift in vLLM-based inference systems.
📂 vllm-runtime-metrics-and-observability-guide/
A public, vendor-neutral catalog to identify GPU under-utilization caused by memory pressure, mis-batching, or scheduling errors, and recover lost throughput and cost efficiency.
📂 gpu-utilization-interpretation-guide/
A public, vendor-neutral catalog to detect, diagnose, and mitigate KV cache pressure that silently causes batching collapse, rising latency, and hidden GPU memory exhaustion in vLLM.
A public, vendor-neutral catalog to detect training–serving skew and configuration drift that silently degrade model accuracy, latency, and production reliability.
📂 ml-production-training-serving-playbook/
This project aims to:
- Define industry-aligned operational standards for AI/LLM systems
- Reduce dependence on tribal or undocumented knowledge
- Provide vendor-neutral, cloud-neutral guidance
- Create consistency across teams and organizations
- Establish the foundation for future specs (ModelSpec, RuntimeSpec, PIQC Advisor)
⚠️ No proprietary logic, algorithms, or scoring systems are included.
Everything in this repository is public, safe, and conceptual.
We encourage contributions from practitioners across ML, MLOps, DevOps, SRE, and platform engineering.
You are welcome to propose:
- New checklist items or categories
- Clarifications and refinements
- Real-world deployment examples
- References, documentation, or standards
Please open an Issue or Pull Request to get started.
This knowledge base is maintained by ParalleliQ as part of its open initiative to improve GenAI infrastructure and deployment standards across the industry.
The content is intentionally high-level to:
- Minimize maintenance burden
- Encourage broad adoption
- Avoid exposing proprietary implementation logic
AI deployment is rapidly evolving, and organizations often struggle with:
- Fragmented documentation
- Runtime misconfigurations
- GPU inefficiencies
- Sudden cost explosions
- Unpredictable latency
- Blind spots in observability
- Missing governance controls
- Lack of shared standards
The PIQC Knowledge Base helps teams adopt a common language, reduce repeated mistakes, and move toward more predictable, reliable, and efficient GenAI operations.
This project exists thanks to contributions from engineers, researchers, and practitioners committed to building safer, faster, and more reliable AI systems.
The goal is simple:
Make AI deployment knowledge open, neutral, and accessible to everyone.
Because the project is neutral & community-owned, there are no personal branding links, but you are encouraged to:
- ⭐ Star the repo
- ⬆️ Create issues
- 🔧 Submit PRs
- 🧠 Share it with your team
Together, we can build better AI infrastructure standards.
Thanks for contributing and helping shape better AI infrastructure standards.