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infocepo.com – Cloud, AI & Labs
Welcome to the infocepo.com portal.
This wiki is intended for system administrators, cloud engineers, developers, students, and enthusiasts who want to:
- Understand modern architectures (Kubernetes, OpenStack, bare-metal, HPC…)
- Deploy private AI assistants and productivity tools
- Build hands-on labs to learn by doing
- Prepare large-scale audits, migrations, and automations
The goal: turn theory into reusable scripts, diagrams, and architectures.
Getting started quickly
Recommended paths
- 1. Build a private AI assistant
- Deploy a typical stack: Open WebUI + Ollama + GPU (H100 or consumer-grade GPU)
- Add a chat model and a summarization model
- Integrate internal data (RAG, embeddings)
- 2. Launch a Cloud lab
- Create a small cluster (Kubernetes, OpenStack, or bare-metal)
- Set up a deployment pipeline (Helm, Ansible, Terraform…)
- Add an AI service (transcription, summarization, chatbot…)
- 3. Prepare an audit / migration
- Inventory servers with ServerDiff.sh
- Design the target architecture (cloud diagrams)
- Automate the migration with reproducible scripts
Content overview
- AI guides & tools : assistants, models, evaluations, GPUs
- Cloud & infrastructure : HA, HPC, web-scale, DevSecOps
- Labs & scripts : audit, migration, automation
- Comparison tables : Kubernetes vs OpenStack vs AWS vs bare-metal, etc.
future
AI Assistants & Cloud Tools
AI Assistants
- ChatGPT
- https://chatgpt.com ChatGPT – Public conversational assistant, suited for exploration, writing, and rapid experimentation.
- Self-hosted AI assistants
- https://github.com/open-webui/open-webui Open WebUI + https://www.scaleway.com/en/h100-pcie-try-it-now/ H100 GPU + https://ollama.com Ollama
- Typical stack for private assistants, self-hosted LLMs, and OpenAI-compatible APIs.
- https://github.com/ynotopec/summarize Private summary – Local, fast, offline summarizer for your own data.
Development, models & tracking
- Discovering and tracking models
- https://ollama.com/library LLM Trending – Model library (chat, code, RAG…) for local deployment.
- https://huggingface.co/models Models Trending – Model marketplace, filterable by task, size, and license.
- https://huggingface.co/models?pipeline_tag=image-text-to-text&sort=trending Img2txt Trending – Vision-language models (image → text).
- https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena Txt2img Evaluation – Image generation model comparisons.
- Evaluation & benchmarks
- https://lmarena.ai/leaderboard ChatBot Evaluation – Chatbot rankings (open-source and proprietary models).
- https://huggingface.co/spaces/mteb/leaderboard Embedding Leaderboard – Benchmark of embedding models for RAG and semantic search.
- https://ann-benchmarks.com Vectors DB Ranking – Vector database comparison (latency, memory, features).
- https://top500.org/lists/green500/ HPC Efficiency – Ranking of the most energy-efficient supercomputers.
- Development & fine-tuning tools
- https://github.com/search?q=stars%3A%3E15000+forks%3A%3E1500+created%3A%3E2022-06-01&type=repositories&s=updated&o=desc Project Trending – Major recent open-source projects, sorted by popularity and activity.
- https://github.com/hiyouga/LLaMA-Factory LLM Fine Tuning – Advanced framework for LLM fine-tuning (instruction tuning, LoRA, etc.).
- https://www.perplexity.ai Perplexity AI – Advanced research and synthesis oriented as a “research copilot”.
AI Hardware & GPUs
- GPUs & accelerators
- https://www.nvidia.com/en-us/data-center/h100/ NVIDIA H100 – Datacenter GPU for Kubernetes clusters and intensive AI workloads.
- NVIDIA 5080 – Consumer GPU for lower-cost private LLM deployments.
- https://www.mouser.fr/ProductDetail/BittWare/RS-GQ-GC1-0109?qs=ST9lo4GX8V2eGrFMeVQmFw%3D%3D GROQ LLM accelerator – Hardware accelerator dedicated to LLM inference.
Open models & internal endpoints
(Last update: 08/12/2025)
The models below correspond to logical endpoints (for example via a proxy or gateway), selected for specific use cases.
| Endpoint | Description / Primary use case |
|---|---|
| ai-chat | Based on gpt-oss-20b – General-purpose chat, good cost / quality balance. |
| ai-translate | gpt-oss-20b, temperature = 0 – Deterministic, reproducible translation (FR, EN, other languages). |
| ai-summary | qwen3 – Model optimized for summarizing long texts (reports, documents, transcriptions). |
| ai-code | gpt-oss-20b – Code reasoning, explanation, and refactoring. |
| ai-code-completion | granite-2b – Fast code completion, designed for IDE auto-completion. |
| ai-parse | gemma2-simpo – Structured extraction, log / JSON / table parsing. |
| ai-RAG-FR | qwen3 – RAG usage in French (business knowledge, internal FAQs). |
| gpt-oss-20b | Agentic tasks. |
Usage idea: each endpoint is associated with one or more labs (chat, summary, parsing, RAG, etc.) in the Cloud Lab section.
News & Trends
- https://www.youtube.com/@lev-selector/videos Top AI News – Curated AI news videos.
- https://betterprogramming.pub/color-your-captions-streamlining-live-transcriptions-with-diart-and-openais-whisper-6203350234ef Real-time transcription with Diart + Whisper – Example of real-time transcription with speaker detection.
- https://github.com/openai-translator/openai-translator OpenAI Translator – Modern extension / client for LLM-assisted translation.
- https://opensearch.org/docs/latest/search-plugins/conversational-search Opensearch with LLM – Conversational search based on LLMs and OpenSearch.
Training & Learning
- https://www.youtube.com/watch?v=4Bdc55j80l8 Transformers Explained – Introduction to Transformers, the core architecture of LLMs.
- Hands-on labs, scripts, and real-world feedback in the CLOUD LAB project below.
Cloud Lab & Audit Projects
The Cloud Lab provides reproducible scenarios: infrastructure audits, cloud migration, automation, high availability.
Audit project – Cloud Audit
Bash audit script to:
- detect configuration drift,
- compare multiple environments,
- prepare a migration or remediation plan.
Example of Cloud migration
Example: migration of virtual environments to a modernized cloud, including audit, architecture design, and automation.
| Task | Description | Duration (days) |
|---|---|---|
| Infrastructure audit | 82 services, automated audit via ServerDiff.sh | 1.5 |
| Cloud architecture diagram | Visual design and documentation | 1.5 |
| Compliance checks | 2 clouds, 6 hypervisors, 6 TB of RAM | 1.5 |
| Cloud platform installation | Deployment of main target environments | 1.0 |
| Stability verification | Early functional tests | 0.5 |
| Automation study | Identification and automation of repetitive tasks | 1.5 |
| Template development | 6 templates, 8 environments, 2 clouds / OS | 1.5 |
| Migration diagram | Illustration of the migration process | 1.0 |
| Migration code writing | 138 lines (see MigrationApp.sh) | 1.5 |
| Process stabilization | Validation that migration is reproducible | 1.5 |
| Cloud benchmarking | Performance comparison vs legacy infrastructure | 1.5 |
| Downtime tuning | Calculation of outage time per migration | 0.5 |
| VM loading | 82 VMs: OS, code, 2 IPs per VM | 0.1 |
| Total | 15 person-days | |
Stability checks (minimal HA)
| Action | Expected result |
|---|---|
| Shutdown of one node | All services must automatically restart on remaining nodes. |
| Simultaneous shutdown / restart of all nodes | All services must recover correctly after reboot. |
Web Architecture & Best Practices
Principles for designing scalable and portable web architectures:
- Favor simple, modular, and flexible infrastructure.
- Follow client location (GDNS or equivalent) to bring content closer.
- Use network load balancers (LVS, IPVS) for scalability.
- Systematically compare costs and beware of vendor lock-in.
- TLS:
- HAProxy for fast frontends,
- Envoy for compatibility and advanced use cases (mTLS, HTTP/2/3).
- Caching:
- Varnish, Apache Traffic Server for large content volumes.
- Favor open-source stacks and database caches (e.g., Memcached).
- Use message queues, buffers, and quotas to smooth traffic spikes.
- For complete architectures:
- https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Architecture
- https://github.com/systemdesign42/system-design System Design GitHub
Comparison of major Cloud platforms
| Feature | Kubernetes | OpenStack | AWS | Bare-metal | HPC | CRM | oVirt |
|---|---|---|---|---|---|---|---|
| Deployment tools | Helm, YAML, ArgoCD, Juju | Ansible, Terraform, Juju | CloudFormation, Terraform, Juju | Ansible, Shell | xCAT, Clush | Ansible, Shell | Ansible, Python |
| Bootstrap method | API | API, PXE | API | PXE, IPMI | PXE, IPMI | PXE, IPMI | PXE, API |
| Router control | Kube-router | Router/Subnet API | Route Table / Subnet API | Linux, OVS | xCAT | Linux | API |
| Firewall control | Istio, NetworkPolicy | Security Groups API | Security Group API | Linux firewall | Linux firewall | Linux firewall | API |
| Network virtualization | VLAN, VxLAN, others | VPC | VPC | OVS, Linux | xCAT | Linux | API |
| DNS | CoreDNS | DNS-Nameserver | Route 53 | GDNS | xCAT | Linux | API |
| Load Balancer | Kube-proxy, LVS | LVS | Network Load Balancer | LVS | SLURM | Ldirectord | N/A |
| Storage options | Local, Cloud, PVC | Swift, Cinder, Nova | S3, EFS, EBS, FSx | Swift, XFS, EXT4, RAID10 | GPFS | SAN | NFS, SAN |
This table serves as a starting point for choosing the right stack based on:
- Desired level of control (API vs bare-metal),
- Context (on-prem, public cloud, HPC, CRM…),
- Existing automation tooling.
Useful Cloud & IT links
- https://cloud.google.com/free/docs/aws-azure-gcp-service-comparison Cloud Providers Compared – AWS / Azure / GCP service mapping.
- https://global-internet-map-2021.telegeography.com/ Global Internet Topology Map – Global Internet mapping.
- https://landscape.cncf.io/?fullscreen=yes CNCF Official Landscape – Overview of cloud-native projects (CNCF).
- https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Wiki – Wikimedia infrastructure, real large-scale example.
- https://openapm.io OpenAPM – SRE Tools – APM / observability tooling.
- https://access.redhat.com/downloads/content/package-browser RedHat Package Browser – Package and version search at Red Hat.
- https://www.silkhom.com/barometre-2021-des-tjm-dans-informatique-digital Barometer of IT freelance daily rates.
- https://www.glassdoor.fr/salaire/Hays-Salaires-E10166.htm IT Salaries (Glassdoor) – Salary indicators.
Advanced: High Availability, HPC & DevSecOps
High Availability with Corosync & Pacemaker
Basic principles:
- Multi-node or multi-site clusters for redundancy.
- Use of IPMI for fencing, provisioning via PXE/NTP/DNS/TFTP.
- For a 2-node cluster:
– carefully sequence fencing to avoid split-brain, – 3 or more nodes remain recommended for production.
Common resource patterns
- Multipath storage, LUNs, LVM, NFS.
- User resources and application processes.
- Virtual IPs, DNS records, network listeners.
HPC
- Job orchestration (SLURM or equivalent).
- High-performance shared storage (GPFS, Lustre…).
- Possible integration with AI workloads (large-scale training, GPU inference).
DevSecOps
- CI/CD pipelines with built-in security checks (linting, SAST, DAST, SBOM).
- Observability (logs, metrics, traces) integrated from design time.
- Automated vulnerability scanning, secret management, policy-as-code.
About & Contributions
For more examples, scripts, diagrams, and feedback, see:
- https://infocepo.com infocepo.com
Suggestions for corrections, diagram improvements, or new labs are welcome. This wiki aims to remain a living laboratory for AI, cloud, and automation.