Open-Source LLMs for Enterprise
A 65-minute decision guide — Llama, Mistral, Phi, Qwen; Ollama, vLLM, and Azure ML; fine-tuning, cost, and sovereign deployment patterns.
Last updated: 2026-05-19
What you'll learn
By the end of this course you'll be able to:
- Why open-source — and the honest cases where a hosted API still wins
- Model selection across Llama, Mistral, Phi, and Qwen families
- Self-hosting paths: Ollama, vLLM, TGI, Azure ML private endpoints
- Fine-tuning approaches — LoRA, QLoRA, full fine-tunes, and when each fits
- Cost and performance tradeoffs at realistic enterprise concurrency
- Air-gapped and sovereign deployment patterns for regulated environments
Who this is for
Senior engineers, ML platform leads, and tech directors evaluating or operating open-source LLMs at enterprise scale. Especially relevant for teams with sovereignty, residency, or per-token cost pressure — and for regulated sectors (BFSI, healthcare, government) where data simply cannot leave a controlled boundary.
Curriculum
8 chapters · 2 hands-on exercises · capstone challenge
Each chapter ends with the learning objectives ticked off. Quizzes are auto-graded with feedback; exercises are open-ended and produce artifacts you can take to your team.
1. Why open-source — and when not
- Frame the open-source decision around sovereignty, cost curve, and roadmap risk
- Recognize the 3 cases where a hosted API is still the right answer
2. Model selection — Llama, Mistral, Phi, Qwen
- Compare model families on capability, licensing, and multilingual coverage
- Match model size to task — and resist the “bigger is better” default
3. Self-hosting — Ollama, vLLM, TGI, Azure ML
- Pick the right serving stack for your latency, throughput, and ops profile
- Configure vLLM and TGI for production concurrency, not laptop demos
4. Fine-tuning approaches
- Choose between LoRA, QLoRA, and full fine-tuning based on data and goal
- Avoid fine-tuning for problems that retrieval or prompting would solve cheaper
5. Cost and performance tradeoffs
- Model the total cost of ownership — GPU hours, ops, evals, and incident load
- Compare per-token economics against hosted APIs at your real concurrency
6. Air-gapped and sovereign deployment
- Architect deployments that satisfy data-residency and air-gap requirements
- Apply the patterns that actually pass regulator and CISO review
7. Operational considerations
- Plan for model upgrades, security patches, and quantization rollouts
- Design observability across GPU, model, and request layers
Capstone: Capstone: Your open-source LLM decision pack
- Document the model, serving, and fine-tuning decision for a real workload
- Produce the TCO and sovereignty case your steering committee will accept
Capstone deliverable: Every learner who completes this course produces «Your Open-Source LLM Decision Pack» — a tangible artifact you take back to your organization.
Curriculum live · full chapter content rolling out through 2026.
The outline, learning objectives, references, and capstone deliverable are published. Full chapter content (video, narration, exercises) ships progressively. Get notified when each chapter goes live.
References & sources
Built on cited sources — not vibes.
Every course is researched fresh against vendor documentation, regulatory sources, and peer-reviewed work. Sources used in this course:
Meta Llama — Models and Licensing
Meta · Source link
Mistral AI Documentation
Mistral AI · Source link
vLLM Documentation
vLLM Project · Source link
Ollama — Local Model Runtime
Ollama · Source link
Hugging Face Documentation
Hugging Face · Source link
Course details
Track
Builder
Level
Intermediate
Audience
Technical practitioner, Director
Function
IT & Engineering
Industry
Cross-Industry
Stack
Open-source
Paired Gennoor Way phase
innovate, build
Format
video, hands-on, interactive
You finished the course. Now what?
From course to outcome.
Reading this course is step one. The next step is applying it where you work. Here's how Gennoor helps — without the deck, without the pitch.
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From $25k · 6 weeks · production-grade
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