Open source · Platform training
Open-Source LLM & Private AI Training
For: Engineering, infrastructure and data teams in regulated or cost-sensitive organizations.
In short
Open-Source LLM training teaches teams to run AI on their own infrastructure — selecting and serving open models such as Llama, Qwen, Mistral and Gemma, fine-tuning them for domain tasks, and making sound trade-offs between private and API-based AI. It's where AI Anytime's open-source roots run deepest.
What your team will be able to do
- Choose the right open model for each use case
- Serve models efficiently with vLLM, Ollama or TGI
- Fine-tune with LoRA/QLoRA on domain data
- Private RAG and agents inside your network
- Clear cost and performance comparisons vs APIs
Capstone
Deploy a private RAG assistant on an open model inside your environment.
Tools & stack
Curriculum
Program modules
A typical outline — every program is tailored to your team's roles, tools, data policies and use cases after a scoping call.
- 01
Open models landscape
- Llama, Qwen, Mistral, Gemma, DeepSeek
- Licensing and benchmarks
- Hugging Face ecosystem
- 02
Serving
- Quantisation and GPUs
- vLLM, Ollama, TGI
- Scaling and monitoring
- 03
Fine-tuning
- Data preparation
- LoRA / QLoRA
- Evaluation
FAQs
Open-Source & Private LLMs: frequently asked questions
Still have questions? Talk to our team — we reply within one working day.
Should we use open-source LLMs or APIs?
It depends on data sensitivity, scale, latency and team skills. Many enterprises use both. The program gives your team a framework and hands-on experience to decide.
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Next step
Bring Open-Source & Private LLMs to your team.
Tell us who you want to upskill and what outcome you need. You'll get a tailored proposal — curriculum, format, trainers and pricing — within 48 hours.