Question 1

Is the Open Source AI Academy free?

Accepted Answer

Yes, 100% free. No sign-up, no credit card. All 15 modules, 18 lessons, and 45 quiz questions are accessible immediately. Your progress saves locally in your browser.

Question 2

What topics does the Open Source AI Academy cover?

Accepted Answer

The course covers Transformer architecture, Meta Llama 4, Mistral, DeepSeek, Qwen, Gemma, Hugging Face, quantization (GGUF, AWQ, GPTQ, EXL2), Ollama, llama.cpp, vLLM, SGLang, LoRA/QLoRA fine-tuning, pre-training from scratch, RLHF/DPO/GRPO alignment, and production MLOps with Docker and Kubernetes.

Question 3

What is vLLM?

Accepted Answer

vLLM is the industry-standard engine for high-throughput production LLM serving. It uses PagedAttention for efficient GPU memory management and continuous batching for >90% GPU utilization.

Question 4

Do I need expensive hardware to run open-source models?

Accepted Answer

No. Many models can run on consumer GPUs (e.g., RTX 4090) or even CPUs via llama.cpp. The course covers deployment strategies for every budget, from Raspberry Pi edge devices to multi-GPU production clusters.

Model	Total Params	Active Params	Experts	Context
Scout	109B	17B	16	10M tokens
Maverick	400B	17B	128	1M tokens
Behemoth	~2T	288B	—	Unreleased

Model	Quantization	Min VRAM	Recommended
Scout	Q4_K_M	~48GB	2× RTX 4090 or 1× A100 80GB
Maverick	Q4_K_M	~200GB	Multi-GPU cluster (4-8× A100)

Llama 4: Architecture & Models

The Llama 4 Family

Model Comparison

Mixture-of-Experts Explained

Hardware Requirements