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

Does the Open Source AI Academy cover China's latest frontier open models?

Accepted Answer

Yes. The academy includes a dedicated China Frontier Models module covering Qwen, DeepSeek, Z.ai GLM, Moonshot Kimi, InternLM, Baichuan, and Yi, plus a practical model-radar workflow for weekly tracking and gated production adoption.

Question 3

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 4

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 5

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.

Question 6

Does the Open Source AI Academy include hands-on labs?

Accepted Answer

Yes. Hands-on terminal labs are built into the lessons — ollama pull, run, and list models, quantize a model to Q4_K_M, and serve it with vLLM using real, vendor-verified commands, with objectives, hints, solutions, and XP rewards. No GPU or sign-up needed.

Ecosystem	Strength Profile	Open-Weight Footprint	Best Use Cases
Qwen	Broad family: text, multimodal, ASR, agent stacks	Very high (active HF org)	General assistants, multilingual apps, agent pipelines
DeepSeek	Reasoning + coding + MoE efficiency	Very high	Reasoning-intensive workflows, code copilots
Z.ai / GLM	Large-scale GLM family + multimodal operators	High	Long-horizon tasks, multimodal agents, OCR pipelines
Moonshot / Kimi	Large MoE frontier models, strong agentic profile	Growing open model line	Agentic workflows, long-context orchestration
InternLM	Research + tooling stack (LMDeploy, XTuner, OpenCompass)	High	Research labs, eval-heavy enterprises
Baichuan	Domain-specialized strong releases (notably medical)	Moderate	Vertical assistants and domain-specific copilots
01.AI Yi	Efficient bilingual foundations	Stable open family	Bilingual products, compact inference footprints

China Model Ecosystem: Who Leads What

Why This Track Matters

Field Map (Operational View)

What Changed Strategically

Adoption Strategy: 70/20/10