Question 1

Is the Open Source AI Academy free?

Accepted Answer

Yes, 100% free. No sign-up, no credit card. All 18 modules, 31 lessons, and 73 quiz questions are accessible immediately. Your progress saves locally in your browser.

Question 2

How is Infinity Open Source AI Academy different from vendor-specific open-weight tutorials?

Accepted Answer

Unlike generic or single-model tutorials, Infinity Tech Stack's Open Source AI Academy bridges global open weights (Meta Llama 4 & 3.3, Mistral Large 3/4, Google Gemma 4, OpenAI gpt-oss-120b/20b) and China Frontier Models (DeepSeek-V4 & R1, Qwen3/3.8, Kimi K3, GLM-5) with 18 comprehensive modules and simulated in-browser terminal labs for Ollama, ggml-org/llama.cpp, vLLM V1, SGLang, Unsloth, and GGUF quantization. It requires zero GPU hardware, no cloud subscriptions, and no sign-up.

Question 3

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 4

Did Kimi K3 score higher than Claude?

Accepted Answer

On several benchmarks, yes. Kimi K3 (July 14, 2026: 2.8T open-weight MoE, 1M context) beat Claude Fable 5 on 6 of 14 shared benchmarks — including BrowseComp (91.2 vs 88.0), SWE Marathon (42.0 vs 35.0), and Terminal-Bench 2.1 (88.3 vs 84.6) — topped the Arena.ai Frontend Code Arena, and edged Claude Opus 4.8 on the Artificial Analysis Intelligence Index (57 vs 56) at about 3.3x lower pricing. Claude Fable 5 won the other 8 benchmarks and leads that index at 60. The academy's China Frontier Models module has a dedicated lesson on the full scoreboard.

Question 5

What topics does the Open Source AI Academy cover?

Accepted Answer

The course covers Transformer architecture, Meta Llama 4, Mistral Large 3/4, OpenAI gpt-oss (120B/20B), DeepSeek-V4/R1, Qwen3/3.8, Google Gemma 4, Hugging Face Transformers v5, quantization (GGUF, AWQ, GPTQ, FP8, MXFP4, EXL2), Ollama, ggml-org/llama.cpp, vLLM V1, SGLang RadixAttention, Unsloth & PEFT LoRA/QLoRA fine-tuning, pre-training from scratch, and TRL RLHF/DPO/GRPO alignment, plus production MLOps with Docker and Kubernetes.

Question 6

What is vLLM?

Accepted Answer

vLLM is the industry-standard open-source engine for high-throughput production LLM serving. Its V1 engine architecture pairs PagedAttention virtual memory paging with zero-overhead Automatic Prefix Caching, continuous batching, FP8/AWQ quantization, and native tool calling (--enable-auto-tool-choice) for >90% GPU utilization.

Question 7

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 8

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.

llama.cpp Engine

// LESSONS IN THIS MODULE