Is the Open Source AI Academy free?

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.

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

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.

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

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.

Did Kimi K3 score higher than Claude?

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.

What topics does the Open Source AI Academy cover?

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.

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.

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

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.

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

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.

What restriction does the Llama 3 Community License impose?

Requires separate license above 700M MAU — Meta requires a separate commercial agreement for products exceeding 700 million monthly active users.

Licensing: Open Weights vs Open Source — The Open Source AI Space

[ ABORT TO HUD ]

SEQ. 1

SEQ. 2

Licensing: Open Weights vs Open Source

🌐 The Open Source AI Space⏱ 7 min⭐ 50 BASE XP⌨ HANDS-ON LAB

Understanding AI Model Licenses

Not all "open" models are truly open source. The distinction between open weights and open source is critical for commercial use.

License Comparison

License	Commercial Use	Modify?	Examples
Apache 2.0	✅ Unrestricted	✅ Yes	OpenAI gpt-oss-120b/20b, Gemma 4, Mistral Large 3, Qwen3
MIT	✅ Unrestricted	✅ Yes	DeepSeek-V3 / V3.2, DeepSeek-R1, DeepSeek-V4
Llama Community	⚠️ Restricted >700M MAU	✅ Yes	Llama 4 Maverick & Scout, Llama 3.3 70B, Llama 3.1 405B
Research Only	❌ No	✅ Yes	Some academic models

⚠️ Warning: Meta's Llama 4 and Llama 3.1 / 3.3 Community Licenses require a separate commercial agreement if your product exceeds 700 million monthly active users, and include specific EU regulatory and Acceptable Use Policy clauses.

The Ecosystem Map

Meta: Llama 4 MoE herd (Scout 17B-16E / 109B total with 10M context; Maverick 17B-128E / 400B total with 1M context; internal teacher Behemoth ~2T MoE) alongside dense Llama 3.3 (70B) and Llama 3.1 (405B/70B/8B)
Mistral AI: European sovereignty across Mistral Large 4 (~1T MoE / 52B active), Apache 2.0 Mistral Large 3 (675B / 41B active), Magistral (reasoning), Devstral (SWE agents), Codestral, and Ministral 3 (3B/8B/14B)
DeepSeek: MIT-licensed reasoning and MoE powerhouse — DeepSeek-V4 Pro / Flash (1.6T / 49B active, 1M context), DeepSeek-V3 / V3.1 / V3.2 (671B / 37B active), and DeepSeek-R1 (GRPO reasoning)
Alibaba (Qwen): Qwen3 (235B-A22B & 30B-A3B MoE + dense 0.6B–32B, Apache 2.0), Qwen3-Coder (480B-A35B & Coder-Next 80B-A3B, Apache 2.0), and Qwen 3.5 / 3.8 (27B Apache 2.0 & 2.4T MoE)
Google (Gemma): Gemma 4 (E2B, E4B, 26B-A4B MoE, 31B Dense, up to 256K context) re-licensed to Apache 2.0, succeeding Gemma 3 (1B–27B, custom Gemma Terms of Use)
OpenAI (gpt-oss): Apache 2.0 open-weight reasoning MoE models — gpt-oss-120b (~117B total / 5.1B active, single 80GB GPU via MXFP4) and gpt-oss-20b (~21B total / 3.6B active, 16GB RAM/VRAM) with 131K context and native tool calling

⌨ HANDS-ON LABVerify Open Source AI Definition (OSAID) Compliance

⭐ +150 XP

Compare model weights licenses (e.g. Apache 2.0 vs Meta Community License) using Python huggingface_hub API.

1Extract license and usage restriction flags from the model card via python.

lab-sandbox — simulated environment

INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$

OBJECTIVE 1 / 1 — type "hint" if stuck

KNOWLEDGE CHECK

QUERY 1 // 2

Which license allows fully unrestricted commercial use with no user-count limits?

Llama Community License

Apache 2.0

Creative Commons

GPL v3