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 (Llama 3.3, Mistral) and China Frontier Models (DeepSeek R1, Qwen 2.5, Kimi K3, GLM) with 18 comprehensive modules and simulated in-browser terminal labs for Ollama, llama.cpp, vLLM, 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, 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.

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.

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.

Which Gemma 4 model is designed for smartphones and IoT?

Gemma 4 E2B — Gemma 4 E2B at ~2.3B parameters is optimized for resource-constrained devices like smartphones and IoT hardware.

The Global Model Families Tutorial | Qwen, Gemma & Others

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SEQ. 1

The Global Model Families

🌏 Qwen, Gemma & Others⏱ 10 min⭐ 100 BASE XP⌨ HANDS-ON LAB

Beyond Llama & Mistral

Qwen (Alibaba)

Alibaba has adopted a dual strategy. While their May 2026 Qwen 3.7 models were strictly proprietary, the Qwen 3.8 series (August 2026) marks a massive return to open weights. They have also heavily pivoted into robotics with the Qwen-Robot Suite.

Model	Type	Active Params	Best For
Qwen3.8-Max	API-Only	2.4T	Proprietary flagship (Aug 3, 2026)
Qwen3.8-2.4T-A95B	MoE	95B of 2.4T	Open-weights frontier MoE (Aug 12, 2026)
Qwen3.8-27B	Dense	27B	Dense multimodal, Apache 2.0 (Aug 14, 2026)
Qwen3.6-27B	Dense	27B	Consistent high performance (legacy)
Qwen3.6-35B-A3B	MoE	3B of 35B	Ultra-efficient inference (legacy)

Key feature: Thinking/Non-Thinking modes - switch between deep reasoning and fast responses in a single model.

Google Gemma 4

Apache 2.0, edge-optimized with native multimodality:

Gemma 4 E2B: ~2.3B params, smartphones & IoT
Gemma 4 E4B: ~4.5B params, flagship mobile devices
128K context window, 2-bit/4-bit quantization support
Runs on Android, iOS, Raspberry Pi, and in-browser via WebGPU

Other Notable Families

Family	Creator	Standout Feature
Phi-4	Microsoft	Small but mighty (14B rivals 70B models)
Command-R+	Cohere	Optimized for RAG & enterprise search
Yi-Lightning	01.AI	Chinese-English bilingual excellence

🐳 Edge Container: Run Gemma 4 in Docker with Ollama:
docker run -d --gpus all -p 11434:11434 ollama/ollama && docker exec -it $(docker ps -q) ollama run gemma4:4b

⌨ HANDS-ON LABBenchmark Qwen 2.5 Coder Output

⭐ +150 XP

Run Qwen 2.5 Coder in the terminal to generate an optimized binary search function.

1Invoke qwen2.5-coder via ollama with a code generation prompt.

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

What is unique about Qwen 3.6's Thinking/Non-Thinking modes?

Different model files

Toggleable deep reasoning vs fast responses in one model

Only works in Chinese

Requires special hardware