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.

How many active parameters does Mistral Large 3 use per token?

41B — Mistral Large 3 is a sparse MoE with 675B total but only 41B active parameters per token.

Mistral Model Family Tutorial | The Mistral Ecosystem

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Mistral Model Family

🌬️ The Mistral Ecosystem⏱ 10 min⭐ 100 BASE XP⌨ HANDS-ON LAB

European AI Sovereignty

Mistral AI positions itself as the European leader in open-weight AI, with nearly all models released under the permissive Apache 2.0 license.

The Full Lineup (April 2026)

Model	Params	Architecture	Specialty
Mistral Large 3	675B (41B active)	Sparse MoE	Flagship general-purpose, 256K context
Codestral 2	22B dense	Dense	Code generation & agentic coding
Devstral 2	-	Dense	Frontier agentic dev workflows
Pixtral Large	-	VLM	Vision-language, multimodal
Mistral Small 4	~14B	Hybrid	Unified instruct+reasoning+coding
Ministral 3B/8B/14B	3-14B	Dense	Edge devices, cost-efficient
Magistral Small	24B	Dense	Reasoning-focused (open Apache 2.0)

💡 Key Advantage: Unlike Meta's Llama, Mistral's models have no user-count restrictions. Apache 2.0 means fully unrestricted commercial use for companies of any size.

Running Mistral Models

# Via Ollama
ollama run mistral-large

# Via llama.cpp (GGUF)
llama-server -m mistral-large-3-Q4_K_M.gguf --ctx-size 32768

# Via vLLM (production)
vllm serve mistralai/Mistral-Large-3 --tensor-parallel-size 4

⌨ HANDS-ON LABDeploy Mixtral 8x7B MoE with vLLM

⭐ +200 XP

Configure vLLM to serve Mixtral 8x7B Mixture of Experts with tensor parallelism across GPUs.

1Launch the vLLM server with tensor parallelism 2 and 4k context window.

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

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What license does Mistral Large 3 use?

Llama Community License

Research Only

Apache 2.0

GPL v3