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.

What is the correct sequence for safe adoption?

Watchlist -> candidate retrieval -> controlled eval -> gated promotion — Controlled evaluation plus gates prevents regressions and reduces operational risk.

Build a China Model Radar Pipeline Tutorial | China Frontier Models

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SEQ. 1

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Build a China Model Radar Pipeline

🇨🇳 China Frontier Models⏱ 16 min⭐ 175 BASE XP⌨ HANDS-ON LAB

From News to Deployment Decisions

Most teams fail because they consume updates but never convert them into a repeatable selection process. This lesson gives you a model radar operating system.

The Radar Scorecard

Dimension	Question	Weight
License Fit	Can we deploy commercially in our jurisdictions?	25%
Serving Economics	What is real cost at target QPS + context?	20%
Task Performance	How does it score on our evals (not public leaderboards)?	30%
Ops Reliability	Stable tool-calling, latency variance, error rates?	25%

Reference Integration Pattern

# 1) Track model release changes weekly
hf api list-models --author Qwen --sort last_modified
hf api list-models --author deepseek-ai --sort last_modified
hf api list-models --author zai-org --sort last_modified

# 2) Pull top candidates for controlled eval
hf download Qwen/Qwen3.6-27B --local-dir ./candidates/qwen
hf download deepseek-ai/DeepSeek-V3 --local-dir ./candidates/deepseek

# 3) Serve challenger lane via vLLM
vllm serve Qwen/Qwen3.6-27B --dtype auto --tensor-parallel-size 2

# 4) Run your private eval suite, then promote or rollback

Production Guardrails

Legal gate: License and regional compliance check before any rollout.
Safety gate: Red-team prompt set across your highest-risk flows.
Reliability gate: p95 latency, timeout rate, tool-call success thresholds.
Rollback gate: Canary deployment with instant fallback path.

💡 Practical Rule: Treat every frontier model as a candidate, not a default. Promotion requires passing your own eval and operations gates.

⌨ HANDS-ON LABModel Radar: Weekly Frontier Scan

⭐ +225 XP

Create a lightweight intelligence loop to track Qwen, DeepSeek, GLM, Kimi, InternLM, Baichuan, and Yi for new releases, then rank candidates for production trials.

1Create a watchlist file with your target organizations and priorities.

2Run a retrieval job that pulls latest model metadata and release recency from your watchlist.

3Score top candidates by license, serving cost, context, eval fit, and tool-calling reliability.

lab-sandbox — simulated environment

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

OBJECTIVE 1 / 3 — type "hint" if stuck

KNOWLEDGE CHECK

QUERY 1 // 2

Which scorecard dimension should usually carry the highest weight?

Release popularity

Task performance on your internal evals

Parameter count

Social media hype