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 training technique did DeepSeek-R1 popularize for reasoning?

GRPO (Group Relative Policy Optimization) — GRPO eliminates the need for a separate critic model, making reinforcement learning for reasoning more memory-efficient.

DeepSeek Architecture — DeepSeek & Reasoning

[ ABORT TO HUD ]

SEQ. 1

DeepSeek Architecture

🔮 DeepSeek & Reasoning⏱ 12 min⭐ 100 BASE XP⌨ HANDS-ON LAB

The DeepSeek Breakthrough

DeepSeek stunned the industry by producing models rivaling GPT-4-class performance at a fraction of the training cost, all released under the MIT license.

Core Innovations

Innovation	What It Does	Why It Matters
DeepSeekMoE	671B total, 37B active per token	Massive quality, efficient inference
Multi-head Latent Attention (MLA)	Compresses KV cache via learned projections	Dramatically reduces memory for long contexts
Multi-Token Prediction (MTP)	Predicts multiple future tokens simultaneously	Denser training signals, better understanding
Auxiliary-loss-free Load Balancing	Balances expert usage without quality penalty	Avoids performance degradation from forced balancing

V3, V3.1/V3.2, R1 & V4 Evolution

DeepSeek-V4 Pro (GA August 15, 2026): Flagship 1.6T total parameter (49B active) MoE architecture with native 1M context window, MLA v2 (Multi-head Latent Attention v2 compressing KV state by 93%), and Multi-Token Prediction (MTP v2) achieving state-of-the-art reasoning and SWE-bench performance.
DeepSeek-V4 Flash / V4.1 Flash: High-speed MoE variants (284B–552B total with compact active parameter budgets) engineered for low-latency production inference and multimodal coding.
DeepSeek-V3 / V3.1 / V3.2 (671B total / 37B active, MIT): Foundational MoE series; V3.1 (Aug 2025) and V3.2 (Dec 2025) unified hybrid thinking/non-thinking modes and introduced DeepSeek Sparse Attention (DSA) for long-context efficiency.
DeepSeek-R1: Pioneering reasoning specialist with visible Chain-of-Thought (<think> tags), trained purely via Large-Scale GRPO (Group Relative Policy Optimization) reinforcement learning that proved models learn reasoning without supervised fine-tuning.
DeepSeek-R1-Distilled: Family of distilled reasoning models (1.5B to 70B) bringing frontier-grade math and algorithmic deduction to consumer GPUs.

🔮 August 2026 DeepSeek-V4 Pro GA: DeepSeek officially achieved General Availability for V4 Pro. Operating under strict hardware constraints, its auxiliary-loss-free load balancing and dual-pipe communication overlap allow near-linear cluster scaling across heterogeneous GPU interconnects, proving architectural optimization triumphs over raw compute scale.

💡 Key Insight: DeepSeek-R1 and V4 demonstrated that pure reinforcement learning (RL) with rule-based outcome verifiers can bootstrap advanced reasoning - transforming open-source alignment and post-training worldwide.

⌨ HANDS-ON LABVerify Multi-Head Latent Attention (MLA) Dimensions

⭐ +200 XP

Inspect DeepSeek-V3 configuration to verify the low-rank KV compression vector dimension (512 vs standard 4096).

1Fetch the DeepSeek-V3 config.json from Hugging Face and filter for kv_lora_rank and routed experts.

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 DeepSeek's Multi-head Latent Attention (MLA) designed to optimize?

Training speed

KV cache memory usage

Tokenizer vocabulary

Dataset quality