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 can existing OpenAI SDK code work with Ollama?

Change base_url to localhost:11434/v1 — Ollama's API follows the OpenAI specification - just change the base URL and your existing code works.

Ollama Quickstart Tutorial | Ollama: Local AI

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Ollama Quickstart

🦙 Ollama: Local AI⏱ 10 min⭐ 100 BASE XP⌨ HANDS-ON LAB

One-Command LLM Deployment

Ollama is the easiest way to run open-source models locally. It handles downloading, GPU detection, and API serving automatically. As of v0.30, Ollama defaults to Vulkan for broad AMD/Intel support, native GGUF loading without conversions, and integrates agent tool-calling natively.

Getting Started

# Install (macOS/Linux)
curl -fsSL https://ollama.com/install.sh | sh

# Run a model (auto-downloads on first use)
ollama run llama3.3-70b
ollama run mistral-large
ollama run qwen3.5:32b
ollama run gemma4:4b

OpenAI-Compatible API

Ollama exposes an API on localhost:11434 that's compatible with the OpenAI SDK - just change the base URL:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
response = client.chat.completions.create(
    model="mistral-large",
    messages=[{"role": "user", "content": "Explain Docker networking"}]
)
print(response.choices[0].message.content)

Custom Modelfiles

# Modelfile
FROM mistral-small:latest
SYSTEM "You are a senior DevOps engineer. Always provide Docker and Kubernetes examples."
PARAMETER temperature 0.3
PARAMETER num_ctx 32768

Build: ollama create devops-assistant -f Modelfile

🐳 Container Deployment:
docker run -d --gpus all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
docker exec ollama ollama pull mistral-large
Now any app on your network can call http://host:11434/v1/chat/completions

⌨ HANDS-ON LABPull, Run & List Local Models

⭐ +150 XP

Zero to local inference in three commands. Pull a model from the Ollama registry, chat with it, then audit what's on disk.

1Pull a model from the registry (e.g. llama3.3).

2Start an interactive chat with the model you pulled.

3List all models stored locally.

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

What port does Ollama's local API run on by default?

8080

3000

11434

5000