// SYSTEM ONLINE
Understand Artificial Intelligence
one term at a time.
A plain-language field guide to how AI actually works — from tokens and weights to inference and transformers. Toggle between Simple and Complex depending on how deep you want to go.
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- terms defined
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- categories
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- depth levels
01 Learning Tracks
New here, or filling gaps? Take a guided, start-to-finish tour — nuts to bolts. Pick your level and step through it like a slideshow.
02 The Glossary
Search any term, or filter by category. Every entry comes in two depths.
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03 The Inference Lab
When you talk to an AI, your words make a round trip: tokenization → inference → detokenization. Pick a level of detail below and watch it happen.
04 Anatomy of a Model
A model is really just layers of simple math, stacked deep. See the big picture — or open it up to the transformer blocks inside.
05 Mixture of Experts
How a model can be huge but cheap to run: a router sends each token to only a few specialist “experts” instead of the whole network.
06 Inference Engines
The software that actually runs a model to generate text. Server engines squeeze out maximum throughput for many users; local engines make models easy to run on your own machine. Model format matters — most local tools want GGUF, most servers want safetensors.
| Engine | Best for | Model formats | Pros | Cons |
|---|---|---|---|---|
| vLLMserver | High-throughput production serving on GPUs | Safetensors (HF); GPTQ / AWQ / FP8; limited GGUF | Top throughput (PagedAttention + continuous batching); OpenAI-compatible API; broad model support | GPU-focused; heavier to set up; overkill for single-user laptops |
| SGLangserver | High-throughput serving with fast structured output | Safetensors (HF); GPTQ / AWQ / FP8 | Very high throughput (RadixAttention KV reuse); excellent for agents / JSON; OpenAI-compatible | Newer; GPU-focused; smaller ecosystem than vLLM |
| TensorRT-LLMserver | Maximum performance on NVIDIA GPUs | HF / safetensors compiled into TensorRT engines | Fastest on NVIDIA (FP8 / INT4, fused kernels); production-grade | NVIDIA-only; complex compile/build step; less flexible |
| Hugging Face TGIserver | Production serving tied to the Hugging Face ecosystem | Safetensors (HF); GPTQ / AWQ / EETQ | Instant access to HF models; continuous batching; solid tooling | GPU-focused; fewer bleeding-edge perf features than vLLM |
| llama.cpplocal | Local / edge inference almost anywhere, CPU or GPU | GGUF (native) | Runs on nearly any hardware (CPU, Metal, CUDA, ROCm, Vulkan); tiny footprint; huge quant selection | Lower multi-user throughput; mostly text; more manual setup |
| Ollamalocal | Dead-simple local models on any OS | GGUF (own registry; can import safetensors) | One-command install & run; clean local API; cross-platform; built on llama.cpp | Less tunable; GGUF-centric; not for large-scale serving |
| LM StudioGUI | Running local models through a friendly desktop app | GGUF; MLX (Apple Silicon) | No-code GUI + chat; built-in model browser; local OpenAI-compatible server | Desktop app (not for headless servers); closed-source |
| Lemonadelocal | Local LLMs on AMD Ryzen AI PCs (CPU / GPU / NPU) | GGUF (llama.cpp); ONNX / OGA (Ryzen AI) | NPU + iGPU acceleration on Ryzen AI; OpenAI-compatible server; multiple backends | Best on AMD hardware; newer, smaller ecosystem |
| MLX-LMlocal | Fast local inference on Apple Silicon Macs | MLX (converted from safetensors); some GGUF | Optimized for M-series unified memory; supports fine-tuning; open-source | Apple Silicon only; smaller model hub than HF |
// Engines and formats move fast — check each project's docs for the latest support.
07 About
Clanker University exists to make the vocabulary of AI approachable for everyone — students, professionals, the curious, and the skeptical. No gatekeeping, no assumed PhD. Just clear definitions with a depth dial you control.
Every term comes in two depths — flip any card or diagram between Simple and Complex to match what you need.