Let’s see what happens after you send a prompt. Explore the machines it runs on, follow the work inside a GPU, and learn how one chip becomes part of an AI cluster.
Choose a machine, drag it around, and select a part to see what it does. Try “Guide me” for a short tour.
Hardware studio
GB300 NVL72Blackwell Ultra
Full rack
Preparing your hardware…
Illustrative 3D modelnot exact CAD
Drag to rotate · scroll to zoomTogether
Choose how to explore
Go back a layer, change your view, or follow one illustrative operation.
A whole system. Select an assembly, then step inside.
View
Follow one operation
All components in this layer
What would you like to learn?
Pick a starting point. You can move between these whenever you like.
TokenQuest is independent of NVIDIA. The practice questions are written for this site.
Interactive visual explainers
Tweak the numbers and watch the system react.
Explore 8 production AI infrastructure concepts right here: PagedAttention, the Roofline model, Tensor vs. Pipeline Parallelism, Speculative Decoding, Disaggregated Serving, MIG partitioning, GPUDirect RDMA, and FlashAttention SRAM tiling.
SERVING MEMORY · LIVE ANIMATED EXPLAINER
PagedAttention and continuous batching
Contiguous KV reservation pre-allocates the maximum context length per request, wasting memory on unused future slots. PagedAttention splits the KV cache into fixed-size blocks mapped through a block table so new requests can join as soon as blocks free up.
Tweak parameters live:
Active requests:
Processed tokens/req:
Page block size:
PLAYABLE PREVIEWINTERVIEW TRACKS COMING SOON
AI Infrastructure Interview Arena
Preparing for an AI Infrastructure, ML Systems, or Kubernetes GPU Platform role? Try our live Whiteboard GPU Sizer, diagnose production DCGM & NCCL incidents, and review senior interview flashcards while we build out the full 4-track simulator.
Track 01 · Preview AvailablePreview live
LLM Serving & Inference System Design
Size GPU clusters for 8B to 405B+ models, balance TTFT vs. TPOT SLAs, and design PagedAttention, speculative decoding and disaggregated prefill/decode pipelines.
KV cache memory math & GQA/MLA compression
Roofline analysis: prefill compute vs. decode bandwidth
Disaggregated prefill/decode & KV cache routing
Track 02 · Preview AvailablePreview live
Kubernetes & Multi-Tenant GPU Operations
Architect production Kubernetes GPU clusters with NVIDIA GPU Operator, Dynamic Resource Allocation (DRA), Topology Manager, MIG partitioning and gang scheduling.
Correlating DCGM SM occupancy, tensor active & DRAM active
Handling XID errors (ECC page retirement, NVLink link flaps)
Liquid cooling manifold alerts & thermal power capping
Hands-on labs & arcade
Learn by solving puzzles and running the pipeline.
Jump directly into any of the six interactive quest boards, calculate real 2×2 matrix tiles in the hardware compute lab, or run the 3-lane Token Dash arcade highway.
Learn by playing
Play the inference map
Follow one request with Mira. Watch each step, try it yourself and see what changes inside the GPU.