HARDWARE. WITHOUT THE GUESSWORK.
The right GPU is more than a bigger number. We’re connecting the specs, the fit and the AI workloads—so you can see the whole picture before you build.
Explore the idea Discover the Dojo →In development / Built for the bigger picture
Hover over a component to explore it, or choose its name above.
The fans push air through the heatsink to carry heat out of the card. The shroud supports the fans and guides airflow; fan size, speed and blade design affect cooling and noise.
A contact plate collects heat from the GPU. Heat pipes spread it through the fin stacks, giving moving air a large surface to cool. Thermal pads can also connect memory and power components to the cooler.
The GPU performs parallel computation for graphics and other workloads. The nearby VRAM holds data such as textures, model weights and intermediate results. Its capacity and bandwidth influence which workloads fit and how quickly data reaches the processor.
The printed circuit board connects the processor, memory and supporting electronics. Power stages regulate incoming power, while the PCIe edge connector carries communication with the computer. The rear bracket supports the display connections.
The backplate stiffens the card and protects the rear of the circuit board. Vent openings can let air pass through the assembly. Some designs also use thermal pads to transfer heat to the backplate; that cooling role varies by card.
EXPLORE GPU NINJA
From your first question to your next build.
01 / CHOOSE YOUR HARDWARE
Explore graphics cards by the memory, power and space your build needs. The early guide brings those tradeoffs together; catalog results will expand as the public data becomes available.
Explore buyer’s guide02 / PLAN YOUR WORKLOAD
Estimate the memory and compute an AI model needs before choosing hardware. Explore how model size, precision and context change the requirements.
Explore workload planner03 / MEASURE YOUR MACHINE
Run a benchmark on your own device to measure memory bandwidth and compute performance. Bring those measurements into the workload planner for a more personal estimate.
Explore browser benchmark04 / CONNECT THE PIECES
Explore a complete hardware configuration and check how its components work together. Review fit, power and workload constraints in one place as the catalog develops.
Explore solution builder05 / UNDERSTAND THE DETAILS
Learn how the tools work, what the estimates mean and where their limits are. Find the methods, data conventions and API plans behind GPU Ninja.
Explore documentation06 / FOLLOW THE BUILD
Read project updates and the thinking behind the tools. Follow GPU Ninja from its first foundations toward a connected hardware and AI resource.
Explore blogTHE IDEA
WHAT COMES NEXT
Useful tools today. A connected catalog at launch. More ways to learn and build after that.
Try the buyer’s guide, workload planner and browser benchmark. Follow the build through the docs and blog.
Explore the connected hardware catalog and public JSON API, with GPU specs, case clearance, power requirements and source provenance.
GPU Ninja Dojo, guided labs and AI assistant integrations will build on the same hardware foundation.
GOOD QUESTIONS
What’s here, what’s coming and how the pieces connect.
Explore the documentationThe connected hardware catalog will be available at launch. For now, explore the early buyer’s guide, workload planner and browser benchmark; catalog-dependent features are limited while the launch data is being prepared.
We’re designing around the connections between hardware: the chip inside a board, the case it fits, and the AI model it could run. The aim is to make those relationships useful to people, applications and AI assistants from the same dataset.
Yes. The public JSON API will be available at launch, with structured hardware data and source provenance for your own tools. Documentation will explain how to use it.
Email signup is not available in this release. You can return to this page to check the roadmap; no account is needed to read it.
