Model architecture
How a model represents information and allocates computation.
TechnologyEfficiency is
Efficiency is
a systems problem.
A language model does not run in isolation. Useful efficiency requires co-design across architecture, training, inference state, runtime, and hardware.
- 01ApplicationsFuture uses
- 02ModelsActive research
- 03RuntimeFuture work
- 04KernelsFuture work
- 05HardwareDeployment foundation
Each layer shapes the possibilities above it.
01 / A connected problemFrom the model
From the model
to the machine.
An architectural idea can change the work a model asks of its hardware. Realizing that potential requires understanding every layer between the model and its deployment.
Training
How efficiently the model learns from data and compute.
Inference state
What must be retained and moved as a model processes a sequence.
Runtime
How model operations are scheduled and executed.
Hardware
How the system maps to the memory and compute of a device.
02 / Our focus todayValidate the architecture.
Validate the architecture.
Then build around it.
Today, ZappyBee is focused primarily on architecture and model research. Real-web transfer completed. Preparing our first ~300M generalist model build.
Runtime and kernel optimization will follow once architectural advantages are validated at larger scale. Our long-term direction combines efficient models with the systems needed to deploy them well.
Future direction
Efficient base models, instruct/chat models, an inference API, private deployment, optimized runtime, and hardware-aware or on-device inference are possible product directions. These products are not available today.
03 / Architecture and scaleWhy not just make
Why not just make
models bigger?
Scaling is a powerful way to improve language models. It also increases demands on training infrastructure, memory, and inference resources.
Architectural efficiency can change the amount of hardware required for a given capability target. We are researching whether different architectural choices can improve that balance, and whether those findings persist as models grow.
Scale remains part of our research. Efficiency determines how deliberately we use it.
Read our research approachLet’s compare notesInterested in efficient
Interested in efficient
AI infrastructure?
We are interested in conversations with researchers, infrastructure teams, hardware companies, potential design partners, and investors working on the economics of language models.