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← Research06

Silicon

Designing the hardware that frontier intelligence demands — custom silicon architectures that push the boundaries of inference speed, energy efficiency, and scale.

Purpose-built silicon that makes frontier intelligence not just possible, but practical — fast enough for real-time, efficient enough for sustainability.
4.8
TB/s Bandwidth
40%
Energy Reduction
85ms
Time to First Token
12K
Tokens/sec
Focus Areas
01
Spatial Dataflow
The W1 architecture: 96 GB HBM3E with 256 MB distributed SRAM, designed for inference from first principles.
02
Energy Efficiency
Co-designed models and silicon achieving 1.87 J/token vs 3.14 J/token on commodity hardware.
03
Memory Architecture
Breaking the bandwidth bottleneck with 12-stack HBM3E and novel interconnect topologies.
04
Scale
512-tile mesh with 1.6 TB/s inter-chip links enabling trillion-parameter model serving.
Publications
2026-03-01
Designing Inference Hardware from First Principles
Why we're building our own chips — and what it means for the future of AI inference.
Webbeon Silicon Team
2026-02-12
Energy-Efficient AI: The Architecture Decisions That Cut Power by 40%
How custom silicon design and architecture-aware model optimization reduce the energy cost of frontier intelligence.
Webbeon Silicon Team
2026-01-25
The Memory Wall Problem and How Custom Silicon Solves It
Breaking the bandwidth bottleneck that limits frontier AI inference — through purpose-built memory architectures.
Webbeon Silicon Team