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Custom Silicon
Oracle Class
Purpose-built silicon for frontier intelligence.
Not GPUs repurposed. Silicon designed from first principles for AI inference. The W1 architecture: spatial dataflow, 96GB HBM3E, 256MB distributed SRAM. 3nm process. Cars, drones, planes, operating systems — anything that runs on silicon, runs on Oracle Class.
4.8 TB/s
memory bandwidth
40%
energy reduction
85ms
time to first token
3nm
process node
Chip visualization showing a 4x4 tile grid with sequential data-flow lighting representing the W1 architecture.
Capability Map
Oracle Class's W1 architecture spans four silicon capability domains.
Architecture
Design principles
GPUs waste silicon on training-grade precision
→
W1 uses flexible per-layer precision — INT4 to FP16 where it matters
Memory bandwidth is a physics problem
→
12-stack HBM3E with 4096-bit interfaces: 4.8 TB/s
Scale requires data center infrastructure
→
4-chip W1 serves 1T parameters at 52 tok/s per user
Energy efficiency means slower inference
→
1.87 J/token vs 3.14 J/token on commodity GPUs — faster AND greener
Applications
Applications
Autonomous vehicles
Real-time inference at 85ms. No cloud roundtrip. Oracle Class runs Odyssey locally in the vehicle.
Drone fleets
Edge intelligence for swarm coordination. Object Class perception on Oracle Class silicon.
Operating systems
Local AI inference as a first-class OS capability. No API calls, no latency, no privacy leaks.
“The memory wall is a physics problem. We solved it with architecture.”
— Webbeon Silicon Team
In The Field
Real results
W1 FPGA Prototype
85ms TTFT, 210 tok/s on Odyssey. Commodity GPU: 320ms, 74 tok/s.
Energy
1.87 J/token vs 3.14 J/token. At 1B tokens/day: 14.7 kW continuous savings.
Scale
4-chip config serves 1T parameters. 1.6 TB/s inter-chip bandwidth.
See what Oracle Class can do.