Modern GPUs carry silicon, software, and power overhead built for broader workloads. Inference is bottlenecked by memory bandwidth, not compute. We built the chip and the OS for the one thing that matters: AI inference.
128 Tensor-Linear Engines on TSMC A16 with SoIC-X 3D bonding to 512GB HBM4. No training paths, no graphics legacy. Just the inference dataflow.
In our measured open-stack benchmark configuration: 5.25× to 5.62× the throughput of H100 and 3.71× to 3.98× the throughput of B200, at less than 1/20 the power. Low enough to deploy anywhere. No liquid cooling, no special power circuits, no facility upgrades.
Three execution contexts. Fewer than 8 interrupts/sec under load. The kernel paths Linux disables for latency? We never wrote them. The schedulers it papers over? Not present.
0.5s cold boot to first token. Deterministic memory layout. The stack you'd build if you started from the workload instead of POSIX.
First Transformer Layer Engine stage synthesized and validated on AWS f2.6xlarge (AMD VU47P FPGA). End-to-end Verilator-to-hardware match, against the same reference vectors the simulator validated.