AI inference chip and full-stack software platform for on-premises deployment
Rebellions designs purpose-built inference accelerators (Rebel 100) paired with full-stack software (PyTorch and vLLM native support) targeting enterprises and governments seeking sovereign AI capability. The tech stack reveals deep hardware specialization—Verilog, SystemVerilog, UVM, PCIe Gen 6, RDMA, InfiniBand, TPU/GPU integration—coupled with infrastructure software (Kubernetes, RTOS, SR-IOV, IOMMU). Active projects center on NPU communication stacks, packaging solutions, and automated design flows, while pain points cluster around power delivery, crosstalk mitigation, and communication bottlenecks—typical of early-stage chip companies scaling from prototype to production.
Rebellions builds AI inference hardware and software for on-premises deployment, founded in 2020 and based in Seoul with operations across South Korea and the United States. The company's core product is the Rebel 100, a chiplet-based accelerator optimized for inference workloads, paired with a software stack supporting PyTorch and vLLM out of the box. They position the system around three competitive angles: performance-per-dollar, power efficiency, and the ability for enterprises and governments to retain control over AI infrastructure without cloud dependency. Active deployments span commercial and government sectors.
Hardware design: Verilog, SystemVerilog, UVM, ANSYS HFSS, Keysight ADS. Infrastructure: Kubernetes, KVM, RTOS, IOMMU, SR-IOV, PCIe Gen 6, RDMA, InfiniBand, NVLink. Software frameworks: PyTorch, TensorFlow, vLLM, TensorRT-LLM.
Next-generation chiplet packaging, automated design flows, NPU communication software stacks, performance benchmarking, and D2D subsystem development. Internal challenges include power delivery stability under high-frequency loads and NPU communication bottlenecks.
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Rebellions's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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