Built for developers who scale AI
Rebellions delivers the performance and efficiency of custom AI silicon—without compromising the software experience.
From PyTorch and vLLM to Triton, our stack offers a seamless, GPU-like workflow optimized for real-world AI inference at scale.
Unified Inference Stack That Just Works
A GPU-class software experience designed for scalable AI inference
- Native integration with PyTorch, vLLM, and Triton
- Broad model support including MoE and decoder-only LLMs
- Production-proven in high-concurrency, latency-sensitiev environments
PyTorch-Native Inference Engine
Purpose-built for real-world PyTorch workflows
- Graph-mode optimization for hardware-accelerated execution
- Eager mode for flexible experimentation and prototyping
- Distributed inference via Collective Communication Library
- Intelligent precision control across FP32, FP16, FP8, FP6, and FP4
vLLM Serving, Optimized for Concurrency
High-throughput LLM serving with native PyTorch support
- Ideal for high QPS workloads with long-context models
- Standard PyTorch API compatibility
- Hugging Face integration for fast model onboarding
Triton SDK & Developer Tooling
Low-level programmability meets developer-friendly design
- Triton backend with custom kernel programming support
- Intuitive APIs for seamless integration into ML pipelines
- Built-in profiling and debugging tools
- Extensive docs and real-world example codes