REBEL-Quad
A 4nm Peta-Scale AI ASIC offering unmatched throughput per watt, ideal for AI workloads.
REBEL-Quad is a cutting-edge 4nm Peta-Scale AI ASIC designed to tackle the most demanding AI workloads with exceptional efficiency. By blending high-throughput computing capability with immense HBM3E bandwidth, REBEL-Quad stands as a beacon of modern AI processing power, showcasing scalable chiplet architecture to enhance performance per watt significantly. Perfectly suited for data centers and organizations looking to leverage AI’s power, it promises future-proof operations across various applications. Despite its advanced capabilities, REBEL-Quad is engineered for energy efficiency, making it a cost-effective solution for long-term AI deployment strategies. Reinforcing its versatility, it accommodates next-gen large language models (LLMs) and ensures compatibility with popular platforms through its proprietary full-stack software.
The target audience for REBEL-Quad includes companies operating at hyperscale, those involved in AI research, and enterprises seeking robust AI infrastructure. With REBEL-Quad, users can expect streamlined performance and minimal latency, supported by the latest in AI architecture innovation. The modular design allows organizations to scale their operations efficiently, maintaining peak performance at all times.
Pros:
- Exceptional energy efficiency
- Scalable architecture
- High compatibility with existing AI workflows
- Future-proof design for ongoing AI needs
Cons:
- High initial setup costs
- Requires specialized infrastructure
- Potentially complex integration processes
Features:
- High Throughput - Capable of 2,048 TFLOPS for dense computing
- Large Memory Bandwidth - 144GB HBM3E at 4.8TB/s bandwidth
- Scalability - Massive scalability enabled with PCIe Gen5
- Energy Efficiency - 350 ~ 700W power usage ensures efficient operation
Integrations:
- PyTorch - Full-stack support for seamless AI software operations
- vLLM - Efficient handling of large language models
Pricing:
- Not Disclosed - Custom pricing available upon request
Implementation Timeline
Not Disclosed
Onboarding
Not Disclosed
Sandbox
No
API Available
Compliance
Not Disclosed
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