Unparalleled Performance

• Sohu delivers 10 times the inference performance of NVIDIA's H100 GPUs.
• Runs large AI models 140 times faster in simulations compared to traditional GPUs.

Revolutionary Architecture

• Utilizes a groundbreaking design that directly embeds the transformer architecture into the chip core, optimizing performance specifically for transformer-based models.
• Achieves unprecedented efficiency by eliminating the need for general-purpose programmability found in GPUs.

Advanced Inference Capabilities

• Supports tree search encoding, enabling parallel comparisons of hundreds of responses for better accuracy and efficiency.
• Implements multicast speculative decoding, allowing real-time generation of new content.

Scalability for Large Models

• Designed to operate trillion-parameter AI models efficiently.
• The system can scale up to support 100 trillion-parameter models, enabling future-proof AI solutions.

Open-Source Software Stack

• Features a fully open-source software stack, allowing for flexibility, customization, and community collaboration.
• Simplifies development and integration for AI applications.

Cost-Effective and Energy-Efficient

• Highly optimized for transformer-based workloads, reducing unnecessary overhead and improving cost-efficiency.
• Uses a streamlined design that minimizes power consumption compared to traditional GPU-based systems.

Real-Time Capabilities

• Enables real-time inference for large models, unlocking potential in applications like content generation, chatbot interactions, and more.

Future-Oriented Design

• Specifically built for transformer-based models, which dominate current state-of-the-art AI applications.
• Poised to handle next-generation AI workloads as models become larger and more complex.

Simplified Hardware and Software Integration

• Features a single-core architecture optimized for efficiency, reducing complexity in deployment.
• The open-source nature of the system makes it easier for developers to adapt and integrate into existing workflows.

Breakthrough Efficiency

• Sohu’s architecture prioritizes high utilization of computational resources, with over 90% FLOPS utilization compared to ~30% in traditional GPUs.
• Designed to solve the compute bottlenecks in large-scale AI inference.