C1004B

Product Introduction

The Cephalon C1004 Series is an AI large-model inference appliance developed independently by the Cephalon team. It is the world's first localized device capable of running massive language models such as DeepSeek R1 / V3 (671B parameter scale), enabling true "Full-Performance Version" private deployment.

By overcoming the high costs and complexity associated with traditional GPU cluster deployments, the C1004 Series incorporates cutting-edge engineering and hardware-software co-design, offering enterprises and research institutions a cost-effective, scalable, and secure platform for private large-model deployment.

— This is the most powerful large language model and AI agent server on the planet!

Breaking with tradition

• Traditional GPU Solution：Requires nearly 8 A100 or H20 GPUsCost exceeds 1 million RMBAffordable only for a few industry giants
• C1004B Solution：Only 1 RTX 5090 GPU requiredPaired with Intel Xeon Platinum ×2, 104 cores, 208 threadsAchieves equivalent—or even superior—performance at just 1/10th of the cost

Performance Advantages

Cephalon Self-developed inference engine framework with optimized hardware selection.

1. Form Factor: Rack-mounted, designed for indoor deployment.
2. Parameters: Fully supports full-performance, full-precision R1 and V3 versions; theoretically compatible with models up to 1 trillion parameters.
3. Configuration: Intel Xeon Platinum ×2 (104 cores, 208 threads)➕1 × RTX 5090 high-performance GPU
4. Decoding Throughput: Minimally affected by context length, consistently maintaining around 20 tokens per second (TPS).
5. Supported Context Length: ≤64K tokens
6. Single-Node Concurrent Throughput: Supports up to 8 parallel streams.
7. Model Compatibility: Compatible with mainstream frameworks including DeepSeek, LLaMA, Qwen, Kimi, etc.

Core Technologies

Technical BreakdownThe system utilizes core technologies such as operator fusion, efficient memory management, and computational graph optimization.

1. Operator Fusion: Increases computational efficiency.
2. Efficient Memory Management: Optimizes memory allocation and usage, preventing waste and conflicts.
3. Computational Graph Optimization: Enables more efficient data flow during computation.

Real-World PerformanceEmpirical tests demonstrate that, under the same hardware conditions, inference efficiency for the 671B parameter model is improved by over 50%, significantly outperforming general solutions. 

In real-world applications, these technological advantages yield substantial benefits. For instance, in intelligent customer service scenarios, the system quickly understands complex user queries and provides accurate responses in real time. When users ask about various aspects of a product, the C1004B model rapidly analyzes and reasons through the question, retrieves relevant information from the knowledge base, and generates clear, coherent responses—greatly enhancing customer satisfaction and operational efficiency