DeepSeek-R1 Model Series: From Light Distillation to Full-Scale

The DeepSeek-R1 model series spans multiple versions from 1.5B to 671B parameters, designed to provide optimized solutions for various tasks and hardware configurations based on parameter scale, computational resources, and inference requirements. As the parameter count increases, the models demonstrate stepped improvements in inference accuracy, capabilities, and use cases, while correspondingly demanding more hardware resources and operational costs. Understanding the specific characteristics and applications of each version helps users select the optimal model for their needs.

"We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models." -- from deepseek

The DeepSeek-R1 series is divided into two categories:

• Distilled Versions: Derived from open-source models (such as Qwen2.5 and Llama series) through knowledge distillation and reinforcement learning optimization. Parameter ranges from 1.5B, 7B, 8B, 14B, 32B to 70B. Each maintains high inference capabilities while significantly reducing resource requirements, suitable for most commercial and small to medium-scale research tasks.
• Full Version: Models like DeepSeek-R1-671B (and DeepSeek-R1-Zero) are trained based on DeepSeek-V3, featuring 671B parameters. These are ideal for high-precision and large-scale AI research, offering performance far superior to distilled versions but with substantially higher hardware costs and deployment complexity.

Note: When articles refer to DeepSeek-R1 series models, all versions except the 671B model are referring to the distilled series. This distinction is often omitted in discussions, which can lead to confusion (for example, DeepSeek-R1-32B actually refers to DeepSeek-R1-Distill-Qwen-32B). Additionally, interested readers can also explore the DeepSeek-V3 vs R1: Model Comparison Guide.

The following table presents key information for each version:

The following table presents the model evaluation for each version:

2.1 Lightweight Distilled Version — DeepSeek-R1-Distill-Qwen-1.5B

• Features: At just 1.5B parameters, this model offers minimal size and rapid inference, ideal for resource-constrained environments.
• Applications: Suitable for real-time applications on mobile devices, older laptops, or even Raspberry Pi devices for simple Q&A and short text generation; runs smoothly in CPU-only mode.
• Benefits: Low cost, simple deployment, perfect for beginners and edge devices.

2.2 Medium-sized Distilled Versions — DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-Llama-8B

• Features: 7B-8B parameters, offering balanced performance and value.
• Applications: Ideal for local development, testing, and medium-complexity tasks like text summarization, translation, and lightweight multi-turn dialogue systems.
• Benefits: Moderate hardware requirements, effective for both high-performance desktops and enterprise applications.

2.3 High-Performance Distilled Version — DeepSeek-R1-Distill-Qwen-14B

• Features: 14B parameters, significantly enhanced accuracy in mathematical reasoning and code generation.
• Applications: Suited for enterprise-level complex tasks like contract analysis, report generation, and long-form writing assistance.
• Benefits: Superior performance in precision-critical professional scenarios, with corresponding hardware requirements.

2.4 Professional and Top-tier Distilled Versions — DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Llama-70B

• Features: 32B and 70B parameters respectively, delivering top-tier inference performance.
• Applications: Ideal for large-scale training, financial forecasting, language modeling, and research tasks with high complexity.
• Benefits: Capable of processing massive datasets, typically requiring server or high-end workstation deployment.

2.5 Ultra-Large-Scale Full Version — DeepSeek-R1-671B

• Features: 671B parameters, optimized through multi-stage reinforcement learning, offering unmatched inference speed and accuracy.
• Applications: Perfect for national-level research, climate modeling, genomic analysis, and AGI exploration requiring extreme precision.
• Benefits: Unparalleled performance, but requires substantial deployment costs and high-end hardware infrastructure, typically limited to large clusters or specialized data centers.

Consider these factors when choosing a model:

• Task Complexity: Lightweight distilled versions (e.g., 1.5B) suffice for simple Q&A and short text generation; 14B+ versions are recommended for mathematical reasoning, code generation, and long-text analysis.
• Hardware Resources: For limited hardware scenarios (low-spec servers or edge devices), prioritize 1.5B to 8B versions; for environments with high-performance servers or multi-GPU clusters, consider 32B, 70B, or even the 671B full version.
• Inference Costs and Response Time: Distilled models offer faster inference and lower operational costs, suitable for real-time commercial applications; the full version is ideal for research projects requiring extreme accuracy and deep reasoning.

Rise CAMP (Computing AI Management Platform) provides comprehensive deployment optimization support for DeepSeek models, ensuring efficient operation across diverse hardware environments while significantly reducing deployment complexity.

Hardware Compatibility and Cross-Platform Support

Rise CAMP's platform architecture supports multiple hardware platforms, including traditional NVIDIA GPUs, Ascend NPUs, Hygon DCUs, and other heterogeneous computing resources. Aligned with DeepSeek's requirements, Rise CAMP dynamically selects optimal hardware architectures for deployment, ensuring peak model performance across various platforms. For instance, high-performance GPU resources are automatically allocated for tasks requiring rapid inference, while CPU or lower-resource compute nodes are utilized for basic inference tasks. This approach provides DeepSeek with highly optimized resource utilization and flexibility.

Inference Efficiency and Cost Optimization

As model parameter counts increase, the demand for hardware resources during inference grows significantly. Large-scale models like DeepSeek-R1-671B may face performance bottlenecks due to insufficient or improperly configured computing resources. Rise CAMP addresses this through intelligent load balancing and elastic scaling technologies, distributing computational loads evenly across multiple nodes to prevent single-point overload and optimize costs. This ensures optimal inference efficiency and response times even under high load conditions while maintaining cost-effectiveness.

Visualization and Monitoring

To help organizations better manage their DeepSeek model deployments, Rise CAMP offers a graphical management interface for real-time monitoring of model performance, resource utilization, task queues, and system health metrics. This visualization approach enhances deployment transparency and enables quick identification of performance bottlenecks or potential issues, improving scheduling efficiency and fault recovery times.

Large-Scale Deployment and Distributed Computing Support

For large-scale AI projects, particularly when deploying massive models like DeepSeek's 671B version, Rise CAMP's distributed computing capabilities are crucial. The platform leverages cluster management and horizontal scaling technologies to support coordinated operation across large-scale nodes. This enables organizations to flexibly scale their compute clusters based on actual needs, handling large data volumes and high-concurrency requests while improving overall computational throughput and inference efficiency.

The DeepSeek-R1 model series, ranging from 1.5B to 671B parameters, offers comprehensive solutions from lightweight applications to large-scale research tasks. The distilled versions (based on Qwen and Llama) deliver efficient inference with lower hardware requirements, meeting most commercial application needs, while the full versions focus on extreme precision and complex tasks, supporting national-level research and large-scale AI exploration. Users can select the most suitable version based on their task complexity, hardware resources, and budget to achieve optimal performance-cost balance.

Rise CAMP's optimization for DeepSeek extends beyond resource scheduling and management, incorporating cross-platform support, automated deployment, fault tolerance, and visualization features to ensure DeepSeek models run efficiently and reliably across various hardware environments. Whether for small businesses or large research institutions, Rise CAMP provides tailored deployment solutions to help organizations maximize the potential of DeepSeek models.