What is the parameter count of DeepSeek's R1 model?

DeepSeek’s R1 model is a Mixture of Experts (MoE) architecture with a total parameter count of 145 billion, of which approximately 2.8 billion parameters are active per token during inference. This design balances computational efficiency with model capacity by dividing the network into specialized “experts” that handle different types of tasks. Unlike dense models, where all parameters are used for every input, MoE systems selectively activate subsets of experts, reducing the computational load while maintaining high performance. For example, in a 16-expert configuration, each token might route through two experts, keeping the active parameter count low compared to the total size.

The parameter count directly impacts the model’s capabilities and resource requirements. The 145 billion total parameters allow R1 to store a vast amount of knowledge and handle complex patterns, making it suitable for tasks like code generation, reasoning, and multilingual understanding. However, the MoE structure ensures that inference costs remain manageable. For developers, this means the model can scale to tackle diverse applications—such as automating code reviews or analyzing large datasets—without requiring the same level of hardware as a dense 145B model. Training such a model would still demand significant resources, but inference can be optimized using techniques like dynamic expert routing and distributed computing.

From a practical standpoint, developers deploying R1 should consider hardware compatibility and latency. The 2.8B active parameters per token suggest that the model can run efficiently on GPUs with sufficient VRAM, such as NVIDIA A100 or H100 instances, though memory bandwidth may still limit throughput. For comparison, a dense model like GPT-3 (175B parameters) uses all parameters for every token, making R1’s MoE approach more efficient for real-time applications. DeepSeek has likely optimized the expert routing logic to minimize overhead, but teams should still benchmark performance for their specific workloads. This architecture exemplifies a trend in large language models: prioritizing smarter parameter utilization over sheer size alone.