Running DeepSeek on-premise involves several factors that impact energy consumption costs. Here's a detailed breakdown of these factors and how they contribute to overall energy expenses:
1. Energy Efficiency of DeepSeek Hardware: DeepSeek is known for its energy-efficient systems, which consume significantly less power compared to other AI providers. For instance, DeepSeek servers use about 1.2 kWh for intensive workloads, whereas similar systems might use up to 1.8 kWh[1]. This efficiency can lead to substantial savings in energy costs, especially for data centers operating continuously.
2. Scalability and Modular Design: DeepSeek's scalable infrastructure allows businesses to start with smaller setups and expand as needed. This modular approach helps avoid unnecessary upfront investments in hardware, which can also reduce overall energy consumption by ensuring that only necessary components are powered[1]. Additionally, the ability to upgrade individual components like GPUs instead of replacing entire systems can further reduce energy usage and costs.
3. Optimized Inference and Training: DeepSeek's architecture, which includes a mixture of experts (MoE) framework and multi-head latent attention (MLA), optimizes computational performance while reducing power consumption[6]. This optimization is crucial for maintaining high performance while minimizing energy use during both training and inference phases.
4. Hardware Requirements: Running DeepSeek models requires significant computational resources, including high-end GPUs and substantial RAM. For example, a setup with a dual EPYC CPU and 384GB of RAM can run certain versions of DeepSeek without a GPU, but more powerful models like the DeepSeek-R1 require substantial GPU power[8][9]. The cost of AI acceleration hardware for DeepSeek-R1 can be around $250,000, and running such models on-premise will consume considerable energy[9].
5. Comparison to Cloud Services: While running DeepSeek on-premise offers full control over data and can be cost-effective in the long term, using cloud services like Azure might provide an alternative with potentially lower upfront costs. However, cloud services incur ongoing operational expenses, including energy costs indirectly passed on through service fees[9].
6. Energy Consumption Reduction: DeepSeek's energy efficiency can reduce power consumption by 50% to 75% compared to competitors, which is particularly beneficial for large-scale deployments[4]. This efficiency not only lowers operational costs but also contributes to a more sustainable AI solution.
In summary, while running DeepSeek on-premise can be cost-effective due to its energy-efficient design and scalable infrastructure, the initial investment in hardware and ongoing energy costs remain significant. The choice between on-premise deployment and cloud services depends on specific organizational needs, including data security, scalability requirements, and budget constraints.
Citations:
[1] https://redresscompliance.com/10-reasons-why-deepseek-hardware-and-technology-is-lower-cost-than-other-ai-providers/
[2] https://www.oneclickitsolution.com/centerofexcellence/aiml/deepseek-models-minimum-system-requirements
[3] https://www.reddit.com/r/ollama/comments/1i9zv0u/deepseek_r1_webui_energy_costs/
[4] https://www.rinnovabili.net/business/markets/deepseeks-energy-consumption-ais-75-power-cut/
[5] https://www.reddit.com/r/LocalLLM/comments/1i6j3ih/how_to_install_deepseek_what_models_and/
[6] https://www.n-ix.com/deepseek-explained/
[7] https://www.gptbots.ai/blog/deepseek-enterprise-on-premise
[8] https://huggingface.co/deepseek-ai/DeepSeek-R1/discussions/19
[9] https://www.computerweekly.com/news/366619398/DeepSeek-R1-Budgeting-challenges-for-on-premise-deployments