IT leaders must evaluate the cybersecurity and financial implications of using large language models (LLMs) hosted on cloud versus private environments. The DeepSeek R1 model, with its 671 billion parameters, necessitates a substantial hardware setup to run efficiently, especially with Nvidia GPUs. The costs can escalate to well over $250,000 for GPUs alone and potentially more depending on server requirements. While comparative US AI technologies are available, options like Azure offer scalable cloud solutions that may reduce upfront capital expenses. Overall, decision-making revolves around balancing costs with performance needs.
IT leaders face significant costs and cybersecurity risks when considering the deployment of LLMs like ChatGPT and DeepSeek, requiring substantial GPU resources.
Nvidia's H100 GPU, with 80GB of RAM, demonstrates how expensive the transition to private LLM models can be, needing substantial hardware investments.
DeepSeek's R1 model competes with US AI technologies without the latest GPU hardware, though running it still necessitates considerable investment in memory and acceleration.
To run the DeepSeek-R1 model effectively, IT leaders must weigh the costs of GPU hardware and explore private versus public cloud options for viability.
Collection
[
|
...
]