DeepSeek: A New Model And New Hopes
When DeepSeek released the open-source AI model, DeepSeek R1, with impressive performance and significantly lower training costs, it garnered immediate attention and rapid adoption. It has outperformed many, if not all, of its competitors’ latest models across many commonly used AI tests. Its model efficiency comes from several architectural choices such as the mixture-of-experts system, multi-head latent attention, memory compression, a mixed precision framework, and other optimization techniques.
The infrastructure needed for inferencing with DeepSeek is far less than what its competitors use — useful deployments of DeepSeek can be done on consumer desktops and laptops. Microsoft announced DeepSeek R1 models for its Windows 11 Copilot+ PCs, and NVIDIA announced that its GeForce RTX 50 Series GPUs can run the DeepSeek family of models, as well.
Key Takeaways: DeepSeek’s Promise Raises AI Aspirations
- This level of efficiency opens generative AI to a much broader audience.
- Organizations now have a choice to size up AI infrastructure that they can both acquire and afford for at least one generative AI model family.
- Tech executives: The bar to participating with generative AI has been set to a new low, and you no longer need to wait or spend enormous sums of money to begin.
- You no longer need bleeding-edge AI infrastructure (data center GPUs, AI servers, high-speed networks) to participate.
- Commonly available commodity IT infrastructure can suffice.
- This is not to say that having the latest GPU or 800-GbE network won’t provide benefits — they definitely will!
