There’s a growing buzz around DeepSeek, a China-based AI company that’s made headlines by releasing open-weight large language models with advanced mixture-of-experts (MoE) architectures. With Nvidia GTC 2025 coming up in March, many developers are exploring how DeepSeek’s methods might influence large-scale AI in the near term. The conversation goes well beyond raw horsepower. Today’s challenge is turning powerful AI engines into fully operational “cars” that businesses and researchers can actually use.
DeepSeek’s Architecture & Innovations
Take DeepSeek-V3 as an example: it relies on 256 experts while only activating eight at a time (32x sparsity), allowing it to command a massive parameter space without dragging down performance. These experts sit on top of Multi-Head Latent Attention (MLA), a mechanism designed to ease the memory strain that typically balloons when models handle large context windows. This is where the “engine vs. car” perspective comes into play. AI models may be the engines, but it’s the right infrastructure that truly brings them to life. This principle is echoed by those who see the MoE framework as part of a larger puzzle, where distributed data centers, optimised interconnects and a holistic approach to middleware and applications complete the vehicle.
Chain-of-Thought Reasoning
DeepSeek’s R1 model adds another dimension: it produces full step-by-step reasoning before arriving at a final answer, making it especially compelling for math, code, and debugging tasks. Such transparency raises the bar for inference, which is the center of AI’s commercial value. Once models shift from the lab to real-world use, the day-to-day load is in serving requests, not just training. Developers who plan to implement chain-of-thought reasoning at scale will want an infrastructure that can handle these inference-heavy demands without bogging down user experiences. 
Data Mobility & GTC Session Spotlight
Hardware and software innovations only solve part of the problem. Bottlenecks often arise in how data moves between different systems, especially at hyperscale. At Nvidia GTC, a session titled “Beyond Silos: Unlock AI’s Full Potential With Petabit-Scale Data Mobility” [EXS74582] by David Hughes (VP Engineering, Stelia) will explore how optimized AI data mobility eliminates latency bottlenecks. Large models like DeepSeek-V3 and R1 need to access distributed datasets swiftly, and achieving that kind of responsiveness demands an architecture focused on throughput. This perspective on hyperscale interconnection may be a guiding theme for developers looking to replicate DeepSeek’s results while keeping latency under control.
Implications for AI Development
Open-weight models let developers customize and deploy solutions on their own hardware, saving money in the long run. But as the AI economy shifts toward execution over experimentation, the real test is whether an organization can scale inference workflows without drowning in compute costs. Techniques like MLA and custom communication scheduling offer partial remedies, yet success also hinges on consistent access to advanced infrastructure. Teams focused solely on tweaking model parameters might miss the bigger picture: robust orchestration of GPUs, data, and networking is critical when real users are hitting systems with thousands of concurrent queries.
Looking Ahead
Nvidia GTC 2025 promises to highlight more breakthroughs in distributed training, memory optimization, and data mobility. Many will be watching DeepSeek to see if it refines its MoE strategy or reveals fresh takes on chain-of-thought refinement. At the same time, Stelia is emerging as the infrastructure layer powering AI-first enterprises. Its emphasis on real-time AI workload acceleration gives developers a pathway to handle massive inference loads. This moment isn’t just about unveiling bigger models — it’s about building the entire vehicle that can harness them effectively.
Anyone following DeepSeek’s work can see how swiftly AI is evolving. The questions around data bottlenecks and scalable inference loom large, but so do the opportunities. By combining pioneering model architectures with the right underlying infrastructure, the vision of open, cost-efficient AI may be closer than we think.
Join Us at Nvidia GTC
If you’re looking to avoid the fate of companies that clung to siloed architectures and missed the hyperscale boat, don’t repeat the past. Instead, discover how to build on the lessons learned by hyperscalers and apply them to the AI revolution.
NVIDIA #GTC2025 Conference Session Catalog
Attend our session, “Beyond Silos: Unlocking AI’s Full Potential with Petabit-Scale Data Mobility,” Tuesday, Mar 18 4:20 PM – 4:35 PM PDT and learn how interconnected, elastic infrastructures are transforming AI at every level. We’ll dissect:
- Why traditional cloud computing creates bottlenecks for AI
- How a petabit-scale platform accelerates data mobility
- The blueprint for building an interconnected compute model
