Nvidia (NVDA.US) GTC Conference Preview: Can the AI Dominance Hold Its Ground? Market Watches Closely for "Post-Training Era" New Strategy

TechNews APP has learned that as the annual NVIDIA (NVDA.US) GTC Developer Conference is set to kick off next week, this event, hailed as the “Annual Benchmark in AI,” has seen even greater excitement and importance this year. When CEO Jensen Huang steps into the packed ice hockey arena next Monday (March 16, local time), global investors will be watching closely to see what strategies he will deploy to respond to intensifying market competition and to reinforce NVIDIA’s position as a leader in AI chips.

This four-day GTC conference is not only a platform for NVIDIA to showcase its latest advancements in chips, data centers, CUDA software platform, AI agents, and robotics in physical AI, but also a critical test of the company’s strategic direction. After delivering better-than-expected earnings but failing to significantly boost its stock price, investors are eager for reassurance: NVIDIA’s profit-sharing strategy for AI ecosystems is beginning to pay off.

Market research firm eMarketer analyst Jacob Bourne said, “I expect NVIDIA to present an updated full-stack roadmap from Rubin to Feynman, with a focus on reasoning, intelligent agents, networking technology, and AI infrastructure.”

“Post-Training Era” Competition Focus: Inference Chips

As the AI industry transitions from the “training” phase of large models to the “inference” phase where AI agents perform tasks in applications, the competitive landscape is undergoing profound changes. Although NVIDIA currently holds over 90% of the training and inference markets, analysts generally believe that market share loss is inevitable, especially in inference.

Sid Sheth, founder and CEO of inference chip startup d-Matrix, said that while NVIDIA will remain dominant in training, “Inference is a whole different ballgame.” He added that CUDA, NVIDIA’s core software supporting most AI training and locking developers into its ecosystem, is less of a moat in inference. Developers can turn to competitors outside NVIDIA because running pre-trained AI models doesn’t require the complex programming needed for training.

To address this trend, NVIDIA is expected to launch new products optimized for inference workloads at the conference. Reports suggest that an inference chip integrating technology from Groq, an AI startup acquired for $1.7 billion in December, may debut. This chip aims to deliver fast, cost-effective inference computing. Groq’s ultra-fast AI technology will be integrated into NVIDIA’s extensive CUDA ecosystem to strengthen its software moat.

Potential Threats and NVIDIA’s “Defense Strategies”

However, challenges remain. Major NVIDIA customers like OpenAI and Meta (META.US) have already begun developing their own chips, with Meta planning to release a new AI chip every six months. The rise of Application-Specific Integrated Circuits (ASICs) is seen as a long-term threat to NVIDIA’s general-purpose GPUs, as custom chips tailored for specific functions demonstrate higher efficiency in inference scenarios.

KinNgai Chan, Managing Director of Summit Insights Group, said that compared to a year ago, NVIDIA will face more intense market competition. He predicts that by 2027, as companies achieve large-scale deployment of self-developed ASICs, NVIDIA’s market share will decline, especially in inference chips.

To counter these threats, NVIDIA is taking multiple measures. Besides acquiring Groq, the company recently invested $2 billion in optical communication firms Lumentum (LITE.US) and Coherent (COHR.US) to promote “co-packaged optics” (CPO) technology. This technology uses light instead of electrical signals to transfer data between chips, potentially greatly improving data center connectivity efficiency and reducing power consumption. William Blair analyst Sebastien Naji expects CPO to be a key breakthrough for the next-generation Feynman chip architecture.

Bourne from eMarketer added that NVIDIA is likely to position CPO technology at GTC as a key to efficiently connecting large-scale AI clusters. However, the current production scale of this technology cannot match NVIDIA’s chip shipments, and the costs and feasibility of large-scale deployment will be a focus for investors.

Meanwhile, the role of traditional CPUs, long dominated by Intel (INTC.US) and AMD (AMD.US), is rebounding in AI tasks. Third Bridge analyst William McGonigle pointed out that with the rise of agent-based AI, the “agent orchestration layer” handled by CPUs is becoming a new performance bottleneck. He expects NVIDIA to showcase server products that rely solely on its CPUs to respond to this emerging trend.

AI Agents and Robotics: Driving the Next Growth Wave

Beyond hardware competition, market attention is also on whether AI applications can sustain the computing power demand. Jensen Huang previously emphasized that intelligent agents will become the next major driver of inference needs. Sheth from d-Matrix said that as the potential of voice, video, and multimodal AI agents is gradually unlocked, this field could spark a new wave of inference computing.

Robotics is seen as another growth area. Daniel Newman, CEO of The Futurum Group, noted that NVIDIA reported about $6 billion in robotics-related revenue last quarter and predicted that the development timeline for humanoid robots will be very “aggressive.” This suggests that physical AI could become a reality faster than expected.

Geopolitical Risks: The Damocles Sword Over Chip Giants

Beyond technological competition, geopolitical factors are increasingly influencing NVIDIA’s future. With the U.S. considering further export restrictions on AI chips and access to key markets like China being limited, NVIDIA’s global sales footprint is reshaping. Reports indicate that after a complete freeze in the Chinese market, NVIDIA has ceased production of the H200 chip and shifted capacity to the next-generation Rubin platform.

In this context, large-scale AI infrastructure investments in the Middle East, such as in Saudi Arabia and the UAE, are significant for NVIDIA. However, regional conflicts, energy costs, and data center construction speeds add uncertainty to the demand in these emerging markets.