NVIDIA is facing challenges from competitors like Google's TPU and Amazon's Trainium. To reinforce its dominance in the AI chip market, the company has intensified its technical demonstrations and public responses. Following private rebuttals against bearish views and public claims of its GPU technology being "a generation ahead," NVIDIA has released another technical blog post, asserting that its GB200 NVL72 system can enhance the performance of top open-source AI models by up to 10 times.

On December 4, reports indicated that NVIDIA highlighted the GB200 NVL72 system's capability to boost leading open-source AI models' performance by as much as 10x. The company emphasized its server system's optimization for Mixture of Experts (MoE) models, including Kimi K2 Thinking by Chinese startup Moonshot AI and DeepSeek's R1 model, in a recent blog post.

NVIDIA's series of technical validations are seen as direct responses to market concerns. Earlier reports suggested that Meta, a key NVIDIA client, is considering large-scale adoption of Google's in-house AI chip, the Tensor Processing Unit (TPU), in its data centers. Google's TPU poses a direct challenge to NVIDIA's over 90% market share in AI chips. Market worries persist that if hyperscale clients like Meta shift to Google, NVIDIA's seemingly unassailable moat could weaken.

Despite NVIDIA's proactive communications, market concerns remain unresolved, with the company's stock declining nearly 10% over the past month.

**GB200 NVL72's Technical Edge** NVIDIA's official blog detailed the GB200 NVL72 system's advantages, which integrates 72 NVIDIA Blackwell GPUs into a unified unit, delivering 1.4 exaflops of AI performance and 30TB of high-speed shared memory. With NVLink Switch connections, the system achieves 130TB/s inter-GPU communication bandwidth.

In performance tests, Kimi K2 Thinking—ranked as the smartest open-source model by Artificial Analysis—achieved a 10x performance boost on the GB200 NVL72 system. Other leading MoE models, including DeepSeek-R1 and Mistral Large 3, also showed significant improvements.

MoE models have become mainstream in cutting-edge AI development. NVIDIA noted that all top 10 open-source models on the Artificial Analysis leaderboard adopt MoE architecture, such as DeepSeek-R1, Kimi K2 Thinking, and Mistral Large 3. This architecture mimics the human brain by activating only specialized "expert" modules for specific tasks, avoiding full-model parameter usage. As a result, MoE models enable faster, more efficient token generation without disproportionately increasing computational costs.

NVIDIA emphasized that its system addresses MoE models' scaling challenges in production environments through hardware-software co-design, effectively eliminating traditional deployment bottlenecks.

**Accelerated Cloud Provider Deployments** NVIDIA revealed that the GB200 NVL72 system is being deployed by major cloud service providers and partners, including Amazon Web Services, Core42, CoreWeave, Crusoe, Google Cloud, Lambda, Microsoft Azure, Oracle Cloud Infrastructure, and Together AI.

Peter Salanki, Co-founder and CTO of CoreWeave, stated, "At CoreWeave, our customers leverage our platform to bring MoE models into production. Through close collaboration with NVIDIA, we deliver a tightly integrated platform."

Lin Qiao, Co-founder and CEO of Fireworks AI, added, "NVIDIA's GB200 NVL72 rack-scale design significantly improves MoE model serving efficiency, setting new benchmarks for performance and scalability." The company has already deployed the Kimi K2 model on NVIDIA's B200 platform, achieving top-tier performance on the Artificial Analysis leaderboard.