When DeepSeek’s R1 debuted in early 2025, almost $600 billion was wiped out from Nvidia’s market value in a single day on fears that artificial intelligence (AI) would require less computing power than previously expected. Moonshot AI’s release of Kimi K3 on Friday triggered a similar reaction, helping push semiconductor stocks sharply lower.
The comparison, however, may overlook an important distinction. DeepSeek’s breakthrough centered on making AI models cheaper to train and run. Kimi K3 also improves computing efficiency, but does so with a far larger model that places heavier demands on memory infrastructure. That could continue to support the need for high-bandwidth memory from SK Hynix, Nvidia’s latest AI systems and advanced chipmaking from Taiwan Semiconductor Manufacturing Co (TSMC).
Kimi K3 contains 2.8 trillion parameters, China’s largest model yet, pushing the sparsity ratio — a measure of computing efficiency — to a record, according to data compiled by Bloomberg from disclosures from model producers. A higher ratio means fewer parameters are activated for each task relative to the model’s total size.
But every one of those 2.8 trillion parameters must still be stored in memory. Even after compressing the model using lower-precision data formats, Kimi K3 occupies roughly 1.4 terabytes of memory. Deploying the model therefore requires clusters of memory-rich AI processors such as Nvidia’s Blackwell GB300 systems.
Another milestone is approaching. On July 27, Moonshot plans to release Kimi K3’s model weights publicly, allowing companies to run the model themselves instead of accessing it through Moonshot’s cloud service. While the software will be freely available, organisations seeking to deploy it at scale will still need significant AI hardware.
Meanwhile, Alibaba Group Holding also unveiled its Qwen 3.8-Max preview over the weekend, a 2.4 trillion-parameter model performing alongside leading frontier models — though it has yet to publish benchmark results. As Chinese AI developers increasingly tailor their models to domestic chips, the country’s hardware ecosystem could stand to benefit. The optimism helped lift China’s tech-heavy ChiNext Index by as much as 3.6 per cent on Monday.
Together, the releases suggest competition in advanced AI models is broadening beyond United States (US) frontier labs such as OpenAI and Anthropic PBC. That could put pressure on the pricing power of the US model providers, while continuing to drive investment in the infrastructure needed to run increasingly capable AI systems.
