Moonshot launched Kimi K3. Then demand shut down subscriptions in 48 hours.

Moonshot AI became the latest AI company to discover that launching a popular model is only half the battle. Less than two days after releasing Kimi K3, the company stopped accepting new subscribers after demand exhausted its available GPU capacity. Existing users will keep access while Moonshot expands its infrastructure and reopens subscriptions in batches.

Inference demand outpaces supply

The incident emphasizes how demand is outpacing available infrastructure. As AI models take on longer, more coding and agentic workloads, companies are finding they need more inference capacity than they anticipated.

“Kimi K3 has received far more love than we expected,” the official Moonshot account writes on X. “Over the past 48 hours, demand has pushed close to the limits of our current capacity. We’re adding capacity as fast as we can and will reopen new subscription spots in batches.”

Kimi K3 has received far more love than we expected, and our GPUs are feeling it.

Over the past 48 hours, demand has pushed close to the limits of our current capacity. To protect the experience of existing subscribers, we're temporarily pausing new subscriptions and… — Kimi.ai (@Kimi_Moonshot)July 19, 2026

“We’re adding capacity as fast as we can and will reopen new subscription spots in batches.”

For infrastructure engineers and developers, the resulting capacity crunch is a bold indication of why companies from OpenAI to Anthropic to Moonshot are rationing access instead of selling unlimited usage.

Open weights, closed capacity

At 2.8 trillion parameters, Kimi K3 is one of the largest open-weight models slated for release — Moonshot has scheduled the public weight drop for July 27. In Arena.ai’s Frontend Code Arena, K3 topped both OpenAI’s GPT-5.6 Sol and Anthropic’s Claude Fable 5. On the broader Artificial Analysis Intelligence Index, it trails both, scoring 57 to Fable 5’s 60 and Sol’s 59. That doesn’t make it any easier to run.

Open weights let anyone deploy the model, but w…