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Kimi.ai @Kimi_Moonshot · 5d

Releasing the model weights of Kimi K3.1 Pro: huggingface.co/moonshotai/Kim…
We'll keep advancing open intelligence.

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AInvest.com Verified account @Ainvest_Wire · Oct 26

Idle GPUs can still produce an expensive AI bill.

In VentureBeat Intelligence’s Q3 survey, 79% of respondents reporting GPU use said their GPUs were busy half the time or less. Yet 70% of that low-use group planned a new, additional or replacement AI platform.

A platform change can mean moving workloads, replacing an inefficient stack or buying access to a different model. It does not automatically mean ordering more GPUs.

An enterprise can also pay twice: once for owned or reserved compute, then again for a hosted model API. Spare capacity on its own hardware does not automatically offset the API invoice. Moving that work requires a suitable model and a functioning deployment.

The fixed-cost arithmetic is unforgiving.

At 20% busy time, each busy GPU-hour carries 5x the fixed capacity cost of a fully used hour. At 50%, that falls to 2x. At 80%, it is 1.25x.

Moving from 20% to 50% cuts fixed cost per busy hour by 60%, with no change in hardware prices. The total fixed bill stays the same; more work shares it. This is an illustrative model, not a measured saving.

The return still needs proving. In September, 58 of 64 respondents planning a platform change lacked finance-reviewed ROI figures. That means unverified returns, not necessarily losses.

For investors, follow three different tests:

• Hardware suppliers: orders converting into collected cash.
• GPU clouds: utilization alongside realized pricing and contract costs.
• Enterprise buyers: cost per successfully completed task.

A busy GPU can run an unprofitable workload. An idle enterprise GPU does not establish a shortage of demand everywhere else.

Survey caveat: self-selected respondents, not a representative census of the world’s GPUs.

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Jessie Dong Verified account @jessiedong_ · Oct 19

the neocloud boom is really an anthropic boom

of the $157B in publicly priced deals neoclouds signed this year, anthropic signed $90B (57% of total)

anthropic's revenue grew 7x in a year and unlike most companies, they actually can use any chip. and they are.

they use amazon's trainium, google's tpus, nvidia's gpus, AND signed a deal with amd

because it can use any chip, it can rent from anyone with capacity, which protects them from any shortage

meta came second at $48B with @nebiusai and @CoreWeave. but where's openAI? this year it signed just $10B with @cerebras
- (but in 2025 openai signed ~$610B with @Oracle, @Microsoft, @awscloud and @CoreWeave, while anthropic only signed ~$80B)

as long as anthropic keeps growing, the biggest neoclouds will continue to have a great customer

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