Similar to DeepSeek in January 2025, Panicans may think that the AI networking switch TAM will massively shrink because Kimi K3 uses KDA Attention, which reduces KV-transfer networking bandwidth by up to 10x. But the opposite is true, as we explain below. 👇️ 1/8🧵
Furthermore, with optimizations like incremental KV-cache transfers, prefill only needs to transfer the portions cached by the non-decode instance, so KV transfer does not take up much networking bandwidth relative to WideEP, even before KDA. 7/8🧵
More efficient attention will further push context lengths from 1M to 5M+, with less context rot. Jevons’ Paradox means that making attention more efficient will lead to wider AI adoption, which will require more networking. 8/8🧵
A year ago, the big three was OpenAI, Anthropic, and Google. Things have changed.
Moonshot's Kimi K3 sits above Gemini on every composite benchmark, and it's open source in 10 days.
New episode: what K3 reveals about frontier margins, model sizes, and who's actually still in
MASSIVE DELAY ALERT TO ORACLE’S STARGATE SITE AND BLOOM ENERGY🚨🚨
Oracle’s Project Jupiter behind-the-meter datacenter project in New Mexico that plans to use Bloom Energy is at risk of a 1-2 year delay due to permitting and pipeline building blockers. (1/8)🧵
And irrespective of the pipeline: the power generation plan itself still isn't approved. In April, Oracle and BorderPlex Digital Assets announced the switch from gas turbines to up to 2.45 GW of Bloom Energy solid oxide fuel cells — one of the world's largest independent
However, the fuel cell Air Permit application is still pending as of mid-July. The New Mexico Environmental Department ordering a public hearing, but without a date set for it.
Project Jupiter is the most delayed of the Stargate projects. Read more about the real delays to the
Similar to the panic over DeepSeek R1, some uneducated people think Kimi K3’s use of linear attention (KDA) is bad for NVIDIA, HBM, DRAM, and networking because it has relatively lower KV-cache requirements. The opposite is true, and we explain why below. 👇️ 1/8🧵
Lastly, Jevons’ Paradox means that making attention more efficient will drive wider AI adoption, which will ultimately require more GPUs, HBM, DRAM, and networking—not less. 8/8🧵
