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- Hacker News
- I read this as if they purchased it from Alibaba the marketplace.....by ericcholis
- I would not minimize the extent to which AI progress depends on things like the effort that goes into training material acquisition and data labeling. In those areas, improvement is a direct function of how much you invest.
I wonder how much spending is motivated by the phenomenon of sudden emergent performance in LLMs. Clearly some people who are smarter than me expect something like emergent AGI, or at least they think the odds justify spending whatever it takes to see if that would happen.
That leaves a lot of room for efficient aggressive followers.
by Zigurd - K3 is natively trained to mxfp4, if they cannot get a hold of Blackwell chip, it is meaningless. Hopper does not do native 4-bit floating math.
Either they have Blackwell with native 4-bit floating math, or they use have Chinese domestic NPU that support mxfp4 natively.
The article’s statement does not make sense.
by ycui7 - It's also built on the back of downloading all the content of the top ten sites on the list, the fact that all the independent content sites with their "pre-war" steel haven't been acquired at top dollar by U.S. companies shows the existential threat / stop China posturing is a total joke.
- by idoxer
- My thesis is still that model building has no moat. Folks continue to migrate around between the big labs. There is a lot of value in having good taste around the harness and how the models are used. The medium to long term winners will be the folks that control the compute.by infecto
- It kind of confirms a hypothesis I have that the next phase of AI development will be about getting smaller (in terms of model size and compute), because smaller is more capital efficient for training (allowing faster iteration and more iteration cycles for a given amount of capital), allows for denser inference (more inference for a given amount of compute hardware), and allows for more edge inference applications.
The goal will be to develop smaller models with more efficient architectures, that have similar or even better performance than larger models.
by a-priori - Interesting if true - that Moonshot can train a ~3T SOTA model on only 20K NVIDIA GPUs, while others like Musk (who freely admits to distilling OpenAI's models) struggle to build a competitive 1T model (Grok 4.5) with massively more compute (Colossus-1 100-250K GPUs, Colossus-2 500K+ GPUs).
I guess at least partly a reflection of all the optimizations in the Kimi 3 architecture.
In the recent leaked DeepSeek investor meeting, they also mentioned only having a 20K GPU cluster (unclear if NVIDIA, or Huawei).