121–130 of 442 posts
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Meta gave up on open weight path after DeepSeek.
It’s more fair to say they gave up after the Llama 4 disaster.
FYI: the non thinking variant has the same price.
uv tool install llm llm install llm-moonshot llm keys set moonshot # paste key llm -m moonshot/kimi-k2-thinking 'Generate an SVG of a pelican riding a bicycle' https://tools.simonwillison.net/svg-render#%3Csvg%20width%3D... Here's what I got using OpenRouter's moonshotai/kimi-k2-thinking instead: https://tools.simonwillison.net/svg-render#%20%20%20%20%3Csv...
Love seeing this benchmark become more iconic with each new model release. Still in disbelief at the GPT-5 variants' performance in comparison but its cool to see the new open source models get more ambitious with their attempts.
uv tool install llm llm install llm-moonshot llm keys set moonshot # paste key llm -m moonshot/kimi-k2-thinking 'Generate an SVG of a pelican riding a bicycle' https://tools.simonwillison.net/svg-render#%3Csvg%20width%3D... Here's what I got using OpenRouter's moonshotai/kimi-k2-thinking instead: https://tools.simonwillison.net/svg-render#%20%20%20%20%3Csv...
Love seeing this benchmark become more iconic with each new model release. Still in disbelief at the GPT-5 variants' performance in comparison but its cool to see the new open source models get more ambitious with their attempts.
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The answer is simply that no one would pay to use them for a number of reasons including privacy. They have to give them away and put up some semblance of openness. No option really.
I know first hand companies paying them. Chinese internal software market is gigantic. Full of companies and startups that have barely made into a single publication in the west.
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The Chinese are doing it because they don't have access to enough of the latest GPUs to run their own models. Americans aren't doing this because they need to recoup the cost of their massive GPU investments.
And Europeans don't it because quite frankly, we're not really doing anything particularly impressive with AI sadly.
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Love seeing this benchmark become more iconic with each new model release. Still in disbelief at the GPT-5 variants' performance in comparison but its cool to see the new open source models get more ambitious with their attempts.
Why is this a benchmark though? It doesn’t correlate with intelligence
what's the hardware needed to run the trillion parameter model?
It's an MoE model, so it might not be that bad. The deployment guide at https://huggingface.co/moonshotai/Kimi-K2-Thinking/blob/main... suggests that the full, unquantized model can be run at ~46 tps on a dual-CPU machine with 8× NVIDIA L20 boards. Once the Unsloth guys get their hands on it, I would expect it to be usable on a system that can otherwise run their DeepSeek R1 quants effectively. You could keep an eye…
I think I get that "reasoning" in this context refers to dynamically budgeting scratchpad tokens that aren't intended as the main response body. But can't any model do that, and it's just part of the system prompt, or more generally, the conversation scaffold that is being written to.
Or does a "reasoning model" specifically refer to models whose "post training" / "fine tuning" / "rlhf" laps have been run against those sorts of prompts rather than simpler user-assistant-user-assistant back and forths?
EG, a base model becomes "a reasoning model" after so much experience in the reasoning mines.
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I must be missing something important here. How do the Chinese train these models if they don't have access to the GPUs to train them?
I believe they mean distribution (inference). The Chinese model is currently B.Y.O.GPU. The American model is GPUaaS