Looking forward to the agentic mode release. Moonshot does not seem to offer subscriptions?
I bought $5 worth of Moonshot API calls a long while ago, still have a lot of credits left.
Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model
241–250 of 442 posts
Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model
#242Earlier quoted context omitted.
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I'm tired of this ol' propaganda trope. * We're leading the world in fusion research. https://www.pppl.gov/news/2025/wendelstein-7-x-sets-new-perf... * Our satellites are giving us by far the best understanding of our universe, capturing one third of the visible sky in incredible detail - just check out this mission update video if you want your mind blown: https://www.youtube.com/watch?v=rXCBFlIpvfQ * Not only that,…
Don't worry, we in the US are hot on your heels in the own-goal game ( https://www.space.com/space-exploration/nasa-is-sinking-its-... ).
All you have to do is wait by the Trump River and wait for our body to come floating by.
Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model
#243How does one effectively use something like this locally with consumer-grade hardware?
Consumer-grade hardware? Even at 4bits per param you would need 500GB of GPU VRAM just to load the weights. You also need VRAM for KV cache.
It's MoE-based, so you don't need that much VRAM.
Nice if you can get it, of course.
Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model
#244It's good to see more competition, and open source, but I'd be much more excited to see what level of coding and reasoning performance can be wrung out of a much smaller LLM + agent as opposed to a trillion parameter one. The ideal case would be something that can be run locally, or at least on a modest/inexpensive cluster. The original mission OpenAI had, since abandoned, was to have AI benefit all of humanity, and…
It is not clear that a simple/small model with inference running on home hardware is energy or cost efficient compared to the scaled up inference of a large model with batch processing. There are dozens of optimizations possible when splitting an LLM on multiple tiny components on separate accelerator units and when one handles kv cache optimization at the data center level; these are simply not possible at home and would be a waste of effort and energy until you serve thousands to millions of requests in parallel.
Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model
#245Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model
#246Earlier quoted context omitted.
> the whole business model of companies like OpenAI and Anthropic, at least at the moment, seems to be that the models are so big that you need to run them in the cloud with metered access. That's not a business model choice, though. That's a reality of running SOTA models. If OpenAI or Anthropic could squeeze the same output out of smaller GPUs and servers they'd be doing it for themselves. It would cut their datace…
> If OpenAI or Anthropic could squeeze the same output out of smaller GPUs and servers they'd be doing it for themselves. First, they do this; that's why they release models at different price points. It's also why GPT-5 tries auto-routing requests to the most cost-effective model. Second, be careful about considering the incentives of these companies. They all act as if they're in an existential race to deliver 'the…
> > If OpenAI or Anthropic could squeeze the same output out of smaller GPUs and servers they'd be doing it for themselves.
> First, they do this; that's why they release models at different price points.
No, those don't deliver the same output. The cheaper models are worse.
> It's also why GPT-5 tries auto-routing requests to the most cost-effective model.
These are likely the same size, just one uses reasoning and the other doesn't. Not using reasoning is cheaper, but not because the model is smaller.
Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model
#247Earlier quoted context omitted.
If NVIDIA had any competition we'd be able to run these larger models at home by now instead of being saddled with these 16GB midgets.
NVIDIA has tons of competition on inference hardware. They’re only a real monopoly when it comes to training new ones. And yet…
Those are for the enterprise. In the context of discussion, end users only have Apple, AMD, and Nvidia.
Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model
#248Looking forward to the agentic mode release. Moonshot does not seem to offer subscriptions?
I bought $5 worth of Moonshot API calls a long while ago, still have a lot of credits left.
Are you using it for chat? I'm thinking of agentic use, which is much more token hungry. You could go through the $5 in a day.
Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model
#249As a Chinese user, I can say that many people use Kimi, even though I personally don’t use it much. China’s open-source strategy has many significant effects—not only because it aligns with the spirit of open source. For domestic Chinese companies, it also prevents startups from making reckless investments to develop mediocre models. Instead, everyone is pushed to start from a relatively high baseline. Of course, many small companies in the U.S., Japan, and Europe are also building on Qwen. Kimi is similar: before DeepSeek and others emerged, their model quality was pretty bad. Once the open-source strategy was set, these companies had no choice but to adjust their product lines and development approaches to improve their models.
Moreover, the ultimate competition between models will eventually become a competition over energy. China’s open-source models have major advantages in energy consumption, and China itself has a huge advantage in energy resources. They may not necessarily outperform the U.S., but they probably won’t fall too far behind either.
Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model
#250Earlier quoted context omitted.
Yes, I am also super interested in cutting the size of models. However, in a few years today’s large models will run locally anyhow. My home computer had 16KB RAM in 1983. My $20K research workstation had 192MB of RAM in 1995. Now my $2K laptop has 32GB. There is still such incredible pressure on hardware development that you can be confident that today’s SOTA models will be running at home before too long, even with…
> My home computer had 16KB RAM in 1983. My $20K research workstation had 192MB of RAM in 1995. Now my $2K laptop has 32GB. You’ve picked the wrong end of the curve there. Moore’s law was alive and kicking in the 90s. Every 1-3 years brought an order of magnitude better CPU and memory. Then we hit a wall. Measuring from the 2000s is more accurate. My desktop had 4GB of RAM in 2005. In 20 years it’s gone up by a facto…
Moore’s Law is about transistor density, not RAM in workstations. But yes, density is not doubling every two years any more.
RAM growth slowed in laptops and workstations because we hit diminishing returns for normal-people applications. If local LLM applications are in demand, RAM will grow again.
RAM doubled in Apple base models last year.