Amazing how fast AI keeps improving, every new model feels like a big step forward

Could someone explain every term in this subthread in a very simple way to someone who basically only knows &quot;transformers are a neural network architecture that use something called &#x27;attention&#x27; to consider the entire input the whole time or something like that&quot;, and who does not understand what &quot;quadratic&quot; even means in a time complexity or mathematical sense beyond that &quot;quad&quot; has something to do with the number four.I am aware I could Google it all or ask an LLM, but I&#x27;m still interested in a good human explanation.

1&#x2F;4 of their layers are conventional quadratic attention

For the uninitiated, what&#x27;s a &quot;hybrid linear attention architecture&quot;?

<a href="https:&#x2F;&#x2F;lifearchitect.ai&#x2F;models-table&#x2F;" rel="nofollow">https:&#x2F;&#x2F;lifearchitect.ai&#x2F;models-table&#x2F;</a>

Your have a 9Mbit downlink? I&#x27;m not sure you&#x27;re trying much of anything with that Internet connection, no offense.

Lots of model releases are like this. We can only upvote. 
We can&#x27;t run the model on our personal computers. 
We can neither test their &#x27;Efficient Attention&#x27; concept on our personal computers.Honestly, it would take 24 hours just to download the 98 GB model if I wanted to try it out (assuming I had a card with 98 GB of ram).

We do live in an age of frontier LLMs... For fun, I&#x27;ll just use Kimi K2 (on Kagi Assistant).&gt; Can you explain what this means and its significance? Assume that I&#x27;m a layperson with no familiarity with LLM jargon so explain all of the technical terms, references, names. <a href="https:&#x2F;&#x2F;github.com&#x2F;MoonshotAI&#x2F;Kimi-Linear" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;MoonshotAI&#x2F;Kimi-Linear</a>Imagine your brain could only “look at” a few words at a time when you read a long letter. Today’s big language models (the AI that powers chatbots) have the same problem: the longer the letter gets, the more scratch paper they need to keep track of it all. That scratch paper is called the “KV cache,” and for a 1 000 000-word letter it can fill a small library.Kimi Linear is a new way for the AI to read and write that throws away most of that scratch paper yet still understands the letter. It does this by replacing the usual “look at every word every time” trick (full attention) with a clever shortcut called linear attention. The shortcut is packaged into something they call Kimi Delta Attention (KDA).What the numbers mean in plain English<pre><code> 51.0 on MMLU-Pro: on a 4 000-word school-test set, the shortcut scores about as well as the old, slow method.
 84.3 on RULER at 128 000 words: on a much longer test it keeps the quality high while running almost four times faster.
 6 × faster TPOT: when the AI is writing its reply, each new word appears up to six times sooner than with the previous best shortcut (MLA).
 75 % smaller KV cache: the scratch paper is only one-quarter the usual size, so you can fit longer conversations in the same memory.
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Key pieces explained<pre><code> Full attention: the old, accurate but slow “look back at every word” method.
 KV cache: the scratch paper that stores which words were already seen.
 Linear attention: a faster but traditionally weaker way of summarising what was read.
 Gated DeltaNet: an improved linear attention trick that keeps the most useful bits of the summary.
 Kimi Delta Attention (KDA): Moonshot’s even better version of Gated DeltaNet.
 Hybrid 3:1 mix: three layers use the fast KDA shortcut, one layer still uses the old reliable full attention, giving speed without losing smarts.
 48 B total, 3 B active: the model has 48 billion total parameters but only 3 billion “turn on” for any given word, saving compute.
 Context length 1 M: it can keep track of about 1 000 000 words in one go—longer than most novels.
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Bottom line
Kimi Linear lets an AI read very long documents or hold very long conversations with far less memory and much less waiting time, while still giving answers as good as—or better than—the big, slow models we use today.

I&#x27;m hoping someone will explain what this release even means.

The Chinese century ain&#x27;t gonna build itself &#x2F;s

125 upvotes with 2 comments is kinda sus

You will effectively want a 48GB card or more for quantized versions, otherwise you won&#x27;t have meaningful space left for the KV cache. Blackwell and above is generally a good idea to get faster hardware support for 4b (some recent models took some time to ship for older architectures, gpt-oss IIRC).

any hardware recommendations? how much memory do we need to this?

Kimi Linear: An Expressive, Efficient Attention Architecture