Amazing how fast AI keeps improving, every new model feels like a big step forward
It solely is improving on efficiency. While it is extremely valuable given the disproportionate (to value) costs of these things, your statement almost sounds like it has improved an even more challenging aspect, pushing performance.
For the uninitiated, what's a "hybrid linear attention architecture"?
1/4 of their layers are conventional quadratic attention
Could someone explain every term in this subthread in a very simple way to someone who basically only knows "transformers are a neural network architecture that use something called 'attention' to consider the entire input the whole time or something like that", and who does not understand what "quadratic" even means in a time complexity or mathematical sense beyond that "quad" has something to do with the number four.
I am aware I could Google it all or ask an LLM, but I'm still interested in a good human explanation.
Transformers try to give you capabilities by doing two things interleaved (in layers) multiple times:
- apply learned knowledge from its parameters to every part of the input representation („tokenized“, ie, chunkified text).
- apply mixing of the input representation with other parts of itself. This is called „attention“ for historical reasons. The original attention computes mixing of (roughly) every token (say N) with every other (N). Thus we pay a compute cost relative to N squared.
The attention cost therefore grows quickly in terms of compute and memory requirements when the input / conversation becomes long (or may even contain documents).
It is a very active field of research to reduce the quadratic part to something cheaper, but so far this has been rather difficult, because as you readily see this means that you have to give up mixing every part of the input with every other.
Most of the time mixing token representations close to each other is more important than those that are far apart, but not always. That’s why there are many attempts now to do away with most of the quadratic attention layers but keeping some.
What to do during mixing when you give up all-to-all attention is the big research question because many approaches seem to behave well only under some conditions and we haven’t established something as good and versatile as all-to-all attention.
If you forgo all-to-all you also open up so many options (eg. all-to-something followed by something-to-all as a pattern, where something serves as a sort of memory or state that summarizes all inputs at once. You can imagine that summarizing all inputs well is a lossy abstraction though, etc.)
Afaik there are two types of attention, cross and self attention. It's quadratic becase you have to process one set of tokens with another, like calculating a matrix product. Originally designed for translation, you'd take tokens in one language on one side and the other language on the other, then compute the relevance of each word with each other which the model then uses further to more accurately generate the translation.
With self attention you compute every token in a sequence with every other token in that same sequence, figuring out which word references which other word (e.g. "George is sitting in the park. He's reading a book.", "He" would correlate with "George", letting the model know what it refers to). Of course these are also trained layers so what the model thinks correlates with what and how that info is used in the DNN perceptron part is depends wholly on the training process.
There is no free lunch with this and with only 1/4 of layers having it, the model will perform significantly worse at identifying relevant info and likely decohere a lot compared to having it on every layer. But since you get rid of the quadratic complicity, it'll be much faster. Think "I'm doing 1000 calculations per second and they're all wrong" meme. So far there have been lots of attempts at doing linear-ish attention (e.g. Google doing the sliding window hackery that only computes a part of the vectors and hopes for good locality, mamba combinations with RNNs, Meta removing positional encodings in attention in the trainwreck that was LLama4, etc.) and they've mostly failed, so the holy grail is finding a way to make it work since you get the best of both worlds. The top performing models today all use fully quadratic attention or combine it with sliding windows in some layers to claw back some speed in long context scenarios at the cost of some accuracy.
125 upvotes with 2 comments is kinda sus
Lots of model releases are like this. We can only upvote. We can't run the model on our personal computers. We can neither test their 'Efficient Attention' 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).
Your have a 9Mbit downlink? I'm not sure you're trying much of anything with that Internet connection, no offense.
I'm hoping someone will explain what this release even means.
We do live in an age of frontier LLMs... For fun, I'll just use Kimi K2 (on Kagi Assistant).
> Can you explain what this means and its significance? Assume that I'm a layperson with no familiarity with LLM jargon so explain all of the technical terms, references, names. https://github.com/MoonshotAI/Kimi-Linear
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
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.
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.
The Chinese century ain't gonna build itself /s
any hardware recommendations? how much memory do we need to this?
You will effectively want a 48GB card or more for quantized versions, otherwise you won'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).