[adityashankar]

Due to perverse incentives and the historical nature of models over-claiming accuracy, it's very hard to believe anything until it is open source and can be tested out

that being said, I do very much believe that computational efficiency of models is going to go up [correction] drastically over the coming months, which does pose interesting questions over nvidia's throne

*previously miswrote and said computational efficiency will go down

[credit_guy]

Like this?

https://huggingface.co/amd/Zebra-Llama-8B-8MLA-24Mamba-SFT

[jychang]

Or like this: https://api-docs.deepseek.com/news/news251201

I don't know what's so special about this paper.

- They claim to use MLA to reduce KV cache by 90%. Yeah, Deepseek invented that for Deepseek V2 (and also V3 and Deepseek R1 etc)

- They claim to use a hybrid linear attention architecture. So does Deepseek V3.2 and that was weeks ago. Or Granite 4, if you want to go even further back. Or Kimi Linear. Or Qwen3-Next.

- They claimed to save a lot of money not doing a full pre-train run for millions of dollars. Well, so did Deepseek V3.2... Deepseek hasn't done a full $5.6mil full pretraining run since Deepseek V3 in 2024. Deepseek R1 is just a $294k post train on top of the expensive V3 pretrain run. Deepseek V3.2 is just a hybrid linear attention post-train run - i don't know the exact price, but it's probably just a few hundred thousand dollars as well.

Hell, GPT-5, o3, o4-mini, and gpt-4o are all post-trains on top of the same expensive pre-train run for gpt-4o in 2024. That's why they all have the same information cutoff date.

I don't really see anything new or interesting in this paper that isn't already something Deepseek V3.2 has already sort of done (just on a bigger scale). Not exactly the same, but is there anything amazingly new that's not in Deepseek V3.2?

[twotwotwo]

These are potentially complementary approaches. Various innovations have shrunk the KV cache size or (with DSA) how much work you have to do in each attention step. This paper is about hybrid models where some layers' state needs don't grow with context size at all.

SSMs have a fixed-size state space, so on their own they'll never going be able to recite a whole file of your code in a code-editing session for example. But if much of what an LLM is doing isn't long-distance recall, you might be able to get away with only giving some layers full recall capability, with other layers manipulating the info already retrieved (plus whatever's in their own more limited memory).

I think Kimi Linear Attention and Qwen3-next are both doing things a little like this: most layers' attention/memory doesn't grow with context size. Another approach, used in Google's small open Gemma models, is to give some layers only 'local' attention (most recent N tokens) and give a few 'full' (whole context window) attention. I guess we're seeing how those approaches play out and how different tricks can be cobbled together.

There can potentially be a moneyball aspect to good model architecture. Even if on its own using space-saving attention mechanisms in some layers of big models cost something in performance, their efficiency could allow you to 'spend' more elsewhere (more layers or more params or such) to end with overall better performance at a certain level of resources. Seems like it's good to have experiments with many different approaches going on.

[T-A]

From your link: DeepSeek-V3.2 Release 2025/12/01

From Zebra-Llama's arXiv page: Submitted on 22 May 2025

[Palmik]

DeepSeek's MLA paper was published in 2024: https://arxiv.org/abs/2405.04434

DeepSeek's Sparse Attention paper was published in February: https://arxiv.org/abs/2502.11089

DeepSeek 3.2 Exp (combining MLA and DSA) was released in September.

You also had several other Chinese hybrid models, like Qwen3 Next and Minimax M1.

[jychang]

That's still behind the times. Even the ancient dinosaur IBM had released a Mamba model [1] before this paper was even put out.

> Granite-4.0-Tiny-Base-Preview is a 7B-parameter hybrid mixture-of-experts (MoE) language model featuring a 128k token context window. The architecture leverages Mamba-2, superimposed with a softmax attention for enhanced expressiveness, with no positional encoding for better length generalization. Release Date: May 2nd, 2025

I mean, good for them for shipping, I guess. But seriously, I expect any postgrad student to be able to train a similar model with some rented GPUs. They literally teach MLA to undergrads in the basic LLM class at Stanford [2] so this isn't some exactly some obscure never-heard-of concept.

[1] https://huggingface.co/ibm-granite/granite-4.0-tiny-base-pre...

[2] https://youtu.be/Q5baLehv5So?t=6075

[nickpsecurity]

"Deepseek hasn't done a full $5.6mil full "

Don't forget the billion dollars or so of GPU's they had access to that they left out of that accounting. Also, the R&D cost of the Meta model they originally used. Then, they added $5.6 million on top of that.

[credit_guy]

Here's what's important about this paper. It is written by AMD researchers. It shows AMD is investing in AI research. Is this the same level of achievement as DeepSeek 3.2. Most likely not. Do they have novel ideas? Difficult to say, there are hundreds of new ideas being tried in this space. Is this worthless? Most certainly not. In order to make progress in this domain (as in any other), you first need to get your feet wet. You need to play with the various components, and see how they fit together. The idea in this paper is that you can combine somehow SSMs (like Mamba) and LLMs (like LLama). The examples they give are absolute toys compared to DeepSeek 3.2 (the largest is 8 billion parameters, while DeepSeek 3.2 has 671 billion parameters). The comparison you are trying to make simply does not apply. The good news for all of us is that AMD is working in this space.

[jychang]

Mamba based LLMs aren't even close to novel though. IBM's been doing this since forever [1].

Also, you're off on Deepseek V3.2's param count, the full model's 685B in size with the MTP layer.

I don't think there's anything interesting here other than "I guess AMD put out a research paper", and it's not cutting edge when Deepseek or even IBM is running laps around them.

[1] Here's a news article from April, although IBM has been doing it for a long time before that https://research.ibm.com/blog/bamba-ssm-transformer-model

[credit_guy]

It's not cutting edge, so what? Your point is that nobody should publish anything unless it is cutting edge?

[jychang]

Yeah, that's the point of publishing. You get scooped, you lose.

[SilverElfin]

How did you get all this info about how each is trained? Is that something they admit now or is it through leaks?

[jychang]

Deepseek? It's literally in their research papers.

OpenAI? The OpenAI head of research @markchen90 straight up admitted it in a podcast.

https://x.com/petergostev/status/1995744289079656834

"In the last 2 years we've put so much resourcing into, into reasoning and one byproduct of that is you lose a little bit of muscle on pre training and post training." "In the last six months, @merettm and I have done a lot of work to build that muscle back up." "With all the focus on RL, there's an alpha for us because we think there's so much room left in pre training." "As a result of these efforts, we've been training much stronger models. And that also gives us a lot of confidence carrying into Gemini 3 and other releases coming this end of the year."

Note, "alpha" in the quote above is referring to https://en.wikipedia.org/wiki/Alpha_(finance)

But it's pretty clear that the last full pretrain run they've released is for gpt-4o 2 years ago*, and since then they've just been iterating RL for their models. You don't need any insider information to notice that, it's pretty obvious.

*Excluding GPT-4.5 of course, but even OpenAI probably wants us to forget about that.

[nl]

Semi-analysis also believes they haven't done a fill pretraining run since 4o (except for GPT-4.5): https://open.substack.com/pub/semianalysis/p/tpuv7-google-ta...

[deepdarkforest]

> which does pose interesting questions over nvidia's throne...

> Zebra-Llama is a family of hybrid large language models (LLMs) proposed by AMD that...

Hmmm

[adityashankar]

yes!, thanks for the link!

[moffkalast]

GGUF when? /s

[ACCount37]

I don't doubt the increase in efficiency. I doubt the "drastically".

We already see models become more and more capable per weight and per unit of compute. I don't expect a state-change breakthrough. I expect: more of the same. A SOTA 30B model from 2026 is going to be ~30% better than one from 2025.

Now, expecting that to hurt Nvidia? Delusional.

No one is going to stop and say "oh wow, we got more inference efficiency - now we're going to use less compute". A lot of people are going to say "now we can use larger and more powerful models for the same price" or "with cheaper inference for the same quality, we can afford to use more inference".

[colechristensen]

Eh.

Right now, Claude is good enough. If LLM development hit a magical wall and never got any better, Claude is good enough to be terrifically useful and there's diminishing returns on how much good we get out of it being at $benchmark.

Saying we're satisfied with that... well how many years until efficiency gains from one side and consumer hardware from the other meet in the middle so "good enough for everybody" open models are available for anyone who wants to pay for a $4000 MacBook (and after another couple of years a $1000 MacBook, and several more and a fancy wristwatch).

Point being, unless we get to a point where we start developing "models" that deserve civil rights and citizenship, the years are numbered to where we NEED cloud infrastructure and datacenters full of racks and racks of $x0,000 hardware.

I strongly believe the top end of the S curve is nigh, and with it we're going to see these trillion dollar ambitions crumble. Everybody is going to want a big-ass GPU and a ton of RAM but that's going to quickly become boring because open models are going to exist that eat everybody's lunch and the trillion dollar companies trying to beat them with a premium product aren't going to stack up outside of niche cases and much more ordinary cloud compute motivations.

[ACCount37]

Good enough? There's no such thing.

People said that "good enough" about GPT-4. Now you say that about Claude Opus 4.5. How long before the treadmill turns, and the very same Opus 4.5 becomes "the bare minimum" - the least capable AI you would actually consider using for simple and unimportant tasks?

We have miles and miles of AI advancements ahead of us. The end of that road isn't "good enough". It's "too powerful to be survivable".

[colechristensen]

I can build fully functional applications without writing a single line of code with Claude. In my free time. On a weekend. I'm going to release one of them pretty soon. A toddler being able to do this instead of an industry veteran isn't that compelling. Avoiding the few pitfalls of the LLM getting stuck and taking a while to get out isn't that valuable.

>Good enough? There's no such thing.

This is just wrong. Maybe you can't imagine good enough, I can. And I think "better" is going to start getting diminishing returns as the velocity of improvements I expect to slow and the value of improvements are going to become less meaningful. The "cost" of a LLM making mistakes is already pretty low, cutting it in half is better, sure, but it's so low already I don't particularly care if it gets some multiple more rare.

[grogers]

LLM only fairly recently underwent a step change from "maybe someday" to actually useful now. That opened many new doors that people didn't even think were possible. Getting incrementally better at something they are already pretty good at isn't that impressive. But getting drastically better at something they are currently bad at, will drive new models and new research.

[cyanydeez]

Elon will boil the oceans if it means not having to deal with poor people.

[buu700]

Coding capability in and of itself may be "good enough" or close to it, but there's a long way to go before AI can build and operate a product end-to-end. In fairness, a lot of the gap may be tooling.

But the end state in my mind is telling an AI "build me XYZ", having it ask all the important questions over the course of a 30-minute chat while making reasonable decisions on all lower-level issues, then waking up the next morning to a live cloud-hosted test environment at a subdomain of the domain it said it would buy along with test builds of native apps for Android, iOS, Linux, macOS, and Windows, all with near-100% automated test coverage and passing tests. Coding agents feel like magic, but we're clearly not there yet.

And that's just coding. If someone wanted to generate a high-quality custom feature-length movie within the usage limits of a $20/mo AI plan, they'd be sorely disappointed.

[nl]

> But the end state in my mind is telling an AI "build me XYZ", having it ask all the important questions over the course of a 30-minute chat while making reasonable decisions on all lower-level issues, then waking up the next morning to a live cloud-hosted test environment at a subdomain of the domain it said it would buy along with test builds of native apps for Android, iOS, Linux, macOS, and Windows, all with near-100% automated test coverage and passing tests. Coding agents feel like magic, but we're clearly not there yet.

If you take out the native builds we are there now. V0, Lovable, etc really do a great job of this. If you want an IDE-like environment Antigravity is pretty good too.

The native builds thing is completely doable too. I've built cross platform apps in 30 minutes of my time using Codex+Flutter. It really does work.

[colechristensen]

>But the end state in my mind is telling an AI "build me XYZ", having it ask all the important questions over the course of a 30-minute chat while making reasonable decisions on all lower-level issues, then waking up the next morning to a live cloud-hosted test environment at a subdomain of the domain it said it would buy along with test builds of native apps for Android, iOS, Linux, macOS, and Windows, all with near-100% automated test coverage and passing tests. Coding agents feel like magic, but we're clearly not there yet.

I'm pretty sure we're there. I'm not sure how interested I am in completely closing that loop and completely removing the human from the loop. But I'm also pretty confident that I could do it with nothing but existing models and software built around them.

[buu700]

I'm not aware that we are there, but would be very interested if you have information to the contrary. Even if we were there, the product/service that does it would have to be at a reasonable cost in order to be useful for most people.

As I said, a lot of the gap may be tooling. But I'm skeptical that even the models themselves are capable of that given sufficiently advanced tooling. I'm not saying we're not close (certainly much closer than we were at the start of the decade), but if we were actually there, you would have zero reservations about removing the human from the loop of an initial prototype.

[colechristensen]

My information to the contrary is my experience in the last few weeks building things with LLMs including tooling to help build things with LLMs. The is experience is one of ... I'm a product manager and devsecops engineer bullying an LLM with the psychology of a toddler into building great software which it can do very successfully. A single instance of a model with a single rolling context window and one set of prompts absolutely can't do what you want, but that's not what I've been doing.

Oneshotting applications isn't interesting to me because I do want to be involved, there are things I have opinions about that I won't know I will have until we get there and there are definitely times where I want to pivot a little or a lot in the middle of development based on experience, an actually agile development cycle.

In the same way I wouldn't want to hire a wedding planner or house builder to plan my wedding or build my home based entirely on a single short meeting before anything started, I don't want to one shot software.

There are all sorts of things where I want to get myself out of the loop because they're stupid problems, some of them I've fixed, others I'd rather fix later because doing the thing is more interesting than pausing and building the tools to make the thing.

There is I think an inverse relationship between the complexity of the tooling and the amount of human involvement; for me I've reached or am quite near the amount of human involvement where I'm much more excited about building stuff than saving more of my attention.

I'm being a bit vague because I'm not sure I want to share all of my secrets just yet.

[sureglymop]

Given that natural language is ambiguous, what if the LLM makes some mistakes though?

I'm wondering because, it's not like it's a human that can then take accountability/responsibility for that...

[buu700]

I'd say the simple answer is that the buck always stops with the vendor. If Acme Co sells a jetpack that explodes and kills someone, the entity doesn't get to deflect liability by saying one of their engineers made a mistake; that may be an explanation, but not an excuse. Swapping out Acme Co and its employees with your grandma and her AI/robots doesn't change the fundamental principle.

[danielbln]

I think you mean computational efficiency will go _up_ in the future. To your last point: Jevons paradox might apply.

[adityashankar]

yup that's what I meant!, Jevon's paradox applies to resource usage in general and not towards a specific companies dominance

if computational efficiency goes up (thanks for the correction), and CPU inference becomes viable for most practical applications, GPUs (or accelerators) themselves may be unnecessary for most practical functions

[atq2119]

Discrete GPUs still have an advantage in memory bandwidth. Though this might push platforms like laptops towards higher bandwidths, which would be nice.