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DSpark: Speculative decoding accelerates LLM inference [pdf]

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Re: DSpark: Speculative decoding accelerates LLM inference [pdf]

#111
post #66

Earlier quoted context omitted.

> Another data point on this is the black market for Claude tokens in China [1]. The chat logs themselves are a commodity to train models. anyone with IQ higher than 130 (thus qualified for actual AI R&D) would be questioning something obvious here - if they are already doing such dodgy stuff with the aim to maximize profits, why would those resellers have large amount of logs with actual American model responses to…

The galaxy brains in the labs putatively buying the logs wouldn't notice this? Or figure out a structure to prevent this?

resellers wouldn't be trying to sell such junk in the first place. they use faked models to avoid the cost of Opus tokens, not to double dip to scam those with arguably the highest IQ in the country.

Re: DSpark: Speculative decoding accelerates LLM inference [pdf]

#112
post #50

Earlier quoted context omitted.

Chinese companies (and labs) operate in conjunction with the CCP so whatever they're doing, it's because it's Chinese state policy. What became clear when DeepSeek came onto the scene was that China was seeking to commoditize LLMs. They consider it an issue of national security not to be beholden to US tech companies when it comes to AI. And I, for one, fully endorse this policy. Another data point on this is the bla…

I don’t see how Anthropic is in a better position. They have a slight edge in model quality right at a time when we’re getting a taste of what cheap, “good enough” AI looks like. They don’t own their own compute. And their own arrogance and lies have alienated a huge chunk of their customer base and alerted everyone to the dangers of being dependent on them.

I personally think not owning their own compute is going to be an advantage.

There is a meteor headed towards all this AI investment that I don't think has been properly accounted for and that is, what happens to all the existing hardware investments when NVidia's next architecture comes out. Blackwell (H100/H200) is the current generation. Rubin (R100, presumably R200) is the next and arrives soon. Now a lot of the investment hasn't been spent yet so will likely be spent on Rubin but at that point, what happens when the next iteration comes out and does 3-4x the compute for the same electricity input and same hardware cost?

Also, what happens when people can run way bigger models on consumer hardware in 5 years? The effective limit for useful local LLMs is currently ~31B parameter models because the RTX 5090 has 32GB of VRAM and Apple's shared memory architecture, which can keep bigger models in memory, just doesn't have the raw processing power.

Anyway, why I argue Anthropic is in a better position (than OpenAI) is that they seem to have captured a market that may well be profitable for them as a company, specifically Claude for coding. So they just haven't burnt quite as much cash as OpenAI so aren't in as deep of a hole.

While I think local models are going to improve maassively over the next few years, running them in a data center at scale is always going to be cheaper for a company. Why? Because they can amortize their costs by running 24/7 and powering them and cooling them is simply cheaper at scale when you're talking about 1000+ engineers who otherwise might only be using their hardware ~40 hours a week.

IMHO Google is in the best position here of all the US companies, even though their models aren't the best, because their data centers are ruthlessly efficient, their homegrown TPUs will eventually catch up (and thus avoid the NVidia tax) and they simply haven't bet the farm on winning AI.

Re: DSpark: Speculative decoding accelerates LLM inference [pdf]

#113

DeepSeek continues to not only push the boundaries but also publish these incredible papers explaining how they achieved their gains - something the American labs no longer do unfortunately. Chinese labs are doing the most interesting work in AI right now.

It's almost as if ... they were what OpenAI was when it started. Sad to see but glad someone is doing is.

Re: DSpark: Speculative decoding accelerates LLM inference [pdf]

#114

Earlier quoted context omitted.

The world runs on incentives. Altruism/Self-serving are down stream of that. Wikipedia is altruistic, and serves humanity quite well.

Open-source is also altruistic. If DeepSeek does become self-serving once they get the top spot, it doesn’t take away from the altruistic contributions that they made towards open models.

And ultimately the motivation for those contributions just doesn’t matter, except to those who like to anthropomorphize company and argue about their souls.

Re: DSpark: Speculative decoding accelerates LLM inference [pdf]

#115
post #69

At this point why can't someone produce a fridge or container-sized AI appliance based on legacy chips (12nm)? I imagine this would cover 80% of corporate use cases where you need to "google-in-a-box" functionality. The state-of-the-art nanometer are impossible to achieve but if you have infinite solar energy during business hours does it really matter? Every company has a parking spot so this ASIC-like appliance cou…

The issue is that there are only so many fabs in the world that make memory. And if you want the good stuff, your easily going into 400 ~ 750b parameter models. That means at FP4 400 to 750GB memory.

Did i mention there are only so many memory makers and they are all busy printing money with HBM memory?

Intel is trying with Crescent Island, to make a 160GB GPU that uses LPDDR5X memory.

HBM takes multiple times the resources to make vs basic DDR5 memory. So by going this route, you have more memory, with the disadvantage that its only 700GB/s. VS HBM pumping out Terrabyte numbers like its nothing.

These cards is reasonably priced, may be good alternative to $10k 96GB Nvidia Blackwells... You give up on token generation (heavily memory dependent), for more memory to run larger models at home/office/company servers.

The problem is, again, there are only so many memory makers and its not like the market is flooded with DDR5 memory anymore, as the big 3 moved a lot of production to HBM.

Another approach is Sandisk making HBF ... Flash memory, like your typical NVME but designed around maximum speed. So instead of loading the models into expensive HBM memory, you use the benefits of density in Flash memory, to offload models into that. Cheaper, but slower... But it leaves your expensive HBM memory free for things like KV Cache, Active parameters, etc... So your model will be slower, but your hybrid using it. As in, faster then running a model from system memory with normal DDR memory, but not as fast as HBM.

So yea, there is a lot in development to reduce the dependance of that resource eating HBM memory. For the wafer cost of 1GB HBM, you normally got 4GB normal memory. That is why the world supply of memory dropped. Not just the insane buying but be HBM is just very inefficient in wafer usage.

Can we not use DDR4 production and create some kind of hybrid solution? Sure, but the big 3 moved away from DDR4 in favor of DDR5 a long time ago. We have competition from China with a mix of DDR4/DDR5, but they also need to scale up. Nobody expected to see a large part of the world production vanish into HBM...

Even if its about DDR4 and older nodes, ironically, most companies had been moving away from DDR4. There is only so much wafer capability in the world, to the point that companies are moving to using DDR2 ... Yea, not a typo, like 2007 DDR2! for IOT devices etc, stuff that does not need fast memory. Because even DDR3 got too expensive for them.

Its not like the old nodes are not used anymore ... Like that capacity was sitting idle. It was still in production making other stuff. The only real solution is that we need more fabs, and those take years to build. And the big 3 delayed investing in new fabs for a long time, unsure about the whole AI bubble stuff. Aka, they did not want to make a ton of fabs to end up with over capacity if the AI growth collapsed.

Re: DSpark: Speculative decoding accelerates LLM inference [pdf]

#116

Earlier quoted context omitted.

Probably because American AI companies are on the hook for quite a lot of investment money. I think they are trying to find the magical moat to justify their valuation. Revealing optimizations similar to these would pretty much reduce their competitive position.

Chinese labs are also still behind, so they’re incentivized to collaborate and have no reason to do it in private. I suspect their tune will change if they ever take the lead..

Regardless of where they are, the Chinese will always share their progress, as they're collectivist/cooperative at their core, compared to the individualistic/competitive US.

Re: DSpark: Speculative decoding accelerates LLM inference [pdf]

#117
post #37

Earlier quoted context omitted.

It'd presumably help a lot, but also when you use their endpoint they get more training data.

US labs do it too.

Name any 2 or 3 that published bleeding edge research and similar in the last 6 months.

Well I can't think of even one at the moment, to be honest might be biased but all Chinese research labs are largely oss except Alibaba now.

I am certain there are lots of American labs that claim to do it, but either they are marketting in hype since they aren't even close to the frontier or contrarily just don't make anything of significant value public/oss.

Re: DSpark: Speculative decoding accelerates LLM inference [pdf]

#118

DeepSeek continues to not only push the boundaries but also publish these incredible papers explaining how they achieved their gains - something the American labs no longer do unfortunately. Chinese labs are doing the most interesting work in AI right now.

The difference between greed and power

Re: DSpark: Speculative decoding accelerates LLM inference [pdf]

#119
post #57
post #24

Earlier quoted context omitted.

> Chinese labs are also still behind, so they’re incentivized to collaborate and have no reason to do it in private. US labs in Google, Meta and SpaceX are not leading, none of them managed to build something on par with GLM 5.2. Care to explain to me why they still don't collaborate and still choose to do it in private?

Google at least still releases open source models to the public.

Thank Apple?

Those are mostly for embedded devices and the current "sponsor" is Apple.

Re: DSpark: Speculative decoding accelerates LLM inference [pdf]

#120

These companies providing tokens, whether SOTA or not, that want to IPO are so fucked as time goes on. Can't sell their SOTA models, only slightly better than the open source models for the models they can sell, cost 20x to 50x for good models, a TAM that consists almost solely of developers, with no customer of theirs actually boasting increased profits as a result of AI... I fear their time to IPO may have passed.

The question is even, was there EVER a time for an IPO?

If the business model requires hundreds of billions to get the required quality (R&D but also infrastructure to collect data and train, either purchased or rented to 3rd party) while "only" dozens of billions can be earned back (as costs still exist to earn, it's not free once models are trained), then maybe there NEVER was nor till be a good time for an IPO in a rational market.

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