Comment by canyon289

> The key advantage of doing batched inference at a huge scale is that once you maximize parallelism and sharding, your model parameters and the memory bandwidth associated with them are essentially free (since at any given moment they're being shared among a huge amount of requests!)

Kind of unrelated, but this comment made me wonder when we will start seeing side channel attacks that force queries to leak into each other.

mmap is not free. It just moves bandwidth around.

> Quite the opposite.

Unless something has dramatically changed, the model is stateless. The context cache needs to be injected before the new prompt, but for what I understand (and please do correct me if I'm wrong) the the context cache isn't that big, like in the order of a few tens of kilobytes. Plus the cache saves seconds of GPU time, so having an extra 100ms of latency is nothing compare to a cache miss. so a broad cache is much much better than a narrow local cache.

But! even if its larger, Your bottleneck isn't the network, its waiting on the GPUs to be free[1]. So whilst having the cache really close ie in the same rack, or same machine, will give the best performance, it will limit your scale (because the cache is only effective for a small number of users)

[1] a 100megs of data shared over the same datacentre network every 2-3 seconds per node isn't that much, especially if you have a partitioned network (ie like AWS where you have a block network and a "network" network)

Yes. And batched inference is a thing, where intelligent grouping/bin packing and routing of requests happens. I expect a good amount of "secret sauce" is at this layer.

Here's an entry-level link I found quickly on Google, OP: https://medium.com/@wearegap/a-brief-introduction-to-optimiz...

Really? So the system recognises someone asked the same question and serves the same answer? And who on earth shares the exact same context?

I mean i get the idea but sounds so incredibly rare it would mean absolutely nothing optimisation wise.

The only one who can stop Google is Google.

They’ll definitely have the best model, but there is a chance they will f*up the product / integration into their products.

Google will win the LLM game if the LLM game is about compute, which is the common wisdom and maybe true, but not foreordained by God. There's an argument that if compute was the dominant term that Google would never have been anything but leading by a lot.

Personally right now I see one clear leader and one group going 0-99 like a five sigma cosmic ray: Anthropic and the PRC. But this is because I believe/know that all the benchmarks are gamed as hell, its like asking if a movie star had cosmetic surgery. On quality, Opus 4 is 15x the cost and sold out / backordered. Qwen 3 is arguably in next place.

In both of those cases, extreme quality expert labeling at scale (assisted by the tool) seems to be the secret sauce.

Which is how it would play out if history is any guide: when compute as a scaling lever starts to flatten, you expert label like its 1987 and claim its compute and algorithms until the government wises up and stops treating your success persobally as a national security priority. It's the easiest trillion Xi Xianping ever made: pretending to think LLMs are AGI too, fast following for pennies on the dollar, and propping up a stock market bubble to go with the fentanyl crisis? 9-D chess. It's what I would do about AI if I were China.

Time will tell.

Yeah honestly. They could just try selling solutions and SLAs combining their TPU hardware with on-prem SOTA models and practically dominate enterprise. From what I understand, that's GCP's gameplay too for most regulated enterprise clients.

Hasn’t the Inferentia chip been around long enough to make the same argument? AWS and Google probably have the same order of magnitude of their own custom chips

But they’re ASICs so any big architecture changes will be painful for them right?

I have met those supersmart specialists but in my experience there are also a lot of smart people who are more generalists.

The real answer is likely internal company politics and priorities. Google certainly has people with the technical skills to solve it but do they care and if they care can they allocate those skilled people to the task?

I think the problem I see with this type of response is that it doesn't take into context the waste of resources involved. If the 700M users per week is legitimate then my question to you is: how many of those invocations are worth the cost of resources that are spent, in the name of things that are truly productive?

And if AI was truly the holy grail that it's being sold as then there wouldn't be 700M users per week wasting all of these resources as heavily as we are because generative AI would have already solved for something better. It really does seem like these platforms are, and won't be, anywhere as useful as they're continuously claimed to be.

Just like Tesla FSD, we keep hearing about a "breakaway" model and the broken record of AGI. Instead of getting anything exceptionally better we seem to be getting models tuned for benchmarks and only marginal improvements.

I really try to limit what I'm using an LLM for these days. And not simply because of the resource pigs they are, but because it's also often a time sink. I spent an hour today testing out GPT-5 and asking it about a specific problem I was solving for using only 2 well documented technologies. After that hour it had hallucinated about a half dozen assumptions that were completely incorrect. One so obvious that I couldn't understand how it had gotten it so wrong. This particular technology, by default, consumes raw SSE. But GPT-5, even after telling it that it was wrong, continued to give me examples that were in a lot of ways worse and kept resorting to telling me to validate my server responses were JSON formatted in a particularly odd way.

Instead of continuing to waste my time correcting the model I just went back to reading the docs and GitHub issues to figure out the problem I was solving for. And that led me down a dark chain of thought: so what happens when the "teaching" mode rethinks history, or math fundamentals?

I'm sure a lot of people think ChatGPT is incredibly useful. And a lot of people are bought into not wanting to miss the boat, especially those who don't have any clue to how it works and what it takes to execute any given prompt. I actually think LLMs have a trajectory that will be similar to social media. The curve is different and I, hopefully, don't think we've seen the most useful aspects of it come to fruition as of yet. But I do think that if OpenAI is serving 700M users per week then, once again, we are the product. Because if AI could actually displace workers en masse today you wouldn't have access to it for $20/month. And they wouldn't offer it to you at 50% off for the next 3 months when you go to hit the cancel button. In fact, if it could do most of the things executives are claiming then you wouldn't have access to it at all. But, again, the users are the product - in very much the same way social media played into.

Finally, I'd surmise that of those 700M weekly users less than 10% of those sessions are being used for anything productive that you've mentioned and I'd place a high wager that the 10% is wildly conservative. I could be wrong, but again - we'd know about that if it were the actual truth.

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They won’t be honest and explain it to you but I will. Takes like the one you’re responding to are from loathsome pessimistic anti-llm people that are so far detached from reality they can just confidently assert things that have no bearing on truth or evidence. It’s a coping mechanism and it’s basically a prolific mental illness at this point

The few problems humanity has that need to be solved:

1. How to identify humanity's needs on all levels, including cosmic ones...(we're in the Space Age so we need to prepare ourselves for meeting beings from other places)

2. How to meet all of humanity's needs

Pointing this out regularly is probably necessary because the issue isn't why people are choosing what they're doing...it's that our systems actively disincentivize collectibely addressing these two problems in a way that doesn't sacrifice people's wellbeing/lives... and most people don't even think about it like this.

The notion that simply pretending to not understand that I was making a value judgment about worth is an argument is tiring.

Just wait until the two combine.