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GLM 5.2 beats Claude in our benchmarks

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Re: GLM 5.2 beats Claude in our benchmarks

#471

Post by Roark66 »

throwawayffffas wrote: post #388
hedora wrote: post #288

Earlier quoted context omitted.

Is there a secure way to use GLM without spending $10K’s for local HW? I “only” have a 128GiB inference machine, and don’t really trust anthropic not to steal my IP over time. I see no reason to trust Z.ai more than other vendors.

Kind of, you need at least 256 gb of vram and 24-40 gb of vram to run the 2bit quantization, because it's a moe you just need the expert to fit in vram to get significant improvement over a pure CPU setup. At 2bits though expect significant quality loss.

2bits is a joke for serious work. You'd be better with Qwen3.6 under 30G probably.

But there are EU only providers for GLM5.2. For example tensorx. Depending on your definition of "secure" it may be acceptable.

Re: GLM 5.2 beats Claude in our benchmarks

#472

Post by throwawayffffas »

Roark66 wrote: post #471
throwawayffffas wrote: post #388

Earlier quoted context omitted.

Kind of, you need at least 256 gb of vram and 24-40 gb of vram to run the 2bit quantization, because it's a moe you just need the expert to fit in vram to get significant improvement over a pure CPU setup. At 2bits though expect significant quality loss.

2bits is a joke for serious work. You'd be better with Qwen3.6 under 30G probably. But there are EU only providers for GLM5.2. For example tensorx. Depending on your definition of "secure" it may be acceptable.

> 2bits is a joke for serious work.

I have not tried it but I will take your word on it. I don't think Qwen3.6 cuts it for large scale coding work. Reading issues, reading code sure, but biting into large issues no, it goes off the track consistently.

Depending on budget it may also be affordable to spin up servers to run it on demand.

Re: GLM 5.2 beats Claude in our benchmarks

#475

Post by DrScientist »

akie wrote: post #459
DrScientist wrote: post #427

Earlier quoted context omitted.

Given GLM is open weight - all you need is one company to take the taalas approach ( model on hardware ), and you're sorted right? https://taalas.com/products/

Yeah I completely agree. But this is much larger model than the 8B one they put on a chip, so that's probably an engineering challenge for now. Also, how expensive would it be?

No idea - AI tells me under 30 dollars per unit for the ROM with development costs in the low 10's of millions.

If that's anywhere near right then it seems like a no brainer.

Re: GLM 5.2 beats Claude in our benchmarks

#477

Post by [deleted] »

nl wrote: post #327
jfaat wrote: post #262

Earlier quoted context omitted.

> but if you only want to use the best model available, it isn't there yet I'm trying to wrap my head around exactly why so may people seem to want the best model available when it has recently become clear that most halfway decent models can write damn good code for a fraction of the price. And the frontier models get nerfed constantly so you with open weight you can get something slightly less performant but way mo…

> most halfway decent models can write damn good code for a fraction of the price. The difference is how the model is used. With Opus you can give it a long-horizon task (eg build an entire feature) and it will plan it out and implement it and almost always stay on task. This is what people mean when they say "agentic tasks" With the lessor models the code is fine, but they need something else to plan what needs to b…

[deleted]

Re: GLM 5.2 beats Claude in our benchmarks

#478

Post by orangeisthe »

throwawayffffas wrote: post #169
horsawlarway wrote: post #147

Earlier quoted context omitted.

My increasing frustration with these plans is the harness lock in. Anthropic won't even let you run "claude -p [prompt]" any more... They bill it at api rates. So if you're trying to automate the ai (and seriously, that's the point) the subsidized plans are crippled.

Z.ai does not lock you in to any harness.

Neither does chatgpt. And is the harness lock-in such massive problem that you would pay 20x more?

Re: GLM 5.2 beats Claude in our benchmarks

#480

Post by kibwen »

coldtea wrote: post #402
kibwen wrote: post #208

Earlier quoted context omitted.

It is self-evident. Bringing up Kolmogorov complexity is irrelevant, we're talking about rote memorization, but if you can't ignore the given example then replace "digits of pi" with "bits of output from a true random number generator". There's an infinite amount of information that we could shove into a model, and a finite amount of bits with which to store any of that information such that it can be usefully recall…

"rote memorization" is not the right way to describe how an LLM works. The memorization of say 100000 world facts through training texts, which enrich model associations all around, is absolutely not the same as rote memorization on 10^50 digits of pi. Not for a human, and even more so, not for an LLM. An LLM trained with digits of pi and one trained with books and posts, even if they both have the exact same amount…

> actual human books vs possible permutations of characters and spaces

You're making the assumption that anything produced by a human necessarily contains more useful information than random noise does. This is false. Even when only considering human intelligence, it's entirely possible to absorb information that makes you stupider, not smarter; learning is only valuable if you actually learn the right things.

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