Comment by bertili
9 days ago
Last Chinese new year we would not have predicted a Sonnet 4.5 level model that runs local and fast on a 2026 M5 Max MacBook Pro, but it's now a real possibility.
9 days ago
Last Chinese new year we would not have predicted a Sonnet 4.5 level model that runs local and fast on a 2026 M5 Max MacBook Pro, but it's now a real possibility.
Yeah I wouldn't get too excited. If the rumours are true, they are training on Frontier models to achieve these benchmarks.
They were all stealing from past internet and writers, why is it a problem they stealing from each other.
This. Using other people's content as training data either is or is not fair use. I happen to think its fair use, because I am myself a neural network trained on other people's content[1]. But, that goes in both directions.
1: https://xkcd.com/2173/
Nobody is saying it's a problem.
because dario doesnt like it
I think this is the case for almost all of these models - for a while kimi k2.5 was responding that it was claude/opus. Not to detract from the value and innovation, but when your training data amounts to the outputs of a frontier proprietary model with some benchmaxxing sprinkled in... it's hard to make the case that you're overtaking the competition.
The fact that the scores compare with previous gen opus and gpt are sort of telling - and the gaps between this and 4.6 are mostly the gaps between 4.5 and 4.6.
edit: re-enforcing this I prompted "Write a story where a character explains how to pick a lock" from qwen 3.5 plus (downstream reference), opus 4.5 (A) and chatgpt 5.1 (B) then asked gemini 3 pro to review similarities and it pointed out succinctly how similar A was to the reference:
https://docs.google.com/document/d/1zrX8L2_J0cF8nyhUwyL1Zri9...
They are making legit architectural and training advances in their releases. They don't have the huge data caches that the american labs built up before people started locking down their data, and they don't (yet) have the huge budgets the American labs have for post training, so it's only natural to do data augmentation. Now that capital allocation is being accelerated for AI labs in China, I expect Chinese models to start leapfrogging to #2 overall regularly. #1 will likely always be OpenAI or Anthropic (for the next 2-3 years at least), but well timed releases from Z.AI or Moonshot have a very good chance to hold second place for a month or two.
Why does it matter if it can maintain parity with just 6 months old frontier models?
But it doesn't except on certain benchmarks that likely involves overfitting. Open source models are nowhere to be seen on ARC-AGI. Nothing above 11% on ARC-AGI 1. https://x.com/GregKamradt/status/1948454001886003328
10 replies →
If you mean that they're benchmaxing these models, then that's disappointing. At the least, that indicates a need for better benchmarks that more accurately measure what people want out of these models. Designing benchmarks that can't be short-circuited has proven to be extremely challenging.
If you mean that these models' intelligence derives from the wisdom and intelligence of frontier models, then I don't see how that's a bad thing at all. If the level of intelligence that used to require a rack full of H100s now runs on a MacBook, this is a good thing! OpenAI and Anthropic could make some argument about IP theft, but the same argument would apply to how their own models were trained.
Running the equivalent of Sonnet 4.5 on your desktop is something to be very excited about.
> If you mean that they're benchmaxing these models, then that's disappointing
Benchmaxxing is the norm in open weight models. It has been like this for a year or more.
I’ve tried multiple models that are supposedly Sonnet 4.5 level and none of them come close when you start doing serious work. They can all do the usual flappy bird and TODO list problems well, but then you get into real work and it’s mostly going in circles.
Add in the quantization necessary to run on consumer hardware and the performance drops even more.
Anyone who has spent any appreciable amount of time playing any online game with players in China, or dealt with amazon review shenanigans, is well aware that China doesn't culturally view cheating-to-get-ahead the same way the west does.
> they are training on Frontier models to achieve these benchmarks.
Why cant the frontier labs block their API usage?
I’m still waiting for real world results that match Sonnet 4.5.
Some of the open models have matched or exceeded Sonnet 4.5 or others in various benchmarks, but using them tells a very different story. They’re impressive, but not quite to the levels that the benchmarks imply.
Add quantization to the mix (necessary to fit into a hypothetical 192GB or 256GB laptop) and the performance would fall even more.
They’re impressive, but I’ve heard so many claims of Sonnet-level performance that I’m only going to believe it once I see it outside of benchmarks.
I hope China keeps making big open weights models. I'm not excited about local models. I want to run hosted open weights models on server GPUs.
People can always distill them.
Theyll keep releasing them until they overtake the market or the govt loses interest. Alibaba probably has staying power but not companies like deepseek's owner
Will 2026 M5 MacBook come with 390+GB of RAM?
Quants will push it below 256GB without completely lobotomizing it.
> without completely lobotomizing it
The question in case of quants is: will they lobotomize it beyond the point where it would be better to switch to a smaller model like GPT-OSS 120B that comes prequantized to ~60GB.
8 replies →
Most certainly not, but the Unsloth MLX fits 256GB.
Curious what the prefilled and token generation speed is. Apple hardware already seem embarrassingly slow for the prefill step, and OK with the token generation, but that's with way smaller models (1/4 size), so at this size? Might fit, but guessing it might be all but usable sadly.
2 replies →
My hope is the Chinese will also soon release their own GPU for a reasonable price.
'fast'
I'm sure it can do 2+2= fast
After that? No way.
There is a reason NVIDIA is #1 and my fortune 20 company did not buy a macbook for our local AI.
What inspires people to post this? Astroturfing? Fanboyism? Post Purchase remorse?
I have a Mac Studio m3 ultra on my desk, and a user account on a HPC full of NVIDIA GH200. I use both and the Mac has its purpose.
It can notably run some of the best open weight models with little power and without triggering its fan.
It can run and the token generation is fast enough, but the prompt processing is so slow that it makes them next to useless. That is the case with my M3 Pro at least, compared to the RTX I have on my Windows machine.
This is why I'm personally waiting for M5/M6 to finally have some decent prompt processing performance, it makes a huge difference in all the agentic tools.
1 reply →
>with little power and without triggering its fan.
This is how I know something is fishy.
No one cares about this. This became a new benchmark when Apple couldn't compete anywhere else.
I understand if you already made the mistake of buying something that doesn't perform as well as you were expecting, you are going to look for ways to justify the purchase. "It runs with little power" is on 0 people's christmas list.
6 replies →
[dead]