A crow exhibits some degree of intelligence in what is a very small brain compared to humans. There is overlap in the problem solving skills of the dumbest humans and the smartest crows.
So the question is: what is that? Yann LeCun seems to think it’s what we now call world models. World models predict behaviour as opposed to predicting structured data (like language.)
If your model can predict how some world works (how you define world largely depends on the size of your training data), then in theory it is able to reason about cause and effect.
If you can combine cause and effect reasoning with language, you might get something truly intelligent.
That’s where things seem to be going. Once we have a prototype of that system, there will be many questions about how much data you really need. We’ve seen how even shrinking LLMs with 1-bit quantization can lead to models that exhibit a fairly strong understanding of language.
I don’t think it’s unreasonable to expect to see some very intelligent low (relatively) memory AI systems in the next couple years.
> We support the following backends:
Metal is our primary target. Starting from MacBooks with 96GB of RAM.
NVIDIA CUDA with special care for the DGX Spark.
AMD ROCm is only supported in the rocm branch. It is kept separate from main
since I (antirez) don't have direct hardware access, so the community rebases
the branch as needed.
Edit: aww, doesn't seem to support offloading to system RAM[0] (yet)
[0] https://github.com/antirez/ds4/issues/108
Guess I'll have to keep watching the llama.cpp issue[1]
Is it true? We'll see, in a few years.
Antirez explained the dev process when he posted a pure C implementation of the Flux 2 Klein image gen model, at https://news.ycombinator.com/item?id=46670279
> Gentle reminder on how, in the recent DS4 fiesta, not just me but every other contributor found GPT 5.5 able to help immensely and Opus completely useless.
I've noticed the same for lower level squeezing-as-much-performance-as-possible code work.
I also don’t have time to do much personal coding outside of work, so I haven’t subscribed to a personal one yet. But I intend to go for Codex just to balance the Claude at work and also because of the hostile moves from Anthropic toward their consumer business.
prefill: 30.91 t/s, generation: 29.58 t/s
prefill: 121.76 t/s, generation: 47.85 t/s
Main target seems to be Apple's Metal, so makes sense. Might be fun to see how fast one could make it go though :) The model seems really good too, even though it's in IQ2.
But I found its tool calling is reliable than other oss models I tried. I assume that it attributes to interleaved thinking. Its reasoning effort is adjusted automatically by queries. I enjoy reading these reasoning traces from open models because you can't see them from proprietary models.
I would love to try DS4 so bad. Well, I don't have a machine for it. I will just stick to openrouter. I wish I can run a competitive oss model on 32GB machine in 3 years.
You could try DS4 on that machine anyway and see how gracefully it degrades (assuming that it runs and doesn't just OOM immediately). Experimenting with 36GB/48GB/64GB would also be nice; they might be able to gain some compute throughput back by batching multiple sessions together (though obviously at the expense of speed for any single session).
It's so hard to predict what size the open-weight models will be, even in 6 months time. Will a 96GB machine turn out to be a complete waste of money? Who knows.
FYI, this to me points to an inference bug, bad sampling, or a non-native quant. OpenRouter is known to route requests to absolutely terrible, borked implementations. A model like DeepSeek V4 Flash shouldn't be making syntax errors like this.
Also have enjoyed playing with https://huggingface.co/HuggingFaceTB/nanowhale-100m-base (but early days for me understanding this space)
The long context reasoning is something I haven't even seen in frontier models - I was running at 124k tokens earlier and it was still just buzzing along with no issues or fatigue.
I am amazed at how well it works, I'm using it right now for some pretty complex frontend work, and it is much much faster than, for example running a dense 27b or 31b model (like qwen or gemma) for me (The benefits of MoE) - but the long context capabilities have been what have been absolutely flooring me.
Super excited about this project and hope Antirez can keep himself from burning out - i've been following the repo pretty closely and there are a ton of PR's flooding in and it seems like he's had to do a lot of filtering out of slop code.
For others who are lacking context :-)