Damn it I was expecting something interesting about the ps4 controller. Not some more junk about AI. Such a rugpull
A few words on DS4
91–100 of 197 posts
Re: A few words on DS4
#92I'm very curious where we will saturate the curve on "enough" intelligence for coding. At some point, you can let a less smart model hammer at a problem for longer and get to the same result, and as long as you are not involved it comes to the same thing. I feel like DeepSeek V4 Pro is nearly there. Maybe Flash is too. Once we hit that point, I am curious how much of Anthropic's current business model falls apart? So…
I imagine we'll get to "good enough" for hobbyist programmers fairly quickly, but businesses will still be willing to pay more for faster and smarter. Why make your programmers wait?
> Why make your programmers wait?
That depends on where the methodology goes. But more and more it's hands off. If the trajectory continues it won't matter because nobody is sitting their waiting / watching the LLM code anyway. It is all happening in the background. We might see hybrid approaches where the weaker / cheaper agent tries to solve it and just "asks for help" from the more expensive agent when it needs it etc.
Re: A few words on DS4
#93Re: A few words on DS4
#94Earlier quoted context omitted.
>The blog post implies that it currently requires 96GB of VRAM. From the Github page it seems it only supports Apple and DGX Spark. I have 128 GB of RAM and a 3090 but it probably won't work.
FYI, llama.cpp (which antirez/ds4 is inspired by) supports system ram. E.g. [1] is a good guide for running a similar-sized model with 128gb ram and a 3090-sized GPU. [1] https://unsloth.ai/docs/models/tutorials/minimax-m27 (Unsloth's deepseek-v4 support is still WIP)
Thanks, I can run Qwen 3.6 27B with vllm, but I was curious about antirez tool.
Re: A few words on DS4
#95I want something like this but not only for my own computer but also for client projects or stuff I might run in cloud GPUs. Because the core idea of having a strong model that is efficient and doesn't require a cluster still applies to a lot of business cases. I am hoping something like this can work in batch mode.
Right now I feel like a 4bit Qwen 3.6 27B with MTP is one of the best for agentic tool calling for some smart voice agents in an H200. I wonder if DS4 Flash being using 80b at 2 bit with 13b active and MTP could be even faster and smarter and allow more concurrent sequences?
This special 2bit quantization seems like a big deal.
Re: A few words on DS4
#96I'm very curious where we will saturate the curve on "enough" intelligence for coding. At some point, you can let a less smart model hammer at a problem for longer and get to the same result, and as long as you are not involved it comes to the same thing. I feel like DeepSeek V4 Pro is nearly there. Maybe Flash is too. Once we hit that point, I am curious how much of Anthropic's current business model falls apart? So…
Kilo (the open source coding agent) tested Deepseek v4 Pro and Flash vs Opus 4.7 and Kimi K2[1].
It did ok, but scored substantially less than Opus. It also cost nearly as much, even with the current launch promo pricing for Deepseek.
That cost is interesting - I've seen similar things with Sonnet vs Opus, and in my own benchmarking there are some models that benchmark well, seem to have a good price but use so many tokens they cost just as much as "more expensive" models.
[1] https://blog.kilo.ai/p/we-tested-deepseek-v4-pro-and-flash
Re: A few words on DS4
#97Great to find this narrow focused thing: > 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. > This project would not exist without llama.c…
> AMD ROCm is only supported in the rocm branch.
Has anybody tried it? There is a lot of emphasis on MacBook Pro in this thread, but I would like to use it with an AMD Halo Strix with 128GB of unified RAM.
Re: A few words on DS4
#98Earlier quoted context omitted.
Thanks. Outside of LLM circles, DS4 is usually a video game controller.
Or a car from Citroen
Technically DS is an independent sibling of Citroën within Stellantis, a sprawling car conglomerate that owns a dog’s dinner of car brands in Europe and USA.
Re: A few words on DS4
#99DwarfStar4 is a small LLM inference runtime that can run DeepSeek 4. The blog post implies that it currently requires 96GB of VRAM. For others who are lacking context :-)
> The blog post implies that it currently requires 96GB of VRAM.
Has anyone tested what happens if you try and run this on lower-RAM Macs? It might work and just be a bit slower as it falls back on fetching model layers from storage.
Re: A few words on DS4
#100With "intelligence" (or whatever you want to call it) and speed both seeming to ramp up quickly with local models I wonder what the growth rate and ceiling(?) might be in this space. Will this kind of iq and performance work with just e.g: 16GB RAM in a couple years? Is there a new kind of Moore's law to be defined here?
Squeezing a model like this complete with 'big model smell' into 16GB...Honestly it's not even possible or feasibly possible today. It'll require some kind of: - breakthrough in architecture or - breakthrough in hardware or - some breakthrough quantisization technique The problem is that all the parameters need to be in memory, even the ones that aren't active (say for Mixture Of Expert Models) because switching para…
"That’s where EMO comes in.
We show that EMO – a 1B-active, 14B-total-parameter (8-expert active, 128-expert total) MoE trained on 1 trillion tokens – supports selective expert use: for a given task or domain, we can use only a small subset of experts (just 12.5% of total experts) while retaining near full-model performance."