I have been toying with a 2.5D engine in C on on top of raylib and using DeepSeek as companion in between.

It's thinking transcripts in OpenaCode are transparent and mind boggling to look at things it would consider in its thought process. Very long to read but none of them useless or meaningless.

Always happened that I discovered an assumption that I didn't think about or was wrong but DeepSeek flags it in its thought process and then in final output it would "align" to my flawed request and I'll tell it wait, I saw you thought so and so too and that's correct I made a mistake let's consider that aspect too.

For others who are lacking context :-)

I do wonder, though, if another agent is really needed. I've been driving it with Pi (Claude Code's system prompt is far too heavy given the prefill speeds) and it's been great. OpenCode is another good option. Is there anything else to gain from another similar tool specific to Deepseek 4?

Once we hit that point, I am curious how much of Anthropic's current business model falls apart? So far it's always been clear that you just pay for the most intelligent model you can get because it is worth it. It now seems clear to me that there is limited runway on that concept. It is just a question of how long that runway is. I honestly wonder how much of their frantic push to broaden out into enterprise / productivity is because they see this writing on the wall already.

> 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]

[1] https://github.com/ggml-org/llama.cpp/issues/22319

The code seems based on llama.cpp and GGML.

I don't fully understand why it is a standalone project. The readme discusses this: DwarfStar 4 is a small native inference engine specific for DeepSeek V4 Flash. It is intentionally narrow: ...

I think the only bigger difference in DeepSeek V4 vs other models is maybe the type of self-attention. And that leads to: KV cache is actually a first-class disk citizen.

But I still feel like those changes could have been implemented as part of some of the other local engines.

I also assume more models will come out, not just from DeepSeek but also from others, and they might share similar self-attention approaches, that would benefit from a similar KV cache implementation.

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.

Wink wink, nudge nudge.

I have a feeling most cybersec researchers would only be interested in negative values of "reduce" :D

> 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.

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.

Sure, MoE models have more knowledge, but extreme quantization may negate the benefits. And generally for coding tasks, you don't need a model that has memorized all the irrelevant trivia like, I don't know, the list of all villages in country X. DS4 also seems to run much slower on Mac Studio Ultra, which appears to be more or less in the same price range as RTX 5090. RTX 5090 gives me 50-60 tok/sec and 260k context with Unsloth's 5-bit quantization (only some layers are 5-bit too) and an 8-bit KV cache; prefill is instant too. It works flawlessly in OpenCode.

If you already have a spare high-end Mac, I can see the benefit, but I'm not sure it's a good configuration overall. Unless Qwen3.6 is more benchmaxxed than DS4 :)

Also have enjoyed playing with https://huggingface.co/HuggingFaceTB/nanowhale-100m-base (but early days for me understanding this space)

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.

> Starting from MacBooks with 96GB of RAM.

... oh. And I thought I bought a lot with 48 GB.