Testing it now. At the very least, competitive with DS4-Flash indeed. On my small (and per Sol's words, _very_ semantically dense) C test codebase, it found things that only gpt-5.2 managed to find back in the day, but also made a stupidly incorrect initial observation that a memfd_create()/mmap was used for IPC (funnily enough - sol missed that as well in its review, until I pointed it out). Re: the claims vs deepseek v4 - both flash and pro are expected to get a "general availability" release very soon (i.e. well-"post-trained"), so things can change in a... well, flash, as per usual in the current environment.
Running deepseek flash on something locally now, this will have to wait a bit. I still stand by my initial quick assessment - looks capable. Some people on r/localllama also reported loops. We'll see in ~10 hours. Hopefully I haven't terribly mislead people.
Looks impressive, and this size fits achievable home hardware.
That said, if someone would kindly quantise this down for the 64GB paupers, that would be appreciated. (I know there’s likely degradation, but some people reported good results with a 2 bit version of Qwen 3.5 122B, and this is starting from a higher point. Would be interesting to try, at least.)
Thanks for flagging. From the few benchmarks I can find, it looks there or thereabouts with Qwen 3.6-35B-A3B, or maybe a touch below. I'm interested to compare a model that is a big jump larger with pretty impressive benchmarks, but more heavily quantized to fit.
Really impressive signal that this 128B model can beat DeepSeek V4 (1.6T) on most coding benchmarks!
Also, I really like Poolside's habit to compare not only to other top models in its weight class (others don't do it, looking at you Mistral), but also to the very top open-weight models, even much bigger ones like the 2.5T Kimi-K3!
Whoa whoa whoa, 118b params, 8b active MOE, long context reasoning, open weights - music to my ears. Hadn't heard of this lab before but I am very excited, will definitely try this out tomorrow - this is a real sweet spot I think in terms of model size and performance.
A lot of people are testing it, and reporting disappointed results / benchmaxxxing claim. But do not realize that thinking has a issue with the default configuration.
Important - make sure that THINKING is enabled. By default it wasn't although I was passing the flag --default-chat-template-kwargs '{"enable_thinking": true}' in vllm recipe. The generation_config.json file that is included has by default max_new_tokens as 32k which seems to be cutting off thinking altogether so increase it.
At first I was very disappointed with the output I was seeing, but once thinking is enabled, the code quality seems to be MUCH better. More real world testing to be done.
Even more important, subjectively, is that this model will run very well on Strix Halo (e.g. Framework Desktop), DGX Spark kinds of devices.
Looking forward to Unsloth dynamic mtp quants.
P.S. Looking at the HF release they already offer Q4_K_M and DFlash drafter for speculative decoding!
This is exactly the kind of model that's been needed in the middle. Realistically self-hosted, Good Enough intelligence, MoE so it's fast on limited bandwidth systems like Strix Halo and DGX Spark.
For a while there's been nothing to run on my Strix Halo that's notably better than what I can run on my dual 32GB GPU desktop (Gemma 4 or Qwen 3.6 dense models), but this seems likely to be the step up in size that actually works better than those.
I love this. Is it possible to give a feel of how this stacks up to the good old Opus 4.5 in coding quality? For me that was the turning point where agentic coding in Claude Code etc became usable. Have we hit that threshold?
I am about 1 hour into using it with pi.dev. Do you have thinking on high? It is doing good but at one point i had to stop it and say 'you're overthinking this' haha
Yes full send mode on thinking. I have moved on from watching my agents and I don't really care how it thinks. I look at the end result and so far this thing has been blowing me away. No way this is as good as it is this small and fast. Outside Fable, this might be the best thing I've ever used.
"What we've done in this model is not necessarily add more intelligence, but improve the behaviors that lead to a more capable model: more verification, less taking things for granted, not declaring victory early, and being more persistent.”
Anyways, keep 'em coming.
edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.
Running deepseek flash on something locally now, this will have to wait a bit. I still stand by my initial quick assessment - looks capable. Some people on r/localllama also reported loops. We'll see in ~10 hours. Hopefully I haven't terribly mislead people.
That said, if someone would kindly quantise this down for the 64GB paupers, that would be appreciated. (I know there’s likely degradation, but some people reported good results with a 2 bit version of Qwen 3.5 122B, and this is starting from a higher point. Would be interesting to try, at least.)
Edit: someone in the process of doing so: https://huggingface.co/vcruz305/Laguna-S-2.1-GGUF
https://huggingface.co/poolside/Laguna-XS-2.1-GGUF/tree/main
https://github.com/vllm-project/llm-compressor
my setup to help you on your way: https://github.com/verdverm/quantr
Though it seems these will not be needed as Poolside has published quants & dflash with their models.
Also, I really like Poolside's habit to compare not only to other top models in its weight class (others don't do it, looking at you Mistral), but also to the very top open-weight models, even much bigger ones like the 2.5T Kimi-K3!
https://github.com/mozilla-ai/otari/pull/348
The pricing here is incredible. This is the first US release that's competitive with DeepSeek V4 Flash. Very excited about this.
A lot of people are testing it, and reporting disappointed results / benchmaxxxing claim. But do not realize that thinking has a issue with the default configuration.
Important - make sure that THINKING is enabled. By default it wasn't although I was passing the flag --default-chat-template-kwargs '{"enable_thinking": true}' in vllm recipe. The generation_config.json file that is included has by default max_new_tokens as 32k which seems to be cutting off thinking altogether so increase it. At first I was very disappointed with the output I was seeing, but once thinking is enabled, the code quality seems to be MUCH better. More real world testing to be done.
https://www.reddit.com/r/LocalLLaMA/comments/1v2pg99/laguna_...
Even more important, subjectively, is that this model will run very well on Strix Halo (e.g. Framework Desktop), DGX Spark kinds of devices. Looking forward to Unsloth dynamic mtp quants.
P.S. Looking at the HF release they already offer Q4_K_M and DFlash drafter for speculative decoding!
For a while there's been nothing to run on my Strix Halo that's notably better than what I can run on my dual 32GB GPU desktop (Gemma 4 or Qwen 3.6 dense models), but this seems likely to be the step up in size that actually works better than those.
https://github.com/njbrake/laguna-otari-bridge
+
https://poolside.ai/assets/laguna/laguna-m1-xs2-technical-re...
This is pretty impressive.
similar performance to deepseek v4, inkling at size of nemotron 3 super (!)