alt.hn

7/21/2026 at 5:17:06 PM

Laguna S 2.1

 https://poolside.ai/blog/introducing-laguna-s-2-1

by rexledesma

7/21/2026 at 7:57:19 PM

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.

Anyways, keep 'em coming.

by Lwerewolf

7/21/2026 at 8:06:29 PM

What harness/quant did you use for testing?

by ilc

7/21/2026 at 8:20:11 PM

nvfp4 mlx, literally barebones pi.

edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.

by Lwerewolf

7/21/2026 at 9:19:02 PM

What inference server are you using? They have a custom branch for llama.cpp, but I wouldn't be surprised at all if it still needs fixing.

by sosodev

7/21/2026 at 10:06:40 PM

This: https://github.com/Blaizzy/mlx-lm/tree/pc/add-lg ...and this is what I should probably wait for (not sure why it's in vlm): https://github.com/Blaizzy/mlx-vlm/tree/pc/laguna-s-nvfp4 ...or perhaps I should've just used the gguf with the provided llama.cpp instead of trying to run the nvfp4-mlx from the get go, but where's the chaos in that :)

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.

by Lwerewolf

7/21/2026 at 8:15:08 PM

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.)

Edit: someone in the process of doing so: https://huggingface.co/vcruz305/Laguna-S-2.1-GGUF

by mft_

7/21/2026 at 8:57:40 PM

They have also published smaller 33B model called Laguna XS 2.1, its Q4 gguf is 20GB.

https://huggingface.co/poolside/Laguna-XS-2.1-GGUF/tree/main

by yogeshp

7/21/2026 at 9:27:28 PM

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.

by mft_

7/21/2026 at 10:05:02 PM

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!

by aubanel

7/21/2026 at 10:15:29 PM

This is fantastic work, really impressive is an understatement. I really hope this sets a new DeepSeek-esque standard and starts another the death knell for companies continuing to cosplay as frontier labs (like Cohere).

by spelk

7/21/2026 at 7:51:27 PM

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.

by kamranjon

7/21/2026 at 8:25:22 PM

If the numbers are legitimate then our prayers have been heard

by svclaws

7/21/2026 at 8:03:23 PM

Incredible. This is definitely the launch of the day. Just crushing Google's releases.

The pricing here is incredible. This is the first US release that's competitive with DeepSeek V4 Flash. Very excited about this.

by mchusma

7/21/2026 at 9:23:11 PM

!! Be careful when testing the model.

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_...

by benjiro29

7/21/2026 at 10:16:12 PM

Thanks for posting this, it made a huge difference tweaking the recipe.

by nshotton

7/21/2026 at 10:00:24 PM

Even the official provider on OpenRouter seems to have this issue. Hope it's an easy fix for them.

by voxgen

7/21/2026 at 7:21:21 PM

Model Looks amazing!

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!

by Iolaum

7/21/2026 at 8:50:11 PM

I hope all models going forward come with a dflash drafter so we don't have to train one up separately.

by verdverm

7/21/2026 at 7:44:20 PM

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.

by SwellJoe

7/21/2026 at 7:47:48 PM

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?

by river_otter

7/21/2026 at 7:56:42 PM

Having played with it for like 3 hours now....I'm probably moving from CC to this

by megavon

7/21/2026 at 7:58:01 PM

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

by river_otter

7/21/2026 at 8:50:08 PM

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.

by megavon

7/21/2026 at 9:04:43 PM

One hour in, no more Codex for me. This thing rips.

by fingerprinter

7/21/2026 at 6:51:29 PM

This is INSANE. How did they do this?

by megavon

7/21/2026 at 7:20:27 PM

"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.”

+

https://poolside.ai/assets/laguna/laguna-m1-xs2-technical-re...

by eisokant

7/21/2026 at 8:00:13 PM

Almost like a built-in heavyweight harness.

by Lwerewolf

7/21/2026 at 8:01:00 PM

"It went from the start of training to launch in under nine weeks..."

This is pretty impressive.

by kamranjon

7/21/2026 at 9:09:54 PM

Any estimates of the performance (prompt processing and decoding tokens/s) on consumer hardware like Macbook Pro M-series?

by docheinestages

7/21/2026 at 9:37:19 PM

happy the US has some counterweights to the Chinese labs, just need about half a dozen more.

by loolhahalmao

7/21/2026 at 6:42:02 PM

open weights and

similar performance to deepseek v4, inkling at size of nemotron 3 super (!)

by tosh

7/21/2026 at 6:46:33 PM

Amazing model at this size if true, that's quite crazy!

by iraldir

7/21/2026 at 8:58:03 PM

Congrats Poolside team!!

by carimura

7/21/2026 at 9:24:08 PM

holy shit its accelerating fast

by danr4