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.
> edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.
Been playing around for a few hours with the poolside/Laguna-S-2.1-NVFP4 + poolside/Laguna-S-2.1-DFlash-NVFP4 + vLLM, been seeing the same behaviour. Usually new model releases are plagued with issues at release though, best to wait 1-2 weeks then retry, or better yet, investigate yourself :) Personally I haven't found any obvious issues.
Update: Seems quite literally they have bugs on the hardware I'm trying to run this with:
From Poolside CEO Eiso Kant on Twitter:
> Learning we have some bugs on the RTX6000. We’re on it. Team has worked non stop last days and it’s getting late for a lot of the inference folks, so might be until tomorrow till we have a solution. - https://x.com/eisokant/status/2079693050796785720
Update2: I'm now running poolside/Laguna-S-2.1-NVFP4 with vLLM 0.23.1rc1.dev1378+gd6dbdb9b0 (FlashInfer 0.6.14) and seeing slightly better results in regards to the looping. I can't see any specific changes that would affect this though, strangely enough.
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.
Replying to myself, seems this PR was merged into main and it the model does work with a Vulkan backend on my Framework desktop, I’m getting about 220 tok/s prompt processing and 21 tok/s output on the 4-bit quant. This is really a sweet spot imo on this machine between maximizing ram use and still having decent speed due to the expert size. This looks really promising.
It’s the Strix halo (AMD) with 128 GB shared memory. The 4bit quant is ~75GB.
Unfortunately I don’t know about the best way of running on an Nvidia gpu, you could try llama.cpp and offloading as many layers as possible into the gpu and using RAM for the rest, not sure if that would slow it down too much though.
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.
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.
For me, poolside.ai “came out of nowhere” a week or so ago when I discovered their local coding harness ‘pool’ and their smaller 33G MOE model that runs fast and is effective on my old 32G mac mini. Really good work!!
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.
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!
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!
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.
Immediate reaction is that it seems to be a bit behind Meta Muse Spark 1.1 performance at approximately the Deepseek v4 Flash price point. That's quite good given Muse Spark benchmarks a lot better than Deepseek v4 Flash (assuming benchmarks mean anything, which they don't).
I've been following work on the second-order effects that ripple through the system for a while. This is the first treatment I've seen that the framing reveals an assumption that isn't explicitly defended.
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).
"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.”
It's an open-weight model so it literally doesn't matter whether it refuses by default or not, because it's pretty trivial to uncensor[1] any open-weight model and make it not refuse.
have only tested a few prompts, but it failed my favorite non-coding question that dsv4 flash aces. Benchmarks look excellent though (don't they always!)
initial impressions, great model for coding, probably swapping it out for qwen 27b for a while to long-term test, more sycophantic than any I've run locally myself
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.
What harness/quant did you use for testing?
nvfp4 mlx, literally barebones pi.
edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.
> edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.
Been playing around for a few hours with the poolside/Laguna-S-2.1-NVFP4 + poolside/Laguna-S-2.1-DFlash-NVFP4 + vLLM, been seeing the same behaviour. Usually new model releases are plagued with issues at release though, best to wait 1-2 weeks then retry, or better yet, investigate yourself :) Personally I haven't found any obvious issues.
Update: Seems quite literally they have bugs on the hardware I'm trying to run this with:
From Poolside CEO Eiso Kant on Twitter:
> Learning we have some bugs on the RTX6000. We’re on it. Team has worked non stop last days and it’s getting late for a lot of the inference folks, so might be until tomorrow till we have a solution. - https://x.com/eisokant/status/2079693050796785720
Update2: I'm now running poolside/Laguna-S-2.1-NVFP4 with vLLM 0.23.1rc1.dev1378+gd6dbdb9b0 (FlashInfer 0.6.14) and seeing slightly better results in regards to the looping. I can't see any specific changes that would affect this though, strangely enough.
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.
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.
Hey, this model is not a joke! Exciting, we already got a usable PR of work out of it.
https://github.com/mozilla-ai/otari/pull/348
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.
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
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
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.
> if someone would kindly quantise this down for the 64GB paupers
The Q4_K_M is 75GB. I'm exactly at 64GB, and I wouldn't quantize it further. Instead, do partial weight residency and stream the rest from SSD.
unsloth is always here to save the day https://huggingface.co/unsloth/Laguna-S-2.1-GGUF
Seems it’s not fully supported in mainline llama.cpp yet https://github.com/ggml-org/llama.cpp/pull/25165
In the huggingface link they mention building for CPU and for CUDA, does anyone know if that means it wouldn’t be possible to build targeting Vulkan?
Replying to myself, seems this PR was merged into main and it the model does work with a Vulkan backend on my Framework desktop, I’m getting about 220 tok/s prompt processing and 21 tok/s output on the 4-bit quant. This is really a sweet spot imo on this machine between maximizing ram use and still having decent speed due to the expert size. This looks really promising.
What's your hardware? I've got 64GB RAM and a 4090 here, wondering if it's worth a play.
It’s the Strix halo (AMD) with 128 GB shared memory. The 4bit quant is ~75GB.
Unfortunately I don’t know about the best way of running on an Nvidia gpu, you could try llama.cpp and offloading as many layers as possible into the gpu and using RAM for the rest, not sure if that would slow it down too much though.
The tool I've been using, llm-compressor, can quant models that do not fit in memory (use the sequential pipeline)
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.
Nice! Do you know of any tools that do this for tensorrt models?
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.
If the numbers are legitimate then our prayers have been heard
impressive benchmarks for the active params. i'll load it up and test
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.
For me, poolside.ai “came out of nowhere” a week or so ago when I discovered their local coding harness ‘pool’ and their smaller 33G MOE model that runs fast and is effective on my old 32G mac mini. Really good work!!
I need to evaluate their large hosted model.
!! 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_...
Looks like the default chat template was updated on HF to enable this by default shortly after you posted this :-)
Even the official provider on OpenRouter seems to have this issue. Hope it's an easy fix for them.
Thanks for posting this, it made a huge difference tweaking the recipe.
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!
chat as listed on that page: https://chat.poolside.ai
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!
I hope all models going forward come with a dflash drafter so we don't have to train one up separately.
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?
Having played with it for like 3 hours now....I'm probably moving from CC to this
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 quant are you using?
K4 Q_M
https://github.com/njbrake/laguna-otari-bridge
One hour in, no more Codex for me. This thing rips.
Immediate reaction is that it seems to be a bit behind Meta Muse Spark 1.1 performance at approximately the Deepseek v4 Flash price point. That's quite good given Muse Spark benchmarks a lot better than Deepseek v4 Flash (assuming benchmarks mean anything, which they don't).
64GB RAM used to mean you could run three VMs and a database. now it means 'maybe the 120B model fits if I close everything else'
I've been following work on the second-order effects that ripple through the system for a while. This is the first treatment I've seen that the framing reveals an assumption that isn't explicitly defended.
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).
This is INSANE. How did they do this?
"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...
Almost like a built-in heavyweight harness.
Are all AI labs in Google's weight class in crawling and ranking the Web's content? I know OpenAI has contractor subject matter experts in all topics.
"It went from the start of training to launch in under nine weeks..."
This is pretty impressive.
happy the US has some counterweights to the Chinese labs, just need about half a dozen more.
Does it refuse to work on "cyber"?
It's an open-weight model so it literally doesn't matter whether it refuses by default or not, because it's pretty trivial to uncensor[1] any open-weight model and make it not refuse.
[1]: https://github.com/p-e-w/heretic
How much does it cost? I even made an account and I cannot find pricing anywhere...
In / Out Price
$0,10 / $0,20per 1M
from openrouter
open weights and
similar performance to deepseek v4, inkling at size of nemotron 3 super (!)
Amazing model at this size if true, that's quite crazy!
Any estimates of the performance (prompt processing and decoding tokens/s) on consumer hardware like Macbook Pro M-series?
You will not, directly or indirectly: copy, modify, or create derivative works of the Site or any Product;
have only tested a few prompts, but it failed my favorite non-coding question that dsv4 flash aces. Benchmarks look excellent though (don't they always!)
This thing is great, twice as fast as DS4Flash and slightly smarter too. I swapped most of my sub-agents to this model.
Anyone know if this is supported in ik_llama.cpp?
It works, thanks to https://github.com/ikawrakow/ik_llama.cpp/pull/1911, which got merged in early June. However, there might still be some issue with the chat template.
Just merged into main llama.cpp a few hours ago:
https://github.com/ggml-org/llama.cpp/pull/25165
Has anyone tried it on a mac yet?
Was able to run it on Apple M3 Max (128 GB)
host: Apple M3 Max, 128 GB model: Laguna-S-2.1, 118B-A8B MoE, Q4_K_M (75 GB), DFlash speculative decoding server: http://127.0.0.1:8000, llama.cpp, ctx 64K, 8-bit KV cache
Congrats Poolside team!!
initial impressions, great model for coding, probably swapping it out for qwen 27b for a while to long-term test, more sycophantic than any I've run locally myself
holy shit its accelerating fast
[dead]