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Laguna S 2.1
- iraldir 2mo agoAmazing model at this size if true, that's quite crazy!
- mchusma 2mo agoIncredible. 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.
- platinumrad 2mo agoDoes it refuse to work on "cyber"?
- kouteiheika 2mo agoIt'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 https://github.com/p-e-w/heretic
- tosh 2mo agoopen weights and similar performance to deepseek v4, inkling at size of nemotron 3 super (!)
- megavon 2mo agoThis is INSANE. How did they do this?
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- eisokant 2mo ago"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-report.pdf https://poolside.ai/assets/laguna/laguna-m1-xs2-technical-re...
- Lwerewolf 2mo agoAlmost like a built-in heavyweight harness.
- kamranjon 2mo ago"It went from the start of training to launch in under nine weeks..." This is pretty impressive.
- aitchnyu 2mo agoAre 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.
- Iolaum 2mo agoModel 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!
- verdverm 2mo agoI hope all models going forward come with a dflash drafter so we don't have to train one up separately.
- alfiedotwtf 2mo agoAlso, that all models going forward target the base prosumer class ie today = Strix Halo
- SwellJoe 2mo agoThis 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.
- mandelken 2mo agoOn Strix Halo, have a look at the work antirez is doing with dwarfstar (antirez/ds4) for deepseek v4 flash at reasonable speeds and quality.
- SwellJoe 2mo agoI've tried it, but, it's not reasonable speed, at all. It's 9-13 tokens per second, which is not usable interactively and not worth using for long-running API stuff when DeepSeek V4 Pro is so cheap via their API. Laguna S 2.1 runs at 15-28 tokens per second, depending on context and...something about how long it's been running, which is very comfortable for chatting, but still not usable for interactive agentic coding. Their `pool` agent just times out when I try to use it with the Strix Halo-hosted instance of the model.
- river_otter 2mo agoI 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?
- megavon 2mo agoHaving played with it for like 3 hours now....I'm probably moving from CC to this
- river_otter 2mo agoI 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
- kamranjon 2mo agoWhat quant are you using?
- river_otter 2mo agoK4 Q_M https://github.com/njbrake/laguna-otari-bridge https://github.com/njbrake/laguna-otari-bridge
- megavon 2mo agoYes 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.
- fingerprinter 2mo agoOne hour in, no more Codex for me. This thing rips.
- kamranjon 2mo agoWhoa 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.
- svclaws 2mo agoIf the numbers are legitimate then our prayers have been heard
- Lwerewolf 2mo agoTesting 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.
- ilc 2mo agoWhat harness/quant did you use for testing?
- Lwerewolf 2mo agonvfp4 mlx, literally barebones pi. edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.
- sosodev 2mo agoWhat 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.
- Lwerewolf 2mo agoThis: https://github.com/Blaizzy/mlx-lm/tree/pc/add-lg 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 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.
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- river_otter 2mo agoHey, 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 https://github.com/mozilla-ai/otari/pull/348
- mft_ 2mo agoLooks 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 https://huggingface.co/vcruz305/Laguna-S-2.1-GGUF
- verdverm 2mo agoThe 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 https://github.com/vllm-project/llm-compressor my setup to help you on your way: https://github.com/verdverm/quantr https://github.com/verdverm/quantr Though it seems these will not be needed as Poolside has published quants & dflash with their models.
- alfiedotwtf 2mo agoNice! Do you know of any tools that do this for tensorrt models?
- verdverm 2mo agoSorry, I don't, still newer to the quantizing side of things since many are already produced by others.
- yogeshp 2mo agoThey 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 https://huggingface.co/poolside/Laguna-XS-2.1-GGUF/tree/main
- mft_ 2mo agoThanks 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.
- carimura 2mo agoCongrats Poolside team!!
- docheinestages 2mo agoAny estimates of the performance (prompt processing and decoding tokens/s) on consumer hardware like Macbook Pro M-series?
- benjiro29 2mo ago!! 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_s_21_released_cheaper_than_deepseek_v4/oyy69u8/ https://www.reddit.com/r/LocalLLaMA/comments/1v2pg99/laguna_...
- voxgen 2mo agoEven the official provider on OpenRouter seems to have this issue. Hope it's an easy fix for them.
- nshotton 2mo agoThanks for posting this, it made a huge difference tweaking the recipe.
- d2p 2mo agoLooks like the default chat template was updated on HF to enable this by default shortly after you posted this :-)
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- danr4 2mo agoholy shit its accelerating fast
- loolhahalmao 2mo agohappy the US has some counterweights to the Chinese labs, just need about half a dozen more.
- aubanel 2mo agoReally 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!
- spelk 2mo agoThis 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).
- _mrinalwadhwa_ 2mo agoHas anyone tried it on a mac yet?
- _mrinalwadhwa_ 2mo agoWas 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 http://127.0.0.1:8000, llama.cpp, ctx 64K, 8-bit KV cache mode: max thinking # tokens tok/s dflash 1 600 14.4 11% 2 600 26.1 27% 3 600 17.8 18% 4 600 14.0 16% 5 600 9.3 15% -------------------------------- median 14.4 mean 16.3 min 9.3 max 26.1 tok/s mode: no thinking # tokens tok/s dflash 1 190 10.0 20% 2 109 26.7 65% 3 95 29.6 72% 4 93 32.8 81% 5 382 14.0 30% -------------------------------- median 26.7 mean 22.6 min 10.0 max 32.8 tok/s
- axus 2mo agoYou will not, directly or indirectly: copy, modify, or create derivative works of the Site or any Product;
- Archit3ch 2mo ago[dead]
- verdverm 2mo agoinitial 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
- drob518 2mo agoImmediate 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).
- polski-g 2mo agoThis thing is great, twice as fast as DS4Flash and slightly smarter too. I swapped most of my sub-agents to this model.
- markasoftware 2mo agohave 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!)
- luciana1u 2mo ago[flagged]
- resonious 2mo agoHow much does it cost? I even made an account and I cannot find pricing anywhere...
- asar 2mo agoIn / Out Price $0,10 / $0,20per 1M from openrouter
- alfiedotwtf 2mo agoAnyone know if this is supported in ik_llama.cpp?
- suprjami 2mo agoJust merged into main llama.cpp a few hours ago: https://github.com/ggml-org/llama.cpp/pull/25165 https://github.com/ggml-org/llama.cpp/pull/25165
- throwdbaaway 2mo agoIt works, thanks to https://github.com/ikawrakow/ik_llama.cpp/pull/1911 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.
- alfiedotwtf 2mo agoAwesome, thanks! Edit: it amazes me how fast ik_llama.cpp moves
- reindeer2 2mo agoI'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.
- khurs 2mo agochat as listed on that page: https://chat.poolside.ai https://chat.poolside.ai
- mark_l_watson 2mo agoFor 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.
- samelldev 2mo agoimpressive benchmarks for the active params. i'll load it up and test
- literallyroy 2mo ago[dead]
- kevincox 2mo agoThis seems like a great option for local usage. With 16 GiB of VRAM on the official (https://huggingface.co/poolside/Laguna-S-2.1-GGUF https://huggingface.co/poolside/Laguna-S-2.1-GGUF) Q4_K_M quant (which seems to be recommended) I can get about 50 t/s in and 10 t/s out. Definitely not winning any speed benchmarks but totally usable for background tasks. The intelligent and decision making looks good overall. It seems to roughly compare to unsloth/Qwen3.6-27B-GGUF:UD-Q8_K_XL while being way faster on my hardware. It does often get stuck considering the same decision over and over again but maybe this is how it makes the better decisions. However it does seem to have a bad habit of corrupting things (most often my name). It will rewrite kevincox to kevinc or kevcox or kevancox. Then fail to read directories or rewrite code with the wrong string and mess it up. Then it can't identify it's mistake. Maybe I'll have try try out the Q8 model, but the description seems to push away from this one.
- mvallebr 2mo agoI am SO impressed by this model! It's cheap as deepseek v4 flash, better than minimax m3 I would say, and almost a glm 5.2. 120b params only for such a good result, with 258k context. This is amazing. Yesterday I was making experiments running laguna xs 2.1 in my local machine (a smaller version), with a ryzen 5700x, 64 RAM and a 4060 ti 8gb nvidia It was able to run locally with a context of 128k using llama-cpp and I tried using it for local development in a project that used uv, python, tool calls, file system explorations, websearches - all worked as a charm... in my own hardware I was SO impressed. Then I saw open router is letting people try laguna S 2.1 for free - it's a model with 120b params and 258k context - if you top up at least 10 usd, it gives you 1000 requests per day for free so you can try it out. but the paid version is super cheap as well, specially the cache.
- alfiedotwtf 2mo agoYep! I loved Qwen 3.5 and 3.6, jumped to Minimax 2.5 and 2.7, but Laguna S2.1 feels like I have SOTA at home. We’re going to get to the point where the enormous size of models will not be able to keep being updated with the huge amount of content being generated online, and so I think a strong SOTA-esque thinking model with a good harness, fast web crawler, and large MCP capabilities will be the future tool of choice, rather than larger and larger static models (unless someone creates upgradable but compact embeddings)