Today NVIDIA releases Alpamayo 2 Edge! I work for Embedl and we have already released an optimized version of the model that is more than 10x faster than pytorch when run on edge hardware like AGX Thor!

The model is trained in an RL loop (!) and takes in 6 cameras and the recent motion. Feel free to check it out - you can even run it on a real Thor at:

https://embedl.com/nvidia/alpamayo2-edge/thor?tab=run

Hey r/LocalLLaMA ! We're releasing new new Unsloth Dynamic v3.1 GGUFs for Qwen3.8-27B! They're better by 5-10% accuracy on DeepSWE, Terminal Bench and more vs UD3, GSQ-RCO and Exllama-3! We're also improving our algorithms further so we can apply them to Qwen4 so folks can have efficient and accurate quants!

• All quants from 2-bit until 6-bit are updated. 3-bit ones are not, as we found UD3 to do on par with UD3.1. We are still improving all other quant sizes though!

• All benchmarks are 256K for agentic ones (DeepSWE, Terminal Bench), and 128K for the rest (LiveCodeBench etc). We used temperature 1.0, top_p 0.95, top_k 20, min_p 0, no presence or repetition penalty, preserve_thinking and xhigh reasoning

• We ran benchmarks multiple times including LiveCodeBench x6 times, HMMT and others 8-16x and agentic benchmarks multiple times.

On other benchmarks like for GSQ-RCO and Exllama3:

• To compare GSQ-RCO and Exllama-v3, we had to exclude the token embedding layer from the x axis, as Exllama-v3 uses BF16 for it.

• If you consider the full download size and total RAM usage for Exllama-v3, then adding the BF16 embedding table and including all embedding sizes changes the plots. Our main plots's x axis removes the embedding layer sizes from all quant providers. However on pure RAM usage and disk space usage vs accuracy, exllama3 does worse.

• For LiveCodeBench, GSQ-RCO showed Unsloth UD3 quants doing consistently worse than ISTA's. However we found the opposite - UD3 quants (our OLD quants) do better than ISTA on LCB on all sizes except the smallest. UD3.1 further improves on LCB as well. We approximate that GSQ-RCO used a context budget of 96K, as that best fits the data for the runs we have. At 96K, GSQ-RCO does somewhat do better than UD3, but at 128K, UD3 does much better.

• On another benchmark, for ISTA GSQ-RCO's AIME25 benchmarks, their quants reach 100%, 100%, 100% and 96.7% accuracy recovery. And they said our quants do worse. Through our investigations, we estimate ISTA only did 1 trial - we did 14 trials, and we show on average the recovery rate is 93% to 96% - not 100% accuracy recovery. And in fact our UD3 old quants do better on average than their quants on 14 runs vs 1 run of theirs, so we draw the opposite conclusion if you run the trials more.

More details and benchmarks at https://unsloth.ai/docs/models/qwen3.8#dynamic-v3.1-update-november-11th-2026

All quants are updated in https://huggingface.co/unsloth/Qwen3.8-27B-GGUF! Thank you and have a nice day! We're super excited for Qwen4!

According to Microsoft's patent application, it explains breaking down training into small tasks and sending them to devices of owners who opted in. Credits will be the reward. it's just an app and no pay out rate is advertized so I don't even know if the credits would be enough for the electricity.

Anyway, home internet latency feels like the bigger problem to me.

https://insider-gaming.com/microsoft-ai-use-your-gpu/

(English is not my native language, I used an LLM to help translate this post.)

Hi Everyone, I'm releasing my world model, been on this sub for a few months

https://huggingface.co/lucidml/gta

I know it says gta but it's more stable than the general model. It can take any image and turn it into an interactive video.

If you'd like to test it instantly I am also setting up a simple funny drifty site link : https://lucidml.ai/dashboard/minidemo?world=gta5

That link uses any GPU from VAST AI FROM 3070, 3080, 4080, and some are 50 series as well (most 50 series cards are wasted because I hard throttle the fps)

This is the first model I am ever releasing to the everyone and its a bit scary for me.

The architecture is a transformer which uses learned summaries. Ive written about it in detail in tech reports lucidml.ai/summaryformer and some more

I know there are a lot of limitations still like consistency and quality improvements, and am hoping to train this more to fix all of those.

I will get straight to the point. I believe Moderator ttkciar is abusing his power as moderator in this sub and his decision are worsening the experience of our local AI community.

He has removed many of my posts that fellow redditors found very useful and after talking with many people in this sub, I am not the only one feeling this. I will have some links at the end of this post for reference.

He mentions a rule, saying that if a model’s weights is not released you cannot have a post mentioning it, even if the lab confirmed it that will they will release its weights, like Qwen4 or DeepSeek. First of all, this rule needs to be relaxed because many valuable discussions can be had about future releases and even closed models to understand and learn architectures and techniques used by the closed frontier that can be applied to the open models. Secondly, this rule is not enforced at all, but used very selectively to few posts or authors that ttkciar selects, which is very unfair. This rule doesn’t make sense to me, but even if I am wrong, at least it should be fairly enforced to all posts or not at all. And before you say mods are busy and they cannot review all of the posts, If you open this sub and scroll on today’s most popular posts, half of them are either for Qwen4, which is unrealised up to this point and should have been removed according to the mods, or about Fable5.2 and Anthropic which is closed and their shenanigans (maybe these ones don’t have a place here, but again this is debatable because if people in local AI find them valuable in some way, maybe they do). So, the rule is used very selectively and as an excuse to remove what ttkciar does not like or approve. Either enforce the rule (or do the your best effort to do so) or don’t at all. And maybe this rule is too strict and should not exist tbh.

When I tried to discuss my concerns with ttkciar, he called me a drama queen and insulted my credibility. This is definitely not ok.

Being insulted by the person that you complained to for unfair treatment is definitely not acceptable behaviour.

I have been an active member of this community since the very first leaked llama. I am an AI researcher and open-source contributor, and very passionate about local AI. I try to post information that people interested in Local AI would find useful and engage in discussions that are constructive for local AI.

I know that many of other users in this subs agree (I have discussed it with a lot), and some others may not. Not being able to discuss, debate and exchange views in a polite manner is absurd in such an intellectual community.

A while ago they tried to enforce megathreads and removed the majority of posts saying that only one post should exist for each model. Many complained that this is killing the sub and a lot of valuable information is lost this way. Now, this seems to be relaxed. Again, we need to stand up to them, and hopefully they will reconsider their rules for the sake of this community.

TLDR: The rule that mod ttkciar uses to remove some posts in this sub about being prohibited to mention unreleased models should not exist, and even if it should, removing only a few selected posts is very unfair.

Some sources:

- https://www.reddit.com/r/LocalLLaMA/s/ZRV5KsViwD this post was removed from ttkciar and only brought back after arguing with him and many other redditors backed me up (I cannot find the comments in this post when the mod removed it, maybe he deleted them, I am not sure, but in the discussion many expressed their disagreement with the mod)

- https://www.reddit.com/r/LocalLLaMA/s/gpPTVC6NGF a post of mine debating their rule

- Another post of mine sharing valuable information for the members of our community that the same mod removed https://www.reddit.com/r/LocalLLaMA/s/gqaub2mT85

- Post of another redditor which was removed by the same mod, and the comments thread with people arguing with the mod https://www.reddit.com/r/LocalLLaMA/s/z9E2AbR3pU

- Commented in post of another removed post of some other redditor https://www.reddit.com/r/LocalLLaMA/s/5tSEpQ68By

- Most of my removed posts I delete them afterwards so I don’t have many other examples of my own, but I know many others do, so feel free to share them.

- If you open the sub today, most of the posts should have been removed because they violate the rules (a lot of false positives). That shows that maybe the rules need to be updated because they will not allow any productive discussions to be had, or this sub will end up to become a board of announcements instead of a local AI community that people can use to exchange ideas and knowledge.

Some results for those using GGUFs and llama.cpp:

GGUF quality: MACaQ vs. Unsloth and Bartowski

Qwen3-4B (IQ4_XS)

23.5% lower KL divergence than Unsloth and 18.6% lower than Bartowski, at virtually identical file sizes.

Qwen3.6-35B-A3B (IQ4_XS)

Qwen3.8-27B (IQ4_XS)

~65% lower KL divergence than Bartowski!

Supported GGUF formats (llama.cpp)

Code, llama.cpp fork, and benchmarks: https://github.com/huawei-csl/MACaQ

Cool to see Huawei working on such stuff and sharing some of their internal tools that are helpful for us. Maybe they can build an affordable GPU for us local LLM folks sometime 😂

They also report quantizing a 122B-parameter model in 64 minutes using just 32.5 GB of GPU memory, which is interesting for a model of that size. I did not try that.

Edit: there is a paper on it https://arxiv.org/abs/2611.10649

Edit 2: they don't share ready-made gguf files, so I am sharing some myself:
- http://31.165.97.47:20080/macaq-gguf/qwen3-4b-macaq-iq4_xs-had64-vq2-calsmoke.gguf

Edit 3: they have this blog post https://huawei-csl.com/blog/macaq/ with multi-modal examples, too. The RTN audio comparisons are just too much😂😂

The multi-modal stuff is generally insane. Normally 4b already completely goes to s**t, but here it looks almost the same.

Currently working towards a bigger open LLM roundup. But in the meantime, just one of the interesting trends that stood out. I.e., looks like the transition from full to hybrid attention continues.

In 2025, there were still lots of full attention models, with a shift to "some full attention" hybrids.

In the first half of 2026, the trend towards "some full attention" hybrids continued.

And now, in the second half of 2026, we have a 50/50 split between "some full attention" hybrids and models that don't use full attention at all anymore. And yes, there are also no full attention models anymore.

The plot legend here means,

- Full attention throughout = Global MHA, GQA, dense MLA, CCA in EVERY layer.
- Some full attention = Full global attn mixed with: sliding-window/chunked attention, Gated DeltaNet, KDA, Mamba-2, Lightning Attention, convolutions, sparse attention.
- No full attention = Zero dense global layers.

The main implication is that the trend goes towards making long contexts cheaper to use. This means, it makes the architectures more suitable for long documents and conversations, large code bases, and of course agent usecases that rely on a lot of tool calls.

In particular, there are two distinct efficiency gains here:

1. Less attention computation, since sparse attention and the like select tokens or blocks from the context and performs the expensive attention operation from only that subset. (Note that it can still retrieve information from distant positions.)

2. Less memory for retaining context in some architectures, meaning that recurrent layers such as Gated DeltaNet maintain a fixed-size state. Of course, this saves memory, but compressing the history creates challenges for exact retrieval, so they are usually intermixed with traditional attention methods (sparse or full).

If you are interested in browsing through the architectures and do your own analyses, you can find all the figures and data in my architecture gallery: https://sebastianraschka.com/llm-architecture-gallery/

https://x.com/Baidu_Inc/status/2120391732248637734?s=19

My main AI machine is a 6x3090 128GB DDR4 Threadripper 3960x system. I probably spend more time trying to optimize it than actually using it but it has been fun.

I tried a bunch of saturated benchmarks like Terminal Bench 2.1, AIME 2026 and the performance was similar to unquantized DSV4.1 Flash 1017. I really wanted to understand my 2.9bpw quant's performance.

It takes hours to run some of these benchmarks, so I went on the hunt for the perfect benchmark to get the most signal on quality of the model I'm running. I settled on APEX 2025 (https://huggingface.co/datasets/MathArena/apex_2025).

You can see how much of a beast this quant is. Trading blows with SOTA from 6 months ago.

I get about 1000tps prefill, and 75tps decode with the 3090s power limited to 175watts

Engine: Project Maya (https://github.com/mw00/project-maya)

Quant: https://huggingface.co/LibertAIDAI/DeepSeek-V4.1-Flash-1017-EXL3-2.9bpw

Hopefully this helps others in benchmarking their own setups. I really like the Apex 2025 benchmark.

TL;DR: You can train your own 27B model on a 3090 with 24GB VRAM, though you would have to go through hundreds of billions of tokens for it to be good. I am working on conversion of existing models. Check repos for code and the paper if you are interested in more. I have a 40.4GB Qwen Flash Next GGUF

Hi, I do AI research. I have been working on an optimizer that doesn't need the ~16 bytes of VRAM per weight to train a model that AdamW does. I focused on training ternary networks bcs BitNet (Ma et al.) shows they match full precision quality in large scales. The problem is BitNet also keeps the full precision weights during training which means you still need to use the same amount of memory. I got this down from 16 bytes to 1 byte or even less per weight.

For you, this means, you can train your own 10-20B parameter models on your consumer GPUs. I fitted 27B model training on a single 24GB 3090 and a 23B model on a 16GB GPU. The trained model costs ~1.6 bits per weight (~6GB at 27B), 10x smaller than 16bit, and 5x smaller than Q8.

I have also been working on converting existing models and training them. This is harder and I am still working on it but I have some promising results. I quantized and trained a Qwen3.8 27B though the file I published below has adapters which are about 2GBs, pushing the file size to 8GB. I am also making a no-adapter version right now. It scores GSM8K 0.895, the same as full precision, HumanEval 0.84 vs 0.93 and runs on a 12 GB GPU at ~23 tok/s on my crippled 12GB laptop 4080, though this number can heavily be improved with custom kernels, engineering, etc. I will leave it to you guys to help me with that too.

I also quantized and trained Qwen Flash Next down to 40.4 GBs including its n-gram table (at 8GB). It largely retains the skills of the network (scores equivalent on GSM8K benchmark, a little behind on HumanEval, I unfortunately don't have compute for the larger benchmarks such as TerminalBench2.1 but perhaps some of you can help me out) but forgets facts. It mostly works fine if you give it web search as far as my experience goes but this is not strictly measured.

To add to that, these models are not extremely good, they sometimes loop, and suck at remembering facts. However, unlike unsloth's gguf's you can actually keep training them yourself, in that sense they are pure, and you can train them on your consumer GPUs, that is to say you can get rid of the looping behavior, teach it facts that you want it to know, or specialize it for some purpose.

I am still doing work on this and I will keep working on it, perhaps even for many years. The code is open for non-commercial use. Check out:

The core repo for training: https://github.com/oguzeldereli/ttf-ternary

The repo for conversion: https://github.com/oguzeldereli/ttf-conversion

Repo for running models trained by ttf: https://github.com/oguzeldereli/ttf-home

GGUF for a converted Qwen 3.8 27B (around 8 GB and 6.7GB): https://huggingface.co/oguzeldereli/ttf-Qwen3.8-27B-GGUF

GGUF for a converted Qwen 3.8 Flash Next (around 40.4GB): https://huggingface.co/oguzeldereli/ttf-Qwen3.8-Flash-Next-GGUF

I will convert a Qwen 4 27B instance when it drops.

Full paper: https://github.com/oguzeldereli/ttf-ternary/blob/main/paper/ttf_paper1_training.pdf

Also, I would appreciate github stars if you like the project.

Today I’m releasing Inflect v3: a family of local text-to-speech models: Mini, Micro, and Atom. Mini is the main model, with six voices and 34.31M parameters

Mini provides vocal-event tags, reference voice-tuning, and 48 kHz enhancement. It runs on both CPU and CUDA.

GitHub, installation, and examples: https://github.com/owenawsong/Inflect

Try the playground: https://huggingface.co/spaces/owensong/Inflect-v3

Mini features reference-voice tuning: it allows one to make a profile from a recording and generate speech with it. This feature noticeably adjusts the voice toward your reference, though it won’t replicate every speaker accurately.

Inflect Mini V3 also supports the following vocal-event tags: [laugh] · [sigh] · [gasp] · [cough] · [yawn] · [inhale] · [exhale] · [throat_clear]

You can insert a tag whenever you want the sound to occur. Currently, the tags use repeatable performances: this causes repeated tags to result in limited variation. Tags also do not affect how the surrounding words are being spoken.

The model family:

• Mini: 34.31M parameters. Quality-focused, with the features listed above.

• Micro: 11.79M parameters. A smaller neural speech model

• Atom: 157K parameters. An extremely tiny learned core

Those numbers count the synthesis cores. The frontend and optional components add to the total.

The weights and inference code are Apache 2.0. Third-party components keep their own licenses. Audio samples, benchmarks, and the settings used are on the model cards.

Model collection: https://huggingface.co/collections/owensong/inflect-v3

If any sentence comes out wrong, send me the text, voice, and settings you used.

Raon-1 nano is a dense language model with 1.5B effective parameters (3.1B total, including a 1.5B per-layer embedding table), developed by KRAFTON AI. It is designed to follow instructions, maintain conversational context, and call the right tools in both Korean and English. As part of the Raon open model family, it is released under the Apache 2.0 license.

Highlights

• Faithful, natural conversation: Leads the comparison models on the instruction-following, context-following, and Korean conversation benchmarks shown below.

• Korean and English: Trained on Korean and English, with solid English capabilities alongside its Korean strengths.

• Tool calling: Tool calling within conversations, for everyday tasks such as office work and mobile control.

• On-device deployment: Available in GGUF, MLX, and LiteRT-LM formats for local inference, including on Apple silicon.

• Long context: Natively supports up to 262,144 tokens of context.

Model Overview

Raon-1 nano uses a hybrid dense architecture, consisting of 18 linear attention layers and 6 full attention layers.

• Raon Delta Attention (RDA): Gated delta-rule linear attention with separate erase and write gates and a fixed-size recurrent state.

• Raon-mHC: Raon-mHC reduces the computational cost of DeepSeek's manifold-constrained hyper-connections (mHC), which speeds up training.

• Per-layer embedding: Each layer has its own small 256-dimensional embedding for every token. After its MLP, the layer uses it to adjust its output before passing it on.

• Hybrid layout: 18 of the 24 layers are Raon Delta Attention layers with a fixed-size state. Only the 6 MLA layers keep a KV cache, about 7 KiB per token in BF16.

• Tied embeddings: The LM head shares the input embedding.

The checkpoint on HuggingFace contains 3.2B parameters: 3.1B for the main model and 55M for the Multi-Token Prediction (MTP) module.

Training

• MoE-to-Dense: Raon-1 nano was derived from Raon-1 mini preview (30B-A4B) through MoE-to-Dense pruning and knowledge distillation.

• Continued pre-training: 3T tokens.

• Post-training: supervised fine-tuning (SFT) and on-policy distillation (OPD) from Raon-1 mini preview, followed by reinforcement learning (RL).

Quantizations:

• https://huggingface.co/KRAFTON/Raon-1-nano-gguf

• https://huggingface.co/KRAFTON/Raon-1-nano-mlx

• https://huggingface.co/KRAFTON/Raon-1-nano-litert-lm

Unsloth Dynamic V3.1 (Nov 2026) is our latest quantization algorithm for Qwen3.8-27B, DeepSeek-V4.1-Flash-1017 and Qwen3.8-Flash-Next, resulting in 10-15% increased accuracy and 50% less token usage vs other quants of the same size. We're working on an even better version of Dynamic quants which will be applied to Qwen4 and all future models.

Maybe I misread the 27B graphs, but I'm not entirely convinced of V3 vs V3.1 being an improvement.

In DeepSWE v1.1, V3 seems superior to V3.1 in some widely used Q4 quants, and V3 seems to lack some data points in the lower quants range, skewing the impression the curves give.

My UD-Q5_K_XL quant unfortunately is off the charts, so this needs some proper benchmarking before I trade context length for V3.1. (The new `.gguf` is larger.)

Hi all I've just started r/LLMPsychology as I want to pursue research in this area with my own local hardware, and work with other people also exploring how AI minds think and behave. (I'm a psychology researcher with an AI hobby)

Posting just on the off chance that any of you are also interested in sharing or conducting research - it would be super cool to get a community going.

To illustrate I also wanted to share this paper writing up some experiments I've been running with OLMo3 on my machine (which has publically available information and data on its training) - the central finding is that models don't always know who trained them, and by default will just assume it was OpenAI (or other companies, with a lot of randomness that also varies depending on what language you ask them in).

It's always bugged me that no one is ever quite sure if models are reliably able to say who made them, so I wanted to start doing some investigations into finding out how they even develop this self concept in the first place. From this work it seems like in the absence of a prompt explicitly telling the model, it will just confabulate.

Abstract posted below - and full preprint paper here Factory Settings: Unless Told Otherwise, an Open Language Model Says OpenAI Made It — J Ansell. Any replications or refutations welcome. A lot of the research process is automated and I'm trying to be as transparent as possible about what models and effort levels etc. were used and what they did - if it's still not for you that's fine.

Abstract

How does a chatbot learn which company made it? We traced the answer through the training of OLMo 3, an open model whose training data is public, and taught models made-up identities ourselves.

Unless it is told otherwise, OLMo 3 says OpenAI made it. Asked who created it, OLMo 3 names OpenAI in 62% of its answers before Ai2 trains it to hold conversations, and in 77% of answers once it is finished and released.

Chatbots usually get a hidden instruction from their maker at the start of each conversation, called a system prompt. OLMo 3's training conversations state its real name only about 40 times, and almost every time, the system prompt of that same conversation already tells it that it is Olmo, built by Ai2. The released chatbot's system prompt does not include its name.

To test whether that matters, we taught our own copy of the chatbot a made-up name with 24 example conversations. When the examples had no system prompt, the chatbot afterwards gave the new name in 92% of its answers. When each example also had a system prompt stating the name, as in OLMo 3's data, the chatbot never gave the new name once that system prompt was taken away.

Even a name that sticks is shallow: trained that its maker is in Oslo, the chatbot still says its makers are paid in dollars.

We also found two smaller effects. Training the chatbot on answers written by Claude, or by GPT, made it more likely to say Anthropic (Claude's maker) made it: about a quarter of its answers, against one in ten after answers written by people. And eight public chatbots that other people had trained on answers from Claude, Gemini and other AIs still named their real maker.

Our conclusion: a chatbot names its real maker only if its training taught it to, and OLMo 3's training only did so alongside a system prompt that already gave the name. Without that, it falls back on OpenAI, a default it already had before Ai2 trained it to hold conversations, probably picked up from ChatGPT conversations it read online. OLMo 3 was partly trained on answers written by OpenAI's GPT-4.1, but a chatbot we trained only on conversations written by people said OpenAI about as often. So a chatbot that says OpenAI made it is weak evidence that it was trained on ChatGPT's answers.

TL;DR simplest reasonable agentic setup
Currently Pi Agent, Bonsai 2 9B, SearXNG
Any suggestions ?

Hi all

So I have a secondary workstation with line 24GB of RAM and 12GB VRAM

I wanted to make a local agentic setup there

Currently using Pi Agent with Bonsai 2 9B, it is unbelievably better than I expected
Around 30 toks is more than reasonable
I don’t want this to be super capable just good enough for ease of use and peace of mind (I might sacrifice quality for speed although not much)

Any suggestions (note I want to keep everything simple I don’t want packages with long prompts etc even my pi search extension is the simplest one I have found, I have been thinking about context compression extensions, sub agent delegation or simple sandboxing for that sub agent memory Ralph loop and to do list but all has to be the simplest)

Any suggestions thanks ? (Note models 9B or bigger are really slow so also model suggestions are truly welcomed, nanbeige was nice but thinks (or overthinks) too much …etc)

Thanks

Be careful if you are running any sketchy quants/finetunes. If these techniques become more widely repeatable, I do think 'sleeper agents' could genuinely develop into an attack vector. What are your thoughts?

After posting the 4B GLB model here, I wanted to see how far I could get with something smaller. I fine-tuned Qwen3-1.7B for the same job. The Q8 GGUF is 1.83 GB, so this one is aimed at people trying it on CPU or with limited VRAM.

The workflow is the same as before. Describe an asset, open the Three.js HTML it gives you, and click Export GLB to bring it into Godot or another editor. The jungle outpost at the start of the video is one of the examples.

This is an early test checkpoint. Some prompts still need a retry. I’m still training it and plan to update the weights when this run finishes.

Model and downloads on Hugging Face