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arXiv : https://arxiv.org/abs/2608.00146 Full Paper : https://arxiv.org/pdf/2608.00146 Tweet : https://xcancel.com/googlegemma/status/2086849199052845451#m FYI both (llama.cpp) PRs ( 24423 & 24427 ) went to Draft mode. I'm still waiting for this one as I could get faster t/s on my 8GB VRAM.

First of all, I'm not a lab, this was a solo summer research project that finally culminated into the github repo and the writeup. The repo includes a much deeper dive with methods, findings about quantization and geometry, limitations, and proposed experiments. I'll also mention that this is far from production-grade, it's mostly a theoretical framework with a "fake" quantization demo as it lacks real kernels. The geometry of LLMs embedding spaces is highly uneven with a few feature...

Motif-Technologies is one of the tech company participated South Korea's AI Foundation Model project.(독파모) Upstage(Solar Series), LG AI Research(EXAONE Series), and SKT(A.X Series) are the competitors. Since LG’s EXAONE put up pretty disappointing results, it looks like Upstage, Motif, and SKT will be the ones advancing to the next round this time. If you reverse-calculate the AAII score from the table, it comes out to 47.364, which slightly edges out Qwen 3.7 Max. With Upstage’s Solar P...

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*SupraLabs released a new experimental model!\ * SupraElegans-500K is a ~500,000-parameter causal language model built around a sparse, signed, recurrent neural graph. No Transformer, no attention mechanism, no positional encoding, no KV cache. Context is carried by a persistent per-neuron membrane potential updated token by token. The architecture is loosely inspired by ideas from the C. elegans nervous system: sparse connectivity, distinct neuron populations, excitatory/inhibitory si...

https://preview.redd.it/xxjh11f38jih1.png?width=1852&format=png&auto=webp&s=76850ed51e29a8bc86c2ca718d4320075eed4363 Just wanted to share a user report that I found to be very interesting. Some person with an intriguing name manu69x managed to run 1M context on a single RTX 3090 with a model based on Qwen 3.5 35B A3B, which was taking up some 17 GB of VRAM alone. And "run" here doesn't mean just "server didn't crash", it's that context didn't turn into a me...

Wowee!! Just when you thought it couldn't get better for open weight models, we probably have had our best period yet!?!?! Models that rival the closed frontier, Opus level models on non-insane hardware and more. A massive industry alliance coming out in support of open AI in response to the two closed model giants best lobbying efforts. Is this the best timeline? Someone pinch me! Or just tell us what you're favorite model is now The standard spiel: Share what you are running right n...

VLX-Seek-1.5-10B VLX-Seek-1.5-10B is the open-source 10B model in the VLX-Seek 1.5 family, designed for fine-grained perception and visual grounding in embodied scenarios. It targets practical settings such as drones, robots, robotic dogs, surveillance cameras, inspection systems, and other edge-side visual intelligence applications where a model must identify what is present, localize the right instance, and avoid grounding objects that are absent. Unlike coordinate-generation-based VLMs th...

Having a ‘Killer Application’ that everyone wants to use helps sell hardware, plain and simple. DeepSeek V4 Flash 0731 isn’t an app of course, but I think it’s going to be the major catalyst for getting a lot of people to buy a couple of NVIDIA GB10-based systems because: It is an amazing coding / agentic use model. It fits perfectly on a 2x Spark Cluster It runs Fast AF with the right vLLM recipe. (I’m getting 60 tk/s with this one: https://github.com/tonyd2wild/DeepSeek-v4-Flash-073...

I know Muse Glimmer is pretty new and all, but was wondering if anyone else has run into Glimmer outright refusing to code even small things? I am using Unsloth Q8, dual 3090's, in Kilo Code. I was trying to get it to help me with a bug in my codebase (using pyton stdlib to manipulate a mouse, moving it, clicking, etc.) and it has been giving me different versions of this: I can’t provide code to control your mouse without context. Moving a mouse programmatically can be misused for automation...

Looks like the Ling team open weighted a much smaller version of the Ling-3.0-flash they open weighted a few days ago. It's 8B params with 1.3B active, and seems to fall between the 4B and 8-12B Qwen and Gemma models in terms of performance. Should have a massive tokens/sec on most systems. I quite like tiny MoE's conceptually. Edit: looks like the model card actually reports speeds: With FP8, Ling-3.0-tiny reaches around 100-105 tokens/s on DGX Spark and 86-90 tokens/s on an M4 Pro Mac...

https://www.forbes.com/sites/sandycarter/2026/08/09/kpmg-says-nearly-half-of-executives-pulled-back-ai-agents-over-cost/ Bubble started to burst?

Sub is drowning in slop posts. Shortly after the new rule it was better. But it's gotten unbearable in the past month or so.

I did some testing this morning, and I was surprised to find that Muse Glimmer actually comfortably fits on a single RTX 3090 with full context + DFlash + mmproj at Q4_K_XL, unlike Qwen3.6-27B and Gemma-4-31B. Muse Glimmer supports up to 256k context according to Unsloth. Here is my command: llama-server \ --model Muse-Glimmer-30B-UD-Q4_K_XL.gguf \ --mmproj Muse-Glimmer-30B-mmproj-kquant.gguf \ --spec-draft-model Muse-Glimmer-30B-DFlash-kquant.gguf \ --spec-draft-ngl 999 \ --spec-draft-n-ma...

Guide: https://unsloth.ai/docs/models/muse-glimmer

Tweet by u/hackerllama Could be copium, but I would love to see Gemma 4.1 there with unified audio input for all model sizes perhaps even up to 120B, much improved tool calling (even with the latest template there are still bugs ), higher precision QAT from the start and improved general performance without hurting the things Gemma 4 is good at like creative writing. Gemma 4 is good already but training an upgrade to 4.1 that does all of the above would be huge for the community. They a...

Hi r/LocalLLaMA 👋 Today we’re excited to release Muse Glimmer, a 30B open-weight model built specifically for local agent workflows. We’re releasing the weights to the community under a permissive Apache 2.0 license. A few specs 30B params, dense Multimodal: interleaved text + images via a dedicated perception encoder Trained on 100+ languages Controllable reasoning effort (quality/speed tradeoff) Memory footprint At full precision, 30B needs 55+ GB, which is out of rea...

https://x.com/i/status/2086755195535413696

Hi all. These are my system specs: dual xeon e5 2696 v2 , 160gb DDR3 ram ECC(1600mhz), 3 gpus: 3060 12gb, p100 16gb, 3050 6gb. And a 400gb nvme sdd RAID0, 3000 mb/s. The model is Deepseek-flash-0731 UD_8_X_XL, loseless, 161gb. Now, I'm not too knowledgeable about llama cpp flags, I wish run it without mmap, because its so slow, and I believe it should fit in my system overall. There's also Dspark and MTP which could help with the speee, but do they work with llama? Any recommendations would help...

https://preview.redd.it/h7dwy09z2rhh1.png?width=1240&format=png&auto=webp&s=7285448ac2d9c895c9a11062557044fc97504fa3 I was evaluating which model to test for text summarization and rewriting in a scenario that also requires general world knowledge. Taking into account the available VRAM and my experience over the past few months, I’ve found Minimax 2.7 Q5 to offer the best balance for my needs and the numbers seem to confirm. What is your favorite model? I’m referring to the mod...

nvidias nemotron omni is open weights and it sees, hears and reasons. theres already a 4bit mlx quant on hugging face but only the text backbone loads with standard mlx tooling. the model card says it plainly, the vision and audio towers need a runtime that implements the c-radio vit-h and parakeet conformer forward passes. so i wrote that runtime in pure mlx. vision tower, audio tower, the processor and the token splicing, all ported from nvidias reference implementation. it runs the mlx-comm...

Code and instructions available here: https://github.com/albertoZurini/echo-dot-2-playground Hello there! After a few days of experimenting I was able to get a completely local voice pipeline running on an Amazon Echo Dot 2. The interesting part for this community is that the device can run `llama.cpp` alongside offline speech recognition even with its very limited hardware. The Echo Dot 2 has an ARMv7 processor and 512 MB of RAM. I compiled `llama.cpp` for `armeabi-v7a` and run the model ...

J'ai consacré beaucoup de temps à l'optimisation de DeepSeek-V4-Flash-0731 GGUF sur une seule RTX 3090. Mon exigence absolue pour chaque configuration était la suivante : Le modèle doit rester utilisable avec une fenêtre de contexte de 128 000 jetons. J'ai testé les différentes combinaisons de déchargement GPU, de placement expert du CPU, de quantification du cache KV, de tailles de lots, de mappage mémoire et de répartition de la mémoire CPU/GPU. Les paramètres ci-dessous ont permis d...

I love to see these impressive models coming out that compete with the giants from companies like Z.ai, Moonshot, Alibaba, etc. A win for the open source/weight community is always welcome. While I am grateful, I worry we might be seeing the slow death of models smaller than 27B. The ones released paling in comparison to Qwen 3.5 4B/9B and Gemma 4 12B. Especially for agentic coding and agentic assistance tasks. Is this because we’ve really hit the limit of what we can accomplish with models in...

Hey peeps. I know you're tired of low quants giving hard to believe numbers. I'm quite skeptical too and from what I tried I'm often left with the impression that the claims fall short. So this model popped up on Twitter for me. Tried it and was lowkey surprised it held its own. I ran some benchmarks with the help of antigravity to at least try to verify it myself. Here is what I got: Axis / Metric Escha (W2 ROCmFPX) APEX (Q5 Balanced) Key Finding / Winner VRAM Memory Alloc...

TL;DR: On a Qwen3.6-35B-A3B Q6 setup sized for 64K context on a 24GB RTX 3090, spilling eight MoE expert layers to CPU freed enough VRAM to increase -b from 512 to 1024 and -ub from 128 to 512. Prompt processing improved by 2.36×, while generation speed remained unchanged within measurement noise. Benchmark Auto-fit baseline Tuned Result PP4K 564.5 tok/s 1330.0 tok/s 2.36× TG4K 97.4 tok/s 97.7 tok/s Within noise TG32K 81.6 tok/s 84.0 tok/...