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table bench https://huggingface.co/bartowski/endless-frontier_BigBang-v1-GGUF I'm downloading this model only because Bartowski converted it to .gguf, so it might be interesting. Doubts : The headline number is basically meaningless. "Performance between DeepSeek Flash (old one) and Pro" okay, on what? Did they average the benchmarks? Weight them? Pick and choose? Because if you actually look at the per-benchmark scores, this thing ranges from decent (50 on HLE) to straight ...

Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost constrained production environments. Quantization-aware distillation (QAD) helps recover accuracy lost under low bit quantization by training a quantized student to match the output distribution of a frozen higher precision teacher via a KL-divergence loss. In this work, we first provide a representation level diagnosis of QAD: output matching alo...

I have a classic test for local LLM's. I asked for 8 ball pool game with only one HTML file and Muse Glimmer spend 21k Token(I m using full context so 128k) and only created a 220 lines of HTML and said its done. With my experience its not even close to Qwen 3.6 27B and we are waiting for Qwen 3.8 27B already. What is your toughts about this model. I was so hopeful until this test.

Hey LocalLlaMa, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5,...

There's an interactive chart and some extra data in the blog post if you're interested. There are plenty of KL-divergence benchmarks for GGUF models, but most of them compare one GGUF quant against another. I wanted to know how those quants stack up against other commonly used formats (especially NVFP4). I tested 16 quantizations of Qwen3.6 27B: GGUF models in llama.cpp and the others in vLLM. At each token in the test set, I compared the quantized model’s next-token probability distribu...

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

Static sites are having a moment, and it makes sense. A folder of HTML, CSS, and JavaScript files is fast to load, cheap to host, and very hard to break. Tools like Astro, Eleventy, and Hugo build tha

Systems design has always been treated as a backend problem. Ask a group of Flutter engineers what systems design means, and most will describe server architecture: load balancers, databases, and micr

Related: Which programming languages are most token-efficient? - https://news.ycombinator.com/item?id=46582728 - Jan 2026 (91 comments) Comments URL: https://news.ycombinator.com/item?id=49245936 Points: 23 # Comments: 14