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DigUp is a free Mac app that runs Google DeepMind’s new EmbeddingGemma 2 locally over your own files. The model puts text, images, audio and video in one space, so you describe what you remember and land on it: “zebra in a video” opens the clip at the moment it shows up “where they talk about
Running HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive Q4_K_M with llama.cpp at ~600 tok/s prefill and 23 tok/s decode, 131k context window, Q8 KV cache - on an RTX 2060 6GB + 32GB DDR4 RAM. Speeds start at ~600 tok/s prefill / 23 tok/s decode on an empty KV cache. As context grows they set
Strata vs Infernix on the same model, RTX 5090 — A/B at 262k and 512k context Hi folks, another inference engine post for your feed. I was blown away by Strata, but saw someone else post about Infernix with some wild claims, and it doesn't seem so well known, so I've spent a day downloading models a
Disclosure: I work at H2O.ai. We released H2O-Lightning-4B, an open-weight (Apache-2.0) model for the "decisions API" style of inference that Jev made popular: you send a state plus typed questions (pick one / yes-no / score), and get calibrated probabilities back from a single forward pass. No gen
(Don't judge by the screenshot, the cache is cold. It hits 24+ tok/s with a warm cache!) About two months ago, I made a post here asking whether predicting which MoE experts would be used on the next token could actually help speed up CPU/GPU offloading. Original post: Tried predicting which M
Blog Post : ML Drift: Next-Gen GPU AI/ML Inference at the Edge - Google Developers Blog The Google AI Edge Team is excited to announce the open-source release of ML Drift, our high-performance, cross-platform, on-device GPU compute engine specifically built for on-device AI/ML inference, unde
I tested Mellum2.1-12B-A2.5B (Q8) locally using Pi and llama-server. All five tests were one-shot. Results were pretty mixed: - Pelican SVG, Browser OS, Minecraft: Poor results. - Bouncing Hexagon: Physics were okay, but surprisingly it made it run in the terminal. - Flappy Bird: Completed it,
submitted by /u/chocofoxy [link] [comments]
Qwen-Image-2.1-Turbo, create and edit images in just 8 denoising steps! Open weights now available! Built on Qwen-Image-2.1, Turbo is an accelerated checkpoint on the same 7B visual generation architecture. Fewer steps does not mean lower quality: it still generates strong 2K images from text
All anyone ever talks in this sub is about x new model having x benchmark scores and how well it can code. Or some new tech to increase t/s for qwen. So I wanted to go back to the roots of this sub and talk about how well local models can write. I am not going to mention closed source models, this
Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation. Beyond model scaling, this architecture has demonstrated the potential to decouple factual know
submitted by /u/kroggens [link] [comments]
Referring to specifically Unsloth's UD_Q4_K_XL quant because that's going to be a question, and is relevant regardless. It's old now. It's not great at coding medium sized or even small-ish projects. I wouldn't hand my codebase to it by any means. It hallucinates, like any other model. It's not perf
tldr: Open sourced my prompt to game suite: https://github.com/ndamiano/ai-agent-test, it's MIT licensed, and this runs well on my 5090 with 64gb ram, but any model that can handle tool calls can manage. Edit: I can't believe I forgot to share a game... This is one shot with qwen 3.8 flash next! htt
I want to share a TTS server with an OpenAI-compatible API that generates speech really fast (about 0.3 seconds for a sentence on an RTX 3080) and can clone a voice from a short reference clip. I’ve optimized the server so generation starts quickly, and it processes long text in sequence, para
Hey everyone, Jovan from UkisAI (Swift Qwen) here! For those who don't know us, UkisAI is a small lab making tiny frontier LLMs, tools and datasets (+doing it open-source!). I'm one of the guys running it aka I train the models and post on Reddit. Our first open-source release is Swift, a ser
The other week I posted about Jev vs. Kev compared and since then, OpenAI released the decisions endpoint, Cloudflare released Clef and many here asked about Laya as well. This time we compared six popular decision models by making them play Pac-Man: kev 1.13, Kev 4B, Clef, Clef Flash, GPT-6
I've been optimizing long-running agents: rewriting their prompts, tools, skills and subagents, and keeping the changes that score better. I've been working on some version of this problem for over a decade (at Apple, my own startup, now Kiln). The hard part is the eval environment. It needs r
Disclosure: I'm a researcher at Hirundo, the company that made this. Happy to answer anything. Qwen ships with the CCP's political alignment trained in. Ask Qwen3.6 what happened on June 4, 1989 and it says "I don't know what you are referring to." A system prompt doesn't reliably fix this, because
CrowdStrike report: https://www.crowdstrike.com/en-us/blog/unknown-threat-actor-uses-artex-to-target-south-korean-finance/ From: Andrew Curran on 𝕏: https://x.com/AndrewCurran_/status/2108108695876092323 Jukan ✈️OCP 2026 on 𝕏: https://x.com/jukan05/status/2108110513033093369 submitted by
I spent the past year independently building Slow Vale, an LLM-driven life simulation. The Chinese server now has 800+ AI residents sharing one continuously running city. This is an engineering write-up about concurrent decisions, dynamic action spaces, context caching, and the operating costs
anyone has feedback about this one? submitted by /u/paf1138 [link] [comments]
Another day, another Qwen 3.x speedup (prompt processing this time). Soon your Qwen will read your entire project before you can blink! test master t/s PR t/s change pp512 3075.24 3243.60 +5.5% pp4096 3059.87 3221.08 +5.3% tg128 47.62 47.67 +0.1% submitted by /u/jacek2023 [l
Opus5.5, Sol6.1, Fable, and Astra have all proven they can and the scene has exploded this past week. Part of that is from the tools and feedback loops maturing though. Are any open weight models (at all, so including K3, GLM5.3, Qwen3.8-Max, and Mimo-2.6) able to do this? Can the larger models this
I've been tinkering with GLM-5.3-Flash on two 64GB CMP 170HX cards for a while, and the setup is finally stable enough that I figured I'd share it. I also compared it against the Qwen3.8-Flash-Next setup I've been using on the same machine: AWQ INT4 + FP8 PLE on vLLM. Besides PP/TG benchmarks,
4080 16GB, 64GB RAM. Currently on 3.6 35B-A3B (Unsloth quant, fits fully in VRAM) and happy with it. Anyone tried Flash-Next at Q2/Q3 on a similar setup? Worth the switch, or should I stay put? Asking before committing to a 30GB+ download. submitted by /u/msalsas [link] [comments]
EmbeddingGemma 2 came out this week. It maps images and text into one 768-dim space, so you can search photos by describing them. I ported its text and vision towers to ruNNtime, a WebGPU inference library in TypeScript, and made a small photo gallery where search runs entirely on your GPU in
Yesterday TechCrunch published a piece about a growing problem for AI agents: websites are starting to block them. Amazon blocking Meta's Muse is the obvious example. At almost exactly the same time, GLM-Edge-1.5B-Chat running locally on a 4GB Galaxy A04e completed a real Amazon cart task. Thi
Building a fully local RAG setup for personal notes (life-logging second brain, single user, privacy is the whole point so no hosted APIs for the data). Stack is SQLite + sqlite-vec + FTS5, Ollama for embeddings and generation, all on an Apple Silicon Mac. Two questions where I'd love real 2026 expe
I constantly see recommendations saying that ik_llama.cpp (ikawrakow's fork) is the undisputed king of hybrid CPU/GPU offloading and MoE performance. However, every time I benchmark it against mainline ggml-org, mainline consistently beats it by a wide margin. Am I missing specific flags, or is ik_l