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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-

It seems Dario's 'too powerful for you users' strategy is paying off: we have two open models at the top of the leaderboard, surpassing every single model from Anthropic. Open source prevails. Even Mi

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 (p

Hi everyone! So far in 2026 I have been iterating and evolving a research system I designed to help me sift through things that LLMs all agree on that may not be correct, and to give me actual confide

ALHR - Adaptive Learnable Hierarchical Routing, uses static binary trees and learnable functions to minimize the amount of keys to be read. It does use a dense teacher while phase 1 of training howeve

I built MaRN (Mapping Networks), a PyTorch library that lets you optimize a compact latent representation instead of directly training every model parameter. Some results from my current benchmarks: M

Disclosure: I'm the developer. The app is called Citenook. I wanted to ask questions across a few hundred PDFs and notes with Ollama, but every setup I tried either needed a lot of glue code or gave a

Last month I created a portable memory format as a .txt file as a small project. I named it as: Memory Journal, in a shape of a skill card. It's essentially a large prompt wrapped in a text file. Init

Live results, coding benchmarks included, agentic benchmarks included, domains and other classifications filters included. (Spoiler: DeepSeek-V4-Vision-Exp rules, but other models have their rule area

In the dawn of the LLM craze, when wild Llama2's roamed free and 4k tokens was considered a fairly long context for local models, I forked an interactive fiction and MUD library called Tale to experim

It's been a long time coming, and I should have done this in the first place. But LumaBrowser is now open source. Since I first released it a year ago, it's had several thousand installs and an absolu

Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens that the target model subsequently verifies. With

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. Be

Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints remains challenging. While elastic architectures

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.

(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

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 archite

LemonSeed Studio is an iPad editor/IDE with on-device inference on an AMD GPU in a Thunderbolt enclosure. It embeds the unmodified upstream Linux amdgpu + amdkfd driver (mac_linuxgpu) as a PCIDriverKi

I wanted the uncensored Qwen3.8-27B (llmfan46's Heretic build, MTP head preserved) on my RTX 4080 with MTP on (because I've been testing TONS of configs, and realized that would be the one) And none o

Hello all! Thank you to everyone that contributed their feedback and notes for LlamAmpere the last time I posted. I've continued to chip away at improvements for the 3090 crowd -- this release is mode

ik ik 'another harness D:'. so... i spent the last 6 months of my life building and ironing out every single detail and bug through almost every route with extensive testing across 3 machines, daily,

I have rtx 4090 and 64 gb of ddr4 ram. This is enough to fit smaller quants of qfn, but I found them not great and switched back to qwen3.8. However, there are option that bother me a lot, buy huawei

HauhauCS ships their uncensored Qwen3.8-27B as GGUF only. NInfer, a C++/CUDA wanted its own format. Now the same model that ran at 91.6 tok/s / 131K under llama.cpp does: 262K context (the model's ful

SlopSoup TV is a live, never-ending pixel-art TV network. Every script, character, voice, camera cut and schedule decision is made by models. It's made to be very absurd, dark and strange. Hardware: o

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 an

A bit of backstory : i got lucky in August and was able to grab a CMP 170 HX 8GB for around 500 on alibaba. I tried later to get a second one for around 1k but my order got cancelled and prices went t

Youtu-Parsing-Omni is a compact (5B) omni-modal parsing model. Given a single input — a document page, a natural image, a chart / flowchart, a geometry figure, an audio clip or an audio-visual video —

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: Ph

Figure 1b from our paper Disclosure: I'm one of the authors (Microsoft). The paper, code, dataset are public and ThinkingBox is on Hugging Face OpenEnv as well. Raw evaluation trajectories are not rel

I've been building ANCHOR, a web automation agent that runs entirely on Ollama. You describe a task in plain language ('Register Maria Silva, IT department, contractor, and save'), point it at a site,

Preamble and disclaimer, Sample size: 8, at any size of form this research is just screwing around being writing it down. This was just a little,(well... big), curiosity test I wanted to run to see wh

Post title is slightly misleading, I don't think you can get these for $350 each anymore but they're still pretty cheap all things considered. They're Radeon Pro V620's which are older RDNA2 enterpris

UCLA’s Trustworthy AI Lab is running a tournament on October 16 where AI agents compete in Pokémon Showdown, Werewolf, Red Alert, and Honor of Kings. It’s open to everyone, including remote participan

Hi all, a bunch of performance improvements have been landed in audio.cpp. The biggest highlight is Higgs Audio TTS, which now runs with around 6 GB VRAM, a 48% reduction in peak memory usage compared

There are way too many forks to keep track of, so I've gotten lost. As far as I can tell, Radiance VLLM is best for models that fit in GPUs while some form of llama.cpp is probably best for MOE RAM-sp

I'm developing a benchmark for a low resource language and I don't want it to be leaked and used for training when it is being used to get predictions. For locally run models it shouldn't be a problem

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

came across a pretty interesting technical breakdown of chatgpt's newly launched 'intelligent ui' feature, and thought this subreddit might find it interesting. for anyone unfamiliar with the concept,

TL;DR: For my use case, mradermacher/Signal-3.8-27B-Terse-Coder.i1-Q4_K_M proved out. Ymmv based on your domain-specific tests. Time taken to solve problems compared to frontier models is massive; esp

Currently having high success with this little niche finetune i found sitting in the corner of huggingface If you want to try it out here is a smaller quantisation iq3_s works really well in my codeba

This table to help anyone looking to build a budget Data Center homelab. I copied the bulk of value based, mid level, decent speed results GPUs and feed it to AI or SI and here are the recommended res

With latest Halogen version update (0.17.2), decode is consistently at ~45 tps even at high context with Qwen 3.8 Flash Next on a 128GB Strix Halo. This is some great work u/peonist-ai. Have been pump

UT-based small models, despite being trained from scratch on these tasks without internet-scale pre-training, consistently outperform most standard Transformer-based Large Language models (LLMs) by a

Disclosure first: I'm one of the maintainers, this is my project. If you've pointed Open WebUI or a script at a local server, you know the failure modes: the server restarts and every conversation is

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

Write-up + demo: https://drksci.com/labs-phosphene So this started as a bit of a gripe. Modern terminal renderers are seriously impressive and seriously complicated. GPU glyph atlases, texture caches,