Search: “model-releases”

I don't know about you guys, but Deep-seek harness is insane. It's not focused on being a coder agent, it's webUI made it very easy to just checkin from time to time, and the best part? Why it's bette

This is one more vibe slopped custom optimization for, in this case, my hardware (m2 ultra 60 cores, 192gb). It is just a fork from llama.cpp with a few changes, it achieves: - DeepSeek V4 Flash, no k

I've tried a few Gemma 4 models, for example 'gemma4:26b-mlx'. It says 'I cannot process images.'. On their website it mentions images, audio etc. https://ollama.com/library/gemma4/tags I’m using Olla

I'm running Qwen 3.8 27B locally on a single GPU. I can push the context to 131k, but I'd rather run it faster at 64k if the agent can manage context properly. What I have in mind is pretty simple: on

Heard a crazy stat the other day. Video generation represents around 70% of the share of AI token consumption in China. A combination of short form content and robotics. In China, video models are gro

Gemma 12B is obviously a very well trained model, I always thought the fine tuning they did on it wasn't really cut out for agentic coding. From my own experiences it struggles to use the tools it's g

I guess children needlessly dying of a disease that has long been fully preventable is a small price to pay for Byron Donalds to become governor of Florida. Brennan: 'Nearly all of Florida is below th

What I ran: 8x B300 on Modal, 56.79 per hour, vLLM, tensor parallel 8, native MXFP4 Cold boot ~27 min (1.56 TB load, JIT, 51 CUDA graph captures) TTFT 0.92 to 1.02 s, decode 92 tok/s steady, 83 tok/s

My setup: RX 6700 XT 12GB i5-11400F 16GB RAM Ubuntu 26.04 ROCm/TheRock + llama.cpp I've spent the last few months experimenting with different backends, quantization, CPU/GPU split, KV cache, batch si

If you maintain an AGENTS.md or a CLAUDE.md, this one is worth your time. (bookmark it) Researchers traced 94K development events across 557 agentic coding sessions, plus 690K file-level change record

It is not inherently bad to publish research on the impact of AI that only refers to older models, but it requires a very careful discussion and has to be very clear to non-technical readers. If you s

Hi all Looking at a PowerColor R9700 for Qwen3.8-27B, Q4, llama.cpp/Vulkan. AMD's own blog quotes 51.8 tok/s but doesn't say what context length that's at, or whether MTP=2 was holding up. Separately

Hey everyone! Last year, Mozilla released Orbit, an AI-powered browser summarizer hosted on a GCP server. After people started digging into the extension, they discovered things like backend endpoints

Hey everyone! Last year, Mozilla released Orbit, an AI-powered browser summarizer hosted on a GCP server. After people started digging into the extension, they discovered things like backend endpoints

i got it last week and im really impressed. its maybe like a sonnet from last year or 6 months ago. i had built some automation for myself and a dashboard cobbled together over the years from bits of

I was just curious how the new qwen3.8:27b does on a hard C to HTML porting job against Opus 5 in a default Claude Code. The job: my fun side project is a procedural shooter in a single C file. Port i

I noticed that a recent Ollama/llama.cpp setup was using a surprising amount of CPU despite the model being fully GPU-offloaded. My setup: Ryzen 9 9950X VM with 16 vCPUs 3× RTX 4060 Ti 16GB Qwen 3.8 2

.@poolsideai engineers were awesome to collaborate with to launch open models. Excited for what’s to come to open models, and the @nvidia team working on Nemotron. A sweeping agreement with startup Po

Prior to Fable, it felt silly to waste too much time improving your coding harness or context strategies. A new model would arrive at the same price (or cheaper!) and paper over most of your problems.

Most YouTube benchmarks only show trivial tasks like generating landing pages or simple Three.js games. Is a local model like Qwen 3.8 27B actually capable of real-world systems programming—such as bu

Our devs got their hands on it a few days ago. One wired it into Codex to compare with GPT Luna, our usual workhorse right now for its cost effectiveness. Another tried it out on one of our OCR pipeli

I was fascinated by Nanbeige's outstanding performance for its size, so I started digging into how much a model can improve its own representation just by looping over itself (for fun). My prototype w

I have been lurking on reddit for like 6 years, and this is the first time posting, so apologies for the mistakes. I'm not a native English speaker, so sorry for the bad English too. I used LLM to tra

Hello, So I've been trying lots of combinations in that never-ending landscape of options and settings. I wanted a proper quant of 3.8 27B running as fast as possible on my 5090 at 400W, with vision a

Companion to the Qwen 3.8 Release Megathread. Compiled from ~2,000 posts scanned across both subs, with deep reads of the 45 highest-signal threads (560 posts and comments), Aug 15–22, 2026, plus inde

Sam Altman blows whichever way the wind does Sam Altman admits he was wrong on AI's timeline and says society and the economy will adapt more slowly 'I thought when we got to GPT-4, which was back in

Further to my last post, https://www.reddit.com/r/LocalLLaMA/comments/1vldngi/tested_in_coding_bf16_muse_glimmer_vs_bf16_qwen36/, I bring this requested comparison of Qwen3.8 27B. Context: Both models

the difference in productivity between 0-1 mode vs. working within a big team at a large co seems universally true even pre-chatgpt. it's really easy to be 'productive' when (a) you're the only person

The latest RAG trend for the current agent harnesses (Codex, Cowork) is to do two passes of document processing to solve a knowledge work task over a data room of documents: 1️⃣ A fast and light pass,

The research on financial advice https://x.com/emollick/status/2085174123743842448?s=20 This paper by researchers from MIT and Stanford finds that most people would be financially better off if they f

They treat DC as a plaything for incompetent leaders. They treat their job as a grift and a joke. Vote them ALL out Mike Johnson says he wants to make the downtown DC Indycar race 'an annual event ...

Hi everyone, I'm currently working on optimizing an LLM setup at my company and I'm trying to identify where the actual performance bottleneck is. Our current setup is roughly: LLM: NVIDIA Nemotron mo

Me and my team made Atomic Dynamic GGUF quants for Qwen 3.8 27B, so we wanted to see the difference between them by giving each quant the same voxel island creation task First of all we were surprised

It performs at full capacity without any issues anywhere, but only the Ollama DeepSeek seems particularly trashy. Why on earth has the Ollama DeepSeek unusable trash that goes into over reasoning and

If anyone still remembers GLM-4.5-Air from last year, you can now get a nice speedup by enabling MTP in llama.cpp. It is a 106B MoE with only 12B active parameters, which makes it interesting for mach

Spent way too long confused about why an 8B at Q4_K_M, about 4.9GB on disk, would OOM on a 12GB card. The weights fit twice over. The answer is the KV cache, and the math is simple enough that you can

What models and configs are we using? Please share here On windows, I am using this copium pared down model https://huggingface.co/Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF with MTP disa

The post describes some experiments I had while trying to desperately run deepseek-v4-flash-0731 4 bit+ quants on my machine which is supposed to support only q2 quants of the model, a or 2.xx bpw qua

I've been working on a project for a few days and now all of a sudden chagpt is unusable.. It will list the next steps of implementation but never actually start working on them. anyone else? UPDATE I

A couple of days ago, I did a quick explainer on Claude’s new watermarking process and implementation. Since it’s such a popular topic and sparked such a lively discussion, I thought it might be inter

https://preview.redd.it/84zi5nsdawkh1.png?width=2368&format=png&auto=webp&s=1109e69db807b153064b1f5b61d22cf1e9fbca05 Another user posted the benchmarks for Qwen 3.8 27B today, and while I think Qwen 2

model: add dots3-note (#27060) text: conversion init impl address review comments fix rope move to a new llama_kv_cache_dsa_iswa Website: https://llama.app Attestations: https://github.com/ggml-org/ll

mtmd: support webp via ffmpeg (#27520) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/42289035 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (

sycl : add Q2_K reordered MMVQ and ESIMD kernels (again) (#27490) Revert 'Revert 'sycl : add Q2_K reordered MMVQ and ESIMD kernels (#26336)' (#…' This reverts commit 7a0e42f. add gate params Website:

common : fix draft-mtp with embeddings (#26352, #27299) (#27400) common: fix draft-mtp with embeddings (#26352) --whitespace Co-authored-by: Sigbjørn Skjæret sigbjorn.skjaeret@huggingface.co Website:

ggml: optimize concat op by replacing per-element memcpy with row-level memcpy (#24575) ggml: optimize concat op by replacing per-element memcpy with row-level memcpy ggml: fix concat offsets for row-