Search: “models”

I'm mosty interested in 128-192GB VRAM with 128-256GB RAM to spare, so SSD streaming is basically not even necessary. Seems only FP4 is supported, so older hardware will likely be slow - no Unsloth GG

I don't own a mac Right now, i want to buy one but its main job will be to host a Ollama or similar software to give me usable AI models in my network, since I don't want to pay for Cloude, GitHub cop

Hear me out before you roll your eyes. We tend to only recognize turning points in hindsight. Nobody in 1993 thought the Mosaic browser would be a history book moment, but the web is. I think Stable D

A new llama.cpp PR (#26563) adds a heatmap that tracks which MoE experts are used most often. Instead of keeping every expert on the GPU or offloading all of them, it caches the frequently selected ex

I've been working on speeding up DeepSeek-V4-Flash-0731 in Krasis and have now got the long-prompt prefill quite a bit faster on a single RTX PRO 6000 96GB. These are timing-disabled internal Krasis r

https://preview.redd.it/522fsdwvtdhh1.png?width=1200&format=png&auto=webp&s=6a6cf7a467514167a8193029dbd20fb3a9ba4f6c It ranks lower than both Sonnet 4.6 and Luna. I'd wager Luna costs in the same ball

Went public in the last few minutes, both repos ungated. Ling-3.0-flash, BF16, 24 shards, ~255GB Ling-3.0-flash-fp8, official FP8, ~128GB 127.5B total, they quote 5.1B active. What jumped out at me in

Here's the build I managed to scrape together System Specifications: CPU: Intel Core i7-7700K Motherboard: ASUS ROG Strix Z270-E Gaming RAM: 32GB Corsair Vengeance DDR4-3000 Storage: 1TB Crucial P5 Pl

It's more accurate to say that many highly engaged online gamers are hostile to AI, not that 'gamers' as a whole are. Gaming is a huge community with hundreds of millions of people, and opinions vary

Id figured since they first emailed people about api price changes coming mid july then delayed the v4 flash release to late july, I wonder if they delayed it for the sake of stealing spotlight from o

I let Gemma4-31b run on my laptop for like almost a day using a heavily altered pi to do a deep dive on our beloved Llama tangentially related Subreddit, and this was the conclusion. Feels pretty accu

I decided to test a few questions against DeepSeek-V4-Flash-0731. Locally, I was running Unsloth's UD-Q2_K_XL quant. After I saw the surprising shape of the results, I tested against DeepSeek's offici

DeepSeek V4 Flash is now available on InferX, and it’s free to use. We’re continuing to add GPU capacity as demand grows. While we’re bringing additional capacity online, you may occasionally see high

I swear some AI doomers have never actually used a local model. They watched one flashy keynote, one YouTube thumbnail with a guy making this face 😱, read three headlines, and suddenly civilization is

Hi everyone, Can anyone suggest a learning path to fully understand the technical report for Kimi K3? My background: • I've taken a graduate-level deep learning course. • I understand the Transformer

I ran into a doubt when using the following command. It seems that the System RAM usage keeps increasing even though there is >10GB of space left in VRAM while using the MTP mode. Does the MTP head lo

I see a lot of discussion here about best subscriptions or APIs to get. Most of them comes almost always back to DeepSeek API for Flash, Opencode Go and Codex Plus. I have the combo Ollama Cloud + Ope

I bought an RTX 5090 last year just to run 27B models natively. I even fine-tuned it with my own data using LoRA, building RAGs and was pretty damn happy with the results at first. But, Q8 quantizatio

Hey guys, I am so excited to share this with you guys. I know for a lot of Pros here, this maybe a baby's work so please be gentle. Until a month ago, I didnt know anything but to use those google AI

I just managed to get it running on windows and this thing is fucking insane. I get around 550-720t/s depending on task at hand. Previously to get to such numbers i would have to do batching and agent

Running GLM-5.2 (the new glm-dsa arch), Unsloth UD-Q4_K_XL, on a 4-socket Xeon E7-8880 v4 box with 1TB RAM and a single RTX 3060 12GB. ik_llama.cpp, experts on CPU (--cpu-moe), 24 attention layers on

If you run local agentic coding harnesses (Aider, Claude Code, etc.), prompt evaluation usually eats up most of your execution time. Every turn re-evaluates thousands of identical prefix tokens_system

https://preview.redd.it/yw41s1jikefh1.png?width=1942&format=png&auto=webp&s=3a180ae6443c1db9f7b0ce621533a4b2aa553921 Hi, I've been running Ollama on my Unraid server since the llama2 era. I use to be

Hi everyone, I'm looking for a local alternative to Kling AI 3.0 Motion Control that I can run in ComfyUI. What I'm specifically looking for is a model or workflow that allows me to: - Control charact

Hi Guys, I'm an old coder and DBA that has been in the field for almost 40 years. More and more the jobs I was doing for work are being taken over by AI and the need for my type of work is diminishing

I’m not affiliated with this project, but I’ve been running it recently and I’m surprised it hasn’t received more attention here: https://github.com/fewtarius/CachyLLama CachyLLama is a fork of llama.

https://github.com/shootthesound/Fizgig Intelligent trainer - Per-image loss tracking with self-adapting training runs — every image gets its own verdict (easy / suspect / stuck / exhausted) and its o

Link to their official GGUF repo: https://huggingface.co/poolside/Laguna-S-2.1-GGUF/tree/main All the GGUFs received this fix 5ish hours ago - correct yarn_attn_factor to 1.0 (llama.cpp derives mscale

Disclosure: I work at Pluralis Research, the lab that built this. Code is open, and I'm happy to answer questions. TL;DR: As far as we can tell, this is the first RL post-training run whose entire rol

This post discusses a solution for keeping multi-hop retrieval-augmented generation (RAG) systems updated when data changes, using an embed-and-append approach with the open-weights Llama-3.3-70B mode

Ideogram 4 is Ideogram's first open-weight text-to-image model trained from scratch, introducing structured JSON prompting with multilingual text rendering and explicit layout controls. Released on Ju

PR #24269 added native video input to llama.cpp's multimodal (mtmd) system, merging on June 8, 2026. The implementation uses FFmpeg as a subprocess to decode video frames and expands a single video ma

Ideogram 4.0 is a 9.3B open-weight text-to-image model that supports structured JSON prompts enabling control over layout, color, and text placement . Baidu's ERNIE Image is a multilingual text-to-ima

This post documents running the Qwen3.6-35B-A3B language model on dual GTX 1080 Ti GPUs using Ollama, achieving approximately 20 tokens per second. The author highlights three critical configuration r

Cosmos3-Super-Image2Video is a 64B model for temporally coherent image-to-video generation . NVIDIA's Cosmos platform is designed to accelerate Physical AI development by enabling machines to understa

Z-Image is a fast, open-source image model from Alibaba Tongyi Lab that has gained popularity for its speed and image quality. Users on r/StableDiffusion likely discuss techniques for forcing the mode

Physics-informed neural networks (PINNs) are machine learning models that incorporate physical laws and equations as constraints during training to solve differential equations. This post likely discu

A discussion of progressive training approaches for diffusion models where training begins at lower scale factors and progressively increases to target scale factors, leveraging previously trained mod

A developer created a visualization tool that displays GPT-2's internal concept activations in real-time during text generation, representing the model's 'thinking' process as a 3D graph that updates

FLUX.2 [klein] 9B is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities. The model matches or exceed

Andon Labs conducted an experiment in which four major AI models (ChatGPT, Gemini, Claude, and Grok) were tasked with operating 24/7 radio stations continuously for five months. Each AI was required t

A user experiments with serious Star Trek: The Next Generation styled video content using LTX-2.3, a video generation model with available LoRA style adapters. LTX-2.3 is a major upgrade to video gene

Uncensored LLMs are architectures that have been modified or fine-tuned to remove standard safety alignment layers (guardrails) that limit a model's ability to discuss sensitive topics. Ollama offers

Bare Metal: Z-Image Turbo - Flux.2 Klein 9b - Wan 2.2 discusses comparisons between these lightweight AI image generation models, with Flux.2 Klein 9B showing good prompt adherence and natural composi

This post describes a project where the developer created an autonomous AI agent capable of modifying and executing its own source code across a 7-day development period, integrating multiple language

This post documents a user's experiment comparing ChatGPT and Gemini's ability to create perspective portrait artwork using two reference images (a front-facing photo and a profile photo) along with a