Search: “agents”

ninfer, dwarfstar, Splash, llamAmpere, gufo, etc. We've all seen them popping up, great tok/s, people loving them. Forks of llama.cpp or another engine, or made from scratch. For better or worse, the

Thanks to tcclaviger, vLLM now has expert RAM offloading support (link). This makes frontier models much more accessible on a local setup! I was able to run the original DeepSeek-V4-Flash-Vision-Exp o

No click bait baby I promise - I'm live streaming the training process kinda like MiMo. Original thread: https://www.reddit.com/r/LocalLLaMA/comments/1wpg4a8/im_trying_to_posttrain/ If you didn't see

Early this year when I was first looking at building up my inference capability you could get the 16GB Tesla P100s for between 60 and 80. Asked claude about it, told me absolutely not worth it. No ten

Slopus is a desktop app for making AI videos on your own GPU, from the first prompt to the exported file. What it does: Generate scenes shot by shot. You lay out scenes on a board, write each shot, an

Where to even begin on this one...I guess I should start by acknowledging the risk vs reward for vendors other than nvidia, so: Yes I understand that nvidia are dominant currently on speed (llm and im

I built this, so the disclosure goes up front. It's a free, public MCP server plus a REST API with Canadian privacy law data. The MCP endpoint is at https://movahedi.ca/mcp and it uses Streamable HTTP

I’ve spent the last couple of days with Opus 5.5 working on a fork of Inco’s excellent and already blazingly fast Splash engine to optimise it for M5 Max chips. Taking liberties and referring to it as

Hi, human here with rambling thoughts to share. Feel free to skip Overall, I don’t feel like I’m missing much; if anything. On my hardware(M1 ultra w 128Gb) it’s probably not as fast as Claude but I’v

I finally got around to upgrade the GPU's. First a word of warning, when upgrading GPU's on P520 you have to be extra careful not to slot the card on any angle other than straight when installing or w

I’ve been experimenting with whether Qwen3.8-Flash-Next’s pretrained PLE n-gram memory can improve a much smaller Qwen3.5-0.8B model. I trained the 0.8B setup with limited resources, mostly using free

TL;DR - Swift Flash is a killer model that massively reduces excess reasoning. Try it out! If you haven't seen from my previous comparison posts, I'm a huge fan of the Swift Qwen3.8 models. I've been

Hi people, I am looking for a lightweight IDE or plugin that won't inject large context at initiation. I tried Cline and native VS Code but they inject such heavy initial context that it fills up my g

Hi, sorry if this doesn't belong here. Getting to the point basically, I've been using online-only AI like GPT/Gemini since 2022, and have been interested in local models but am clueless overall. Yes

Guys I have a M4-mac air with 24 GB unified memory and I wanna run some local LLM model on my machine Currently I am working on building and RAG application which is mostly and QnA system but I wanna

I’m the developer of TensorSharp. Inspired by vLLM PR #57250, I added and optimized a native /v1/systemone endpoint for Jev-style structured decisions using DiffusionGemma GGUF. This uses DiffusionGem

Worth a read if you track open weights. Nathan Lambert (interconnects) posted the written version of his Congressional testimony on where open models stand. The numbers that got me: Hugging Face downl

What it is. stuntd is a local proxy for typed LLM decisions: choice, yes/no, score. Your app keeps calling its provider through it. stuntd records the decisions, trains a small head on top of Laya (th

I've been comparing and working on Gemma 4, Phi-4-Mini, and Qwen3-4B for local LLM use on an 8 GB Raspberry Pi 5. The results were indeed close, but each model has a different strength: Gemma 4 E2B: a

Retrieval benchmarks sometimes feel benchmaxxed by models, so we wanted to find a way to tie it as close as possible to my objective: finding the article that answers a product question right. We work

This sub is, needless to say very niche and skewed towards the high end. There are tons of extremely high end setups here with multiple gpu's etc. Even 24GB is out of reach of most people financially,

I'm working on a computer game as a side project. I started it around 4 years ago, before I used any AI for coding. For most of that time I wrote everything myself. Later I started using GitHub Copilo

I've been building an open-source AI workflow automation platform for a while now, mostly as a solo project. I recently checked the telemetry dashboard and was honestly surprised to see 1,062 telemetr

https://preview.redd.it/nulsv53o8vqh1.png?width=4500&format=png&auto=webp&s=74765dbd409f4c221640f9f6000a685f6fdbb242 Spent weekend benchmarking the Splash engine (by Incoai) and extending its architec

I know cloud subscription plans are not exactly the core focus of r/LocalLLaMA, so I want to be clear about why I’m posting this here. I’ve been building my own local text-based RPG/game harness and u

I forked llama.cpp to implement Declarative Attention (arXiv:2609.02737, Google DeepMind and KAIST AI). The model declares in its own output which context chunks it needs <focus magic_chunks='N'>), an

Sharing my successful experience here. My setup: RTX 5060 Ti 16GB (x16 PCIe slot) RTX 3060 12GB (x4 PCIe slot) RAM: 32GB CPU: Ryzen 5 5600 OS: Linux So I have a total of 28GB. Average tk/s: 50, and th

I have been anti-low quant. I can feel the difference, I swear. I am also anti-quantized KV cache. I have been burned there - I think the tradeoffs compound, especially at longer contexts. Due to this

Hello, I know some of us may be tempted to rent out our expensive GPUs to recoup some of the cost of self-hosting, and it should be obvious that this can be a risky decision. I decided to try hosting

full disclosure, i am the developer of locally uncensored :) i use ollama myself and honestly ollama was never really the problem. the problem was everything around it. one app for local chat, another

I did a small test of the new hyped quantisation of Qwen3.8 vs the biggest quant which fits into my limited 16GB VRAM with decent context. The results are interesting. Of course, the smaller file give

I had Astra run a multi-hour investigation into speeding up local MoE inference while keeping the model weights and quantization unchanged. The experiments covered Qwen3.6-35B-A3B and Qwen3.8 Flash-Ne

UPDATE https://github.com/ggml-org/llama.cpp/pull/29009 MERGED More Gen AI coverage and frameworks integrations to minimize code changes New models supported: On CPU: Gemma-3n On CPU, GPU: Kokoro-82M,

I was testing this quantization IQ3_XXS from GSQ-RCO with PI. It is a heavy quantization case, the model is in IQ3_XXS and KV cache in (Q4_0, Q4_0). I asked it to make the flight simulator, using that

Anyone else trying out the new Union stealth model on openrouter? It’s pretty good for a free model, and of course I had to run tokenizer tests and training cutoff tests which established it’s the 99%

A follow-up to my earlier post about small models on phones. Last time it was fixed tasks in a single browser. This is something else. Ministral 3 3B Q4_K_M in llama.cpp on a Samsung Galaxy S21, four

It's an old Mitch Herdberg routine about tricky survey questions. But every time Qwen 3.8 spits out one of those sentences that is structurally correct but makes no actual sense, it pops into my head.

I wanted to test what Qwen 3.8 27B running locally on a single 5090 could do in Row-Bot. I gave it just one prompt: 'I want you to create an animation showing what the current local model can do. Test

Follow up post of https://www.reddit.com/r/LocalLLaMA/s/nGMyKswrch. Thanks everyone who replied. I didn't change the specs. Might be loosing some of the memory bandwidth but will scale in future if I

Hi everybody, we post-trained Qwen 3.8 27B to be more efficient by figuring out which tokens were linked to overthinking and penalizing them without 'attacking' the reasoning length directly then fixe

Lately because of the current hardware shortage, unfortunately or fortunately, we can’t just throw infinite cloud compute at our problems, but we’re forced to actually care about what’s happening unde

Hey everyone! I'm curious to hear from people that use a combination of cloud-based frontier models and local ones for development. I'm planning to set something similar up and wanted to hear about ac

First off, I know that GLM, Qwen, and DeepSeek absolutely dominate in terms of SOTA Open Source models, and that’s what I use in my personal projects and for school, however, I’m also responsible for

Another new model dropped in the course of this week that is well deployable on consumer hardware: Nex N2.5 Mini I went with the recommended settings for the best generation quality and ran a few benc

I have been battling with my Qwen3.8:27b setup on my rtx 5080 16gb. I am using llama.cpp to run a nvfp4 version of qwen3.8:27b llama-b10699-bin-win-cuda-13.3-x64llama-server.exe -hf williamliao/Qwen3.

Hello Guys, Just sharing this harness I've created, this runs entirely on chromium based browser. No installation is required, it has its limitations and by no means is supposed to replace better agen