Search: “r-localllama”

local vibecoding with Qwen 3.8 27B and Godot

OK, you guys want to start with local vibecoding but don't know how. In the discussions, you recommended that I use Godot. I downloaded it without knowing what it was. I was expecting a library, but i

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My local LLM demoscene generator can now watch its own output and rewrite it!

DGX agent

I've updated my auto_demo_scener project with Ninfer support and a “rewrite based on video” feature that I thought you might find interesting. The project is basically an endless demoscene machine. A

model-releasesr-localllama

5 Sept 2026

My only real use case for a local AI use is document management, how much VRAM do I realistically need for a good experience?

I just want to use paperless-ai and be able to ask questions relative to it. Bonus points if I could use it with home assistant but that's not the focus. I just can't see needing a 32 GB VRAM GPU for

NInfer fork: 555k context@fp4 for 5090 with YARN, reliable kv host cacheing, monitoring, jinja, opened model support

Hiya, NInfer is amazng for Qwen, but lacking for real-world-use. As adoption of issues/pr's was not really what I needed, I created a fork and hit it for this week with 3 concurrent claude code sessio

NInfer vs llama.cpp vs vLLM: quality + speed comparison for Qwen3.8-27B NVFP4 on RTX 5090

I've been running Qwen3.8-27B as a local inference server for a production content intelligence pipeline (HVAC industry stuff, lots of long-context retrieval and structured extraction). I have been wa

Otaku — an LLM frontend

Otaku is an LLM frontend, primarily designed for roleplay, an alternative to SillyTavern and the like. However, It also works for general-purpose chat with local backends (including Ollama) or cloud m

Qwen3.8-27B beat the Wikipedia game in 6 clicks.

Used qwen3.8-27b in Opencode to make this silly mini-game because I'm not sober: ``` We are going to play a game, it will be the Wikipedia game. The Wikipedia game has the following rules: You will ha

Qwen3.8 27b for agentic coding and next .... what?

First, I'd like to thank the Qwen and Unsloth teams for the Qwen3.8 27b UD Q4_K_XL. Fits the poor 24GB of 3090 VRAM with 100k context at Q8 and works phenomenally well! Imho if theres anything that ca

Qwen3.8 27B on Strix - the optimized setup

Ever since u/jfowers_amd has asked me to help with the Lemonade project (and provided some hardware to test on), I've been trying my best to optimize llama.cpp for AMD setups. This has led me in some

Qwen3.8 Flash Next - Templates Comparison

I was running into a lot of posts that praised both the Fixed template and the Sharp template in comparison to the stock one, so I put them to the test. It's not as extensive as it should be for a pap

Qwen3.8-Flash-Next (UD-Q4_K_XL) on a single RTX 3090 24GB + 128GB DDR4, is this config optimal?

Qwen3.8-Flash-Next (UD-Q4_K_XL) on a single RTX 3090 24GB + 128GB DDR4 — is this config optimal? Hardware CPU: Intel Core i5-12600K RAM: 128 GB DDR4 @ 3600 MHz GPU: NVIDIA RTX 3090, 24 GB VRAM OS: Win

Reusing old hardware for starting local ai-journey?

Hi, I am thinking about putting some money / time into my local ai learning path and 'cleared' my attic where I found the following hardware. 3 x NUC11 (Core i5 1145G7 / 2,6 GHz, 64 GB RAM DDR4 2666MH

The gap has closed, open source will win

I've been trying the latest models from the frontier labs and honestly, after extensive testing I can not tell the difference between the best open source options. I think the differences are now marg

Title: MLX-DLSS: NVIDIA's DLSS 5 neural renderer and frame generator, running on a Mac (and in PyTorch anywhere)

NVIDIA ships two neural networks inside DLSS: the DLSS 5 renderer that makes game frames look more photoreal, and the frame generator. Both only run on RTX cards inside games that call DLSS. I pulled

Which agent harness do you use and why?

I see a new one being launched every few days... How do these new harnesses compare to claude code, pi etc. has anyone switched from these? which harness to prefer and why submitted by /u/Background-J

~22% less weight VRAM, lossless: base-3 packing for ternary GGUFs

I built a denser GGUF format for ternary models: Q2_B3 / “B3S” If you're running a ternary model like BitNet-b1.58 or Ternary-Bonsai, the weights are already restricted to -1, 0, or +1 times a block s

Hardware

Does high / long term inference damages GPUs?

I always heard that cryptocurrency mining can damage a GPU ( it's maybe wrong) so how much long term inference is damaging a GPU? I mean servers are made for 25/7 operation... Gaming GPUs not so sure?

hardwarer-localllama

Drummer's Artemis 31B v1 and v1.1 - Coming back with a bang!

Hey everyone, been a while! https://huggingface.co/TheDrummer/Artemis-31B-v1.1 https://huggingface.co/TheDrummer/Artemis-31B-v1 A few months ago, Gemma graced us with models that served as a much need

Even Qwen3.8 followed the instruction inside my translation data, and Gemma 4 beat the translation specialists I tested

A month ago I posted about Gemma sometimes solving the reasoning problems inside my translation data instead of translating them. A few people suggested two very fixes, which is to use a proper transl

Has anyone already tried IFM's new K2-Horizon-MoVA-36B-A4B?

How good/bad is it against comparable MoEs the same size? How does it compare against Qwen 3.6 35BA3B? Since we don't have 3.8 35B this seems like an upgrade if we look at some benchmarks like termina

Help me understand gguf size/ctx size

Let's say I have 2x 16Gb GPUs and I want to run Qwen3.8 27B. Monitor is ran by the integrated GPU so both 16Gb GPUs are almost fully free. I load the UD-Q4_K_S on one card at 15.4Gb. I then load the c

How do you guys handle your personal RAG setup

I am getting into developing a RAG setup, for getting information out of existing documents, new document ingestion, web searches, and good visuals. I am planning to use it for, alongside the regular

How to estimate tokens/sec for your hardware

We all want more tokens per second but I keep seeing confusion on what to expect for given hardware. For the decoding phase (TG/s) to produce one token all the model weights and KV cache needs to be r

I benchmarked 21 Qwen3.8 27B variants on 16GB VRAM

After Qwen3.8 27B came out, I decided to benchmark the models that could fit in my GPU (RTX 5080) on my actual code (C code), the results were not completely unexpected but some quants were definitely

I built a server with 768GB VRAM for frontier, but all new frontier open source models are likely to be two trillion or above now, including next GLM 6, am I cooked?

This epyc server I am using twelve cards with 64 GB memory, plus 256GB ram. Looking at the most capable models in open source, GLM 5.3 seems to be the only option, but with Astra releasing it will lik

Agents

Ling-3.0-flash-VL, built on Ling-3.0-flash with visual understanding and visual agent capabilities

It performs well across visual perception, STEM reasoning, document intelligence, multimodal agent tasks, frontend coding, and medical report interpretation. submitted by /u/niacolhealth [link] [comme

agentsr-localllama

Qwen 3.8 Flash Next Can Build Funny Games

This is nothing impressive but, i had so much fun i wanted to share my experience with this model. (yes this post is written by human) I made an FPS with local Q4_K_XL 3.8 Flash Next (256k context) (i

Qwen3.8-27b is the first Local model im able to blindly trust

You know that thing where you just throw a task at a frontier model and not have to supervise it worrying of it going off course? Qwen3.8-27b has officially gotten me to that point for local work. He

Qwen3.8-Flash-Next: 256k context, 16tok/s on DDR4 and a Tesla T4

I've got an refurb Dell R740 running Proxmox that I put a Tesla T4 in, mainly to run some CTC local transcription work, but thought it would be fun to try DS4 when it came out, and it was appalling at

Tossed distorted audio samples to an open-weight voice model; it did fairly well.

Being a person obsessed with testing new models that come out, times are really insane for me. Tested different kinds of TTS and voice cloning models but none of them gets it right in terms of emotion

UPDATE: Qwen3.8-Flash-Next on 2x3090 + DDR4 (Part 2): 25-29 -> 37-41 t/s decode (UD-Q4_K_XL + expert cache + MTP), plus a branch you can build

This is a follow-up to my post from yesterday (17 -> 25-29 t/s with the expert cache PR). Same box: 2x RTX 3090 on PCIe 3.0, dual Xeon E5-2696 v4, 188 GB DDR4-2133 LRDIMM, llama.cpp, full 261k context

Updated my benchmark with a new vLLM based recipe for Qwen 3.8 Flash Next : now up to 98/100 (instead of 91 previously)

I was using: weights https://huggingface.co/RadixArk/Qwen3.8-Flash-Next-NVFP4 with the optimized SGLANG (patched) from https://old.reddit.com/r/BlackwellPerformance/comments/1w04xb7/qwen38_flashnext_o

We open-sourced Paddock, our Rust/C++ inference engine with its own CUDA kernels (MIT/Apache-2.0)

I'm one of the developers. We said in August it would go open source in September and it did last night. MIT or Apache-2.0, pick one. The repo you see is our internal repo, kernels included, so from n

You can now run a 90M conversational LLM on the Sony PSP (hardware from 2004). Doesn't get more local than this.

Github link: https://github.com/thatblend/LLMPSP I wanted to see what the PSP can theoretically handle and I got my answer - a 90M model is about the max it can do without atrocious inference speeds.

AntLing open sourced Ling-3.0-flash-Fin, a finance-enhanced model for real-world workflows

Ling-3.0-flash-Fin is the first finance-enhanced model in the Ant Ling family. Developed by Ant Group with leading financial institutions and domain experts, it extends Ling 3.0 flash through continue

Can a 4B local model actually feel like an AI assistant?

I've been building Arcon around Qwen3-4B + LoRA. Instead of just making it a chatbot, I'm experimenting with persistent memory, personality/mood, internal state, tools, and eventually having it proces

DeepSeek-V4-Flash vs. GLM-5.3-Flash on 2× DGX Spark

I've tried both and been having this debate with myself for the last few days, on two Asus Ascent GX10s (effectively the same as 2x DGX Spark): DeepSeek-V4-Flash-0731 (official weights) GLM-5.3-Flash

Frontier models sabotaging local AI implementations?

For a few days I've been working on creating a custom local-only harness for some work related research using Codex / GPT 5.6 Sol and the model feels not only dumber than usual, but straight up counte

GLM5.3 Flash over DSV4 Flash?

I've been using Deepseek V4 Flash 0731 for a few weeks now and while I havent thrown it anything very hard, im quite happy with it. Using through antirez's great ds4 project. They've added support for

Google released TimesFM-3, a 330M-parameter time series foundation model with native multivariate forecasting (non-commercial license)

TimesFM-3 is the third generation of Google Research's zero-shot forecasting model, and the main change from 2.5 is that it handles multivariate inputs natively instead of being limited to a single se

I released sanoTTS: smallest complete TTS stack in 294k params (337 KB) that runs on $3 microcontroller and a 1.46m one that beats models 3x and 10x it's size

I have been trying to squeeze TTS stack down far enough to run in a $3 chip which has 512kb of SRAM without NPU. While trying to get to that milestone i built sanoTTS which has - 11 voices, 6 language

IFM/K2-Horizon-MoVA-36B-A4B-GGUF · Hugging Face

more sizes (probably still uploading): https://huggingface.co/IFM/K2-Horizon-32B-GGUF https://huggingface.co/IFM/K2-Horizon-7B-GGUF https://huggingface.co/IFM/K2-Horizon-3.7B-GGUF https://huggingface.

Increasing active parameters per token in MOE (Qwen 35B A4B+) reduce reasoning token by 8.5% - and you don't need to train or finetune!

I want to share a short paper just published exploring a simple but surprisingly effective optimization for sparse MoE reasoning models. The idea: Instead of retraining anything, we just tweak the rou

Introducing Quartermaster, an open source local AI platform designed for ease of use that does not sacrifice customizability

It started as a fork of llama-swap, but I have been building it out for myself since then as a convenient tool for all my local AI needs, and by now it has drifted far enough to be its own thing. The

model: add NVIDIA Nemotron-3-Puzzle-75B-A9B (NemotronHPuzzle) support by YanissAmz · Pull Request #25444 · ggml-org/llama.cpp

75B MoE is an interesting size to check, you can run it today (no MTP support yet) The model employs a hybrid MoE architecture with interleaved Mamba, MoE, and Attention layers. Like Nemotron-3-Super,

Hardware

'ModelScope' Is a Hugging Face Alternative now that Nvidias deal is a Go

I liked the Nvidia that focused on just GPUs for gaming, not on the Nvidia of today which seem want power consolidation. Modelscope is another platform for those that simply want to know an alternativ

hardwarer-localllama

My RULE of Thumb of choosing a models

This is mostly for setting up for expectation, since personally without LLM i could take 3 days (15 hours of active programming) to debug or implement a feature, but with Qwen 27B (even before Qwen 3.

Need to decide: DGX spark vs framework desktop vs Mac mini/studio

I’ve been running qwen on my personal Mac but I’m getting to the point where’d I’d like to have something always on, running various jobs, and some more ability to experiment and earn about fine tunin