Search: “r-localllama”

Hey Guys, What amazing weeks it has been for open source releases. I was really impresssed by DS flash final checkpoint and i have been playing around with it until qwen 3.8 released. I checked the be

It's a small experiment of mine to check thinking effort on Qwen and I do have to say xhigh does overthink but I'm not sure if it's bad because the result is rather amazing. Although the prompt was ve

Qwen released the 2.4T Max weights and I was curious how well it can re-create COD in one prompt I ran the model on a rented B200 cluster and used roughly 1.1M output tokens over a 5 hour time span Re

I managed to run the 143–144 GiB DeepSeek-V4-Flash-0731 UD-Q4_K_XL GGUF on four RTX 3060 12GB cards while keeping a 360k–376k context window. Hardware: CPU: Intel Core i9-10920X, 12C/24T RAM: 128 GB D

Overview UI-Mate-27B is an open-weight foundation GUI agent for long-horizon work across applications and operating systems. It observes live screenshots, reasons over the visible state, and produces

Hi everyone, I use AI agents to code and manage my personal files heavily. However, I am worried that the agents may accidentally delete some important files on my machine, outside of the defined scop

Theoretical use case: I have a compressed version of wikipedia, and some function or tool fooRAG. Then I set my system prompt (or make a skill, or hook, etc.) to include 'You can search wikipedia with

Qwen 27b Q3_K_M 2x rx 580 8gb (~50 each in my country, edge cases 60 per gpu) gives us 16gb vram We used it on an old already existing ddr3 motherboard with 2 gpu slots(you can buy it ror around 200$

Following up on my previous post about my budget server setup (Intel N100 + RTX 5060 Ti 16GB), a few of you asked for a deeper dive into my actual inference config and real-world agentic performance.

Guys, could someone smarter than me explain what makes Deepseek Harness so efficient? I run it with local Qwen 3.8 (Q6). I tried Opencode/Openchamber (my favourite so far), Pi agent and Hermes. New Qw

Note: I use a Mac Studio M3U with 512 GB RAM, so this is not for everyone. I have been using antirez/ds4 with DS4 Flash for a few weeks now. Top quality, I am really impressed. This combination just d

EXL3 is an alternative to llama.cpp. And while there is extensive tooling for llama.cpp, EXL3's primary deployment (TabbyAPI), has a OpenAI compatible API so it shouldn't matter. Why won't this tool m

Do you think today’s GPU-poor systems running Qwen 3.5 9B can outperform the ones we had with ChatGPT-4o? I mean, when 4o disappeared, people felt like they’d lost a great model, and now Qwen 3.5 9B Q

Trying to get a feel for where I stand. If you can list your relevant hardware and model used, that would be awesome. Here's mine: Model: Qwen3.8-27B-heretic-ara, Q5_K_M GGUF T/s: ~30-32 t/sec (I thin

I created 3 SVG prompts, each one rather hard. Perspective: A animated drone view perspective on a park. Beauty: A beach scene with an evil cat Composition: An AGI breaking out of a virtual sandbox pr

I'm back. Yesterday I released the first version of hyper-optimized Qwen3.8-27B inference engine for a RTX 3090, reaching 82 tps on single request and 672 peak. Over the last 24 hours I've been explor

Support for the new ling 3.0 models has been merged into llama.cpp: https://github.com/ggml-org/llama.cpp/pull/26608#event-29549472828 Ling tiny 8b1b - https://huggingface.co/inclusionAI/Ling-3.0-tiny

In medium reasoning mode, it both scores higher than the 3.6 version, AND is very much more efficient (almost half requests needed, and a third less tokens generated) - at DeepSeek v4 Flash 3107 MXFP4

Hi all! I finally just got around to testing out Qwen 3.8 27b. I'm using Unsloth's UD-Q8_K_XL quant as a sit-in replacement to Qwen 3.6 27b, same quant size. Wow -- this thing isn't messing around. I

for those looking for something small AND powerful, there is a new 1B (they claim, it looks more like 1.7B ...) model that claims to beat qwen 3.5 0.8B & 2B and gemma 4 E2B on a range of benchmarks. t

We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve compositional reasoning.

Thought i would just add my own flags here for llama.cpp (literally pulled and rebuilt latest today). Running on a 4090 FE and 48gb of DDR4 ram on WSL2. Basically you have 3 knobs you can tune for max

Yes, it sucks to waste time waiting on 16K+ reasoning tokens alone. But here's the thing, this is only a 27B model trying to perform on par with 1T+ parameter models. Something has to be sacrificed, a

So many comments begging for these models, and I get it. People from the labs frequent r/LocalLLaMA, so maybe the begging comments aren't useless. They make demand known at least. Same with polls, etc

Artificial analysis index scores Qwen 3.5 27b: 35 Qwen 3.6 27b: 38 Qwen 3.8 27b: 52 What the hell kind of a jump was that? Even if it is benchmaxxed, the jump is insane. Qwen3.6 35b A3b: 32 That's ~6

Sorry for the slop, but I was impressed by this model as I have been testing Qwen3.8-27B Q8_0 locally on my ROG Flow Z13 (Ryzen AI Max+ 395, 128 GB unified memory) and this model was the only one who

Overview EVIE-Preview-4.5B is a state-of-the-art multilingual Visual Document Retrieval (VDR) model built upon Qwen3.5-4B. It employs ColBERT-style late interaction with native 128-dimensional multi-v

Yes it uses a ton more reasoning tokens than 3.6 did But test in on the same tasks with the other chinese models, glm 5.3, deepseek v4 flash and pro, etc it's really similar, and they are needed The r

qwen3.8 27B has seriously impressive benchmarks on its model card, but that's for the unquantised version. Almost everyone here will run one quant or another. Are there good benchmarks for how those q

Just curious - I like to be able to see and manage the scripts my agent is working on. I find stuff like Claude Code and Open Code useful for doing stuff on my linux box but I don't understand why peo

I've got a dedicated AI rig sitting here with a RTX 5090 and 96GB RAM and for the past few years have been using Windows 11 and primarily LM Studio, but have also used vLLM, llama.cpp and Ollama. With

Title pretty much says it all. I’ve deployed Qwen 3.8 27B using vLLM on an RTX 6000 Pro (tried multiple vLLM releases and launch recipes), but I can't get it into a usable state because of crazy long

Including the rationalisation for the data below - this is a more robust version of an earlier post I did similar to this - explaining below: How I chose the comparisons The basic question I’m trying

Running a 12700k + 5060 Ti 16 gb with 128 gb DDR4 ram and I'm wondering what's the ideal setup to maximize performance. I did some preliminary stuff but I have to admit, I'm still learning and kinda j

I was surprised by the fact that the qwen 3.8 27b download count is about 1 million (globally). This means that even on this subreddit, very few people have used 27b. At most 50k–100k active users, an

At hugging face, this is the model I use for most of my analysis that most of my services have to not be refused. previous versions are quite good. It seems this might have dropped today and pulling r

I'm just thinking, ever since microsoft announced bitnet, this sub (and myself) has been hoping for massive ternary models. In the last month alone, prismML dropped 27B ternary (though I've read commu

Single prompt 'make me a 3d scene of an anime girl' (stolen from someone on discord, I like it a lot since it seems like every model struggles a lot with it) Everything is one shot. No vision, no iter

With all the new models released recently one important upgrade went unnoticed: Llama.cpp bumped ROCm from 7.2 to 7.14. I was waiting for that because in 7.14 support for gfx1103 (Radeon 780m) was int

Hey! Like a lot of people here with consumer GPUs (RTX 4060 8GB in my case), I wanted to see if I could use local models for daily coding tasks instead of burning cloud credits on simple boilerplate.

In this commits, the 35B model was removed. Looks like it's confirming the 35B model won't get released. I think they need to be made aware how big the 35 moe is widely used. Think need to make noise

If you are using llama-server with their web-ui for testing, keep in mind, that the reasoning selector is just a reasoning budget aka a hard cap and has, at least to my knowledge, nothing at all to do

https://developer.nvidia.com/blog/serve-qwen3-8-2-4t-a95b-a-2-4t-parameter-model-with-configurable-reasoning-on-nvidia-gb300-nvl72/ 4k tokens per second per GPU of which there are 72. 350 tokens per s

https://preview.redd.it/wkg27e152qjh1.png?width=853&format=png&auto=webp&s=2e3f8b11ea6393041f501e95c5835f9bea0245dd So ive been trying different harnesses and coding agents with the new qwen 3.8 , and

Hi, After a long night of optimizations, I believe I have made the fastest inference engine for Qwen3.6-28B on a 3090. Quick metrics: - 250w power capped - Up to 195k context (ships with 150k for safe

Did a quick local test because I wanted to see what is actually usable on my 12GB laptop GPU. I tested the newer Qwen3.8 27B dense files at Q2 and Q3, then compared them against Qwen3.6 35B-A3B MoE. H