Knowledge catalogue
I have deleted tons and tons of older models to make space since I can't afford storage anymore. Easily 10TB... Anyways, I have been considering deleting DeepSeekV3.2 but decide to run it one more tim
As per title. I'm not affiliated with the team behind this model in any way, shape or form. As a GPU poor myself (8 GB VRAM laptop + 12 GB VRAM desktop), I found Laguna to be very promising on my lapt
Reposting here after sharing this on [r/MachineLearning](r/MachineLearning) a few days ago, where it got a much better response than I expected (300+ upvotes, great questions, zero roasting) GitHub is
It worked pretty well, digging through and analyzing some local files. Claude code regularly stops at some point and fails to continue while DSH worked for 2 h, recognized that it could benefit from r
similar to video upscaler LLMs, what's the bleeding edge right now on something similar for audio music? the idea is you feed it in with a low bitrate MONO old soundtrack, and the LLM then recreates i
Sounds quite interesting, a big IDE provider optimizing for local AI with their coding harness. Especially that they picked Qwen3.6 over Qwen3.8 because of the thinking needs. Haven't read the full ar
MobileMoE is a family of on-device Mixture-of-Experts (MoE) language models with sub-billion active parameters, designed to push the quality–efficiency Pareto frontier for on-device LLMs, including th
An AQuA research loop let one LLM write an intraday trading feature and a second LLM review it for causality. The feature divided the volume seen so far by the day's final total volume. At prediction
ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning Large Language Models (LLMs) have demonstrated remarkable abilities in tackling a wide range of c
TL;DR: I'm planning to spend around 100 on cloud GPUs to benchmark Qwen3.8-27B with a focus on questions that actually matter when running it locally: different quant levels/providers, 8-bit vs 16-bit
If you’re trying to run local LLMs on a normal laptop, an older desktop, integrated graphics, limited VRAM, or simply the hardware you already own, r/LowEndLocalAI is meant for you. The idea is simple
For the GPU poor among us: I'm curious what results you're getting with low quants of Qwen 27B 3.8. My main inference hardware is limited (Mac mini M4 24 GB), but I'm getting great results with Unslot
I ran the Aider benchmark on Qwen 3.8 27B FP8 with FP8 KV cache 256K context vLLM. The score: 72.9 This matches Gemini 2.5 Pro from 2025-04-12 which also scored 72.9. Beats Claude Opus 4 from 2025-05-
I commented on another Qwen 3.8 27B post that I was frustrated getting anything to work. You all gave some great comments. I nuked openwebui and straightened out my llama.cpp docker config. 1 hour of
Ornith does really well. TielCoder (https://llm-bench.io/benchmarks/cmt7kp2zj002r01lcmpchvlko) might be even a bit better in coding. Will give it a try soon. Details of the comparison see here: https:
With vision enabled I am able to run at 150k context on a single RTX 3090 and the results are just amazing. I was even able to write a gmail plugin for DeepSeek harness with locally hosted Qwen 3.8 27
Thanks to Unsloth Dynamic 3.0 quants coming in slightly leaner and better preserved, I settled on Qwen 3.8 27B (`UD_Q4_K_XL`) at 100K context as my daily driver for Hermes Agent and OpenCode. On an RT
Hi, Back in late march I begun playing around with Qwen 3.6 27b and found like everyone else that it's notoriously good at tool calls, where every model I tried before just derailed after a few turns
Try it here: https://ocean.blackbeardlabs.dev/ Model: Qwen 3.8 27b Q8_X_KL Unsloth Hardware: 3 x RTX3090 Harness: DeepSeek Harness Prompt: /goal I want you to create a **JavaScript + Node.js WebGL pro
Qwen3.8-27B is amazing, but it’s slow. A stronger 35B-A3B Mixture of Experts-coder that can run and solve real codebase issues fast (even on constrained hardware) is a valuable addition to the arsenal
I'm curious what other people are using for local LLM coding / agentic coding with only 8 GB of VRAM. My current setup is: Intel Core i7-11800H RTX 3070 Laptop, 8 GB VRAM 32 GB DDR4 RAM openSUSE Tumbl
Given OpenRouter.ai was snapped up by Stripe, who do we think would go after the 'GitHib' of AI models? It is a big chunk of change they are looking ($13B). Apple may be a contender to give them a rea
A few weeks ago I shared Agent Quest, my open-source experiment that turns Claude Code and Codex sessions into heroes living inside a small 2D world. The original idea was mainly about making it easie
WARNING: I think I made a mistake with omlx, and its results below seem to be without any kind of acceleration. I am rerunning them with Lightning MTP and ANE support for prefill. Will update the post
I haven't had a lot of luck in the reliability of the output of any models so far for local coding use, and find myself relying on Claude's limited free sessions to assist with coding. My coding appli
This is something I've noticed. Back when GLM 5.2 and Kimi K3 launched, there was a media push on pushing how dangerous open source models are. We know why they were doing this; these open models are
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'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
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
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
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
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
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
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
Earlier this year I posted about building what at the time I believe was the first 16x DGX Spark Cluster. I’m now adding 20 more Sparks to the cluster in my homelab server rack, giving me 4.6TB of uni
https://huggingface.co/jojohai/Qwen3.8-27B-MTP-graft Tested on Vulkan, the grafting saves RAM compared to using an external file. What I don't guarantee however is the quality of answers. The model is
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
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
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
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
Context: Earlier, open-source large models like Mimo V2.5 Pro, DeepSeek V4 Pro (first version), and Kimi K2.5 used to struggle with this prompt, and Qwen 3.6 27B couldn't even render the globe properl
Im looking to set up a new local llm (probably on unsloth studio as that seemed to be doing pretty well last time I tested it). This one won't need to do agentic coding or app building or anything (no
I love the Ornith 35B local models, 1.0 has been running my HAM radio rig for me. I have a hackRF receiver and a 5 watt quansheng portable the both run headless through the PC. I tried out the new Orn
Has anyone else experimented with ubatch size when running GLM-5.2 locally? I was testing the 226 GiB GLM-5.2-UD-IQ2_XXS GGUF on 3x RTX PRO 6000 Blackwell GPUs and got a pretty interesting result. Wit
I can't seem to find a good answer to this, my Hermes agent has access to Firecrawl and some other web scrapers for content extraction, but anyone know of a way to let a local LLM drive a 'real' web b
Hey guys, Inco AI shipped DFlash 2 a few days ago with a drafter for Qwen 3.8 27B and a llama.cpp PR. I built the PR and ran it against plain decoding, MTP, the n-gram lookup drafters, and my July DFl
Good afternoon, everyone! I wanted to show the work I've been doing around porting Ninfer over to the CMP170HX (Github) So, first, I do want to call out the amazing work that Neroued, Sergiuszm and sp
As I’m navigating the best setup for using Qwen3.8 27b as both my main coder and my personal assistant, I want to hook it into Wiki & beyond but I don’t want to be reliant on an internet connection. W
Using llama.cpp I seem to be unable to get my to GPUs working tougether correclty, so I need help somehow. Setup: 96GB RAM, one Blackwell 5000 (48GB) and one 3090 (24GB). I am trying to run the UD-Q3_
Liquid AI currently possesses among the fastest LLM architectures around, and some of the best SLMs (in terms of utility IMO) around, so I'm very excited to see what a potential 100B LFM (3?) model wo
New/Old benchmark that provides a lot of answers for local LLM. I present to you a new test that I developed somewhat by accident: https://huggingface.co/cHunter789/Qwen3.8-27B-i1-IQ4_KS_KT-GGUF/tree/
Hi, I have been using opencode with openrouter for quite a while now. Having read the success stories of using Qwen3.8-27B, I thought of trying it too. But every attempt has been a failure for me so f