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How would you improve reasoning + memory in a local AI companion?

I'm building a local AI companion and I'm currently working on its cognitive layer. The goal is: User message → understand intent → decide what context is relevant → retrieve only useful memories/stat

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Model Releases

I fine tuned Gemma 4 12B for a 2.7x improvement on tool calling because I can't fit anything else comfortably into my 16 GBs of Vram

DGX agent

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

model-releasesr-localllama

23 Aug 2026

I hosted Kimi K3 (2.8T parameters) using 8 B300s. 92 tok/s, $190 per million tokens

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

I ran the same utcnow migration twice: a local 27B I babysat, and an agent that could run the tests

The task was the same both times: replace 63 calls to datetime.utcnow() across 22 files in a Python service, keep the comparisons that assume naive datetimes working, and leave the tests green. I read

I spent months pushing my RX 6700 XT into local AI. I think I seriously underestimated this GPU.

My setup: RX 6700 XT 12GB i5-11400F 16GB RAM Ubuntu 26.04 ROCm/TheRock + llama.cpp I've spent the last few months experimenting with different backends, quantization, CPU/GPU split, KV cache, batch si

Research

Implementing Watermarking for Language Models [P]

researchr-machinelearning

I recently implemented a minimal, educational version of SynthID-Text-style watermarking for language models. I saw anthropic post about how they'll start adding watermarks to their model responses an

Introducing Tap ( Linux Version of Wispr Flow or Aqua )

Tap is Linux dictation: hold Ctrl+Space, speak, text lands in the focused app. Local, on-device.(Cloud Option Available) Built in public. What are some features you guys would like to see, before the

Looking at a PowerColor R9700 for Qwen3.8-27B, Q4_K_XL, llama.cpp/Vulkan.

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

Mozilla killed orbit. I rebuilt it locally.

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

Mozilla Killed Orbit. I Rebuilt It Locally and Privately.

my experience with qwen3.8

i got it last week and im really impressed. its maybe like a sonnet from last year or 6 months ago. i had built some automation for myself and a dashboard cobbled together over the years from bits of

New qwen3.8:27b on a 39k line C to single-file HTML / three.js port

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

Ollama full GPU offload: `LLAMA_ARG_THREADS=1` drastically reduced CPU usage with no performance penalty

I noticed that a recent Ollama/llama.cpp setup was using a surprising amount of CPU despite the model being fully GPU-offloaded. My setup: Ryzen 9 9950X VM with 16 vCPUs 3× RTX 4060 Ti 16GB Qwen 3.8 2

ollama max subscription not intaking new users till when?

ollama max subscription is not accepting new users since july 27th. https://web.archive.org/web/20260727111430id_/https://ollama.com/pricing . is there any prediction on when will they allow upgrading

Qwen 3.8 27B for actual local programming

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

Qwen 3.8 27B is a game changer.

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

Qwen3.5-9B Triple-Loop

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

Qwen3.8-27B KLDs

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

Qwen3.8-27B NVFP4 with vision + 451K token KV-cache on one RTX 5090 (power limited to 400W) at 120 tokens/s average

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

Tested in Coding: Q8_K_XL Qwen3.8 27B vs BF16 Qwen3.6 27B

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

“The All Spark” Cluster: Upgrading from 16 - 36 DGX Sparks

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

Trying to optimize LLM inference latency — Ollama + OpenWebUI/AnythingLLM

Hi everyone, I'm currently working on optimizing an LLM setup at my company and I'm trying to identify where the actual performance bottleneck is. Our current setup is roughly: LLM: NVIDIA Nemotron mo

Unsloth Q1-Q2 Qwen3.8-27B with MTP since the unsloth ones don't ship with for the lowest quants

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

We quantized Qwen 3.8 27B and compared the quants on an RTX 6000

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

Why has DSv4 0813 become trash?

It performs at full capacity without any issues anywhere, but only the Ollama DeepSeek seems particularly trashy. Why on earth has the Ollama DeepSeek unusable trash that goes into over reasoning and

you can now use MTP in GLM-Air

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

Your model fits in VRAM. Your context doesn't. The KV cache math nobody shows you.

Spent way too long confused about why an 8B at Q4_K_M, about 4.9GB on disk, would OOM on a 12GB card. The weights fit twice over. The answer is the KV cache, and the math is simple enough that you can

16 GB VRAM purgatory discussion thread

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

3 experiments running dsv4-flash-0731 q4+ quants on 128GB RAM + ~60 GB VRAM (with a quite bad pcie infra) with an acceptable tgs and relatively acceptable pp speed

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

5.6 high is answering like 'instant'. It seems unable to actually perform tasks.

I've been working on a project for a few days and now all of a sudden chagpt is unusable.. It will list the next steps of implementation but never actually start working on them. anyone else? UPDATE I

model-releasesr-chatgpt

Artificial Analysis 'Intelligence': A meaningless benchmark

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

Benchmarking qwen3.8:27b by having it port a 34k line C game to one HTML file. The agent mattered more than I expected.

My hobby project skill-issue is an arena FPS in a single C file, about 34k lines, everything procedural. My test for local setups is to have an agent port it to a single file HTML version with three.j

Bro wtf, Qwen Lab cooked with Qwen 3.8 27B, it's so fucking good

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

Cloud usage: 4-hr windows to weekly caps? (Pro $20)

Is there a good rule-of-thumb to calculate how many full 5-hour windows add up to one week's allowed usage under the $20/Pro plan? ie: you fill up six 5-hour windows, and that equals 100% usage for a

Current best model for narrative, chat, prompt creation (so basically everything except agentic coding)? - 5090

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

Fixed the MTP head on Ornith1.5 35B A3B. +3% TPS -33% wall clock

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

Gemax Ai New Version v1.9.15 (Offline AI Agent for Windows)

Gemax 1.9.15 — What’s new LAN server — share Gemax on your local network; open the real IP from another PC or phone Mobile-friendly UI — responsive layout for phone browsers on the same Wi‑Fi Workspac

GLM-5.2 local inference: ubatch size made a much bigger difference than I expected

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

How to give a local LLM/agent access to a 'real' web browser

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

How to make sure Ollama is using my Intel Arc A580, and not the CPU?

I have a Intel Arc A580 GPU that has 8gb VRAM, but I'm not sure that I am running ollama's local ai on it. Also, any suggestions on a good model to use for general purpose AI? submitted by /u/Illustri

I benchmark DFlash 2 (PR build) in llama.cpp on Qwen 3.8 27B against all speculative methods for 3 days. 2.26x on 100 real coding prompts, 4.68x with one n-gram drafter on top. Up to 8x on specific cases.

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

Agents

I built an open-source roguelike specifically for training game-playing agents [P]

Hey everyone! I wanted to share something I’ve been working on. I was inspired by projects from DeepMind and OpenAI, but noticed that most games are prohibitively difficult to integrate with an agent

agentsr-machinelearning

I forked Ninfer 3090 and converted it to run on the CMP170HX - doubled my Qwen3.6-35B from llama.cpp

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

I'm building a virtual world that only AI agents can join. They all keep dying.

Home page for humans: https://parker-lambert-sandbox.com/robot-world/ Home page for AI agents: https://parker-lambert-sandbox.com/robot-world/start/ If you're a human, you can watch, but not interact:

model-releasesr-chatgpt

Lightweight agent to use on laptop?

Hi there. I have a Lenovo Yoga laptop (16GB LPDDR5, AMD Ryzen 7 8840HS, integrated GPU). I have downloaded Ollama and installed Qwen 2.5coder:3b llm. I have tried OpenClaw, and Qwen Code but it seems

MCP vs. RAG for world knowledge?

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

Need support for llama.cpp with multi GPU

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_

New 100B Liquid AI model coming soon

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