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https://preview.redd.it/i8rjx0ar5mlh1.png?width=2160&format=png&auto=webp&s=c2588bc7b2519ea71b176ca73faf566dfc585496 I wanted to see whether a heavily quantized 27B model running entirely on an older

I am aware that I may not be able to run some higher end models, but I’m starting my journey into Ollama to learn capabilities and limits of local hosting. What could I expect to run on a Lenovo Think

Hello, I've been self-hosting LLMs on various budget hardware for a while (6x RTX 3060 12 GB, Intel Arc Pro B60 24 GB, RX 9070 XT, etc). Over the last few months, I wrote about it in 4 articles: Gener

Benchmarked qwen3.8 xhigh, medium and muse glimmer. Xhigh effort mode with qwen3.8 took almost 30hrs. (And still failed on 16 cases because of the 32K output token limit) Medium effort mode and muse g

we made vision mlx quants of qwen3.8 27b (9 builds from 8bit at 29.5 GB down to 3.23bpw DWQ at 11.8 GB) and compared them against other community vision mlx quants from hf (we only compared vision bui

ollama ps settles this in one line, and almost nobody runs it before changing their quant. The PROCESSOR column reports exactly one of three things: 100% GPU - every layer on the GPU 100% CPU - the GP

I ran 11 uncensored variants of Gemma 4 12B that I grabbed from huggingface, sorting by downloads. 10 full abliterations plus 2 LoRA adapters which were requested to be added in the comparison, agains

With hopes of a Qwen3.8-35B-A3B release now mostly dashed, many people including myself are looking at fine-tunes and other variants of Qwen3.6-35B-A3B to run on VRAM-limited hardware. I decided to tr

.. didn't llama.cpp aka ggml get acquired by Huggingface not too long ago? How would this sale affect llama.cpp and ggml? What are possible risks, and did ggerganov share his opinions or potential nex

'A 12-core GPU, also with two more cores than before, now includes Neural Accelerators in each core for the first time on Mac mini, resulting in up to 4x faster AI performance and 2x faster graphics t

TL:DR: power management was doing auto-suspend and dumping model weights into RAM. Models weights 18gb+ RAM 16gb = no Bueno immediate crash. amdgpu.runpm=0 in your grub disables runtime power manageme

One of the things I like most about Ollama is how low it makes the barrier to experimenting with local models. The downside is that once you move away from high-end hardware, finding the right informa

An RLM agent microharness that 'persistently' thinks continuously. What could go wrong :) Introducing Headlong, an open source microharness for persistent agents: self-guided agents that think continu

We are seeing every so often people mentioning using Kiwix or offline Wikipedia for training. It is not something Kiwix is actively involved in (as in, people found us rather than the other way around

Hello everyone. I hope is all well with you all. I been around this sub for a few months and been quietly reading and I have seen how many of you are spending $10k on the Mac M5 and I am assuming you

Granite-4.2-30B is the flagship reasoning model in the Granite 4.2 family. It delivers the strongest performance across reasoning-intensive tasks by leveraging built-in <think>...</think> chain-of-tho

Building a local AI agent hierarchy on a budget — RAG, quantization, and testing whether fact-grounding pushes small models toward real creativity I don't have a CS background, a GPU, or much of a bud

Hi, since ollama provide multiple cloud models i was wondering is there an app that could be used for chat like claude already has but you can switch models? Or if that is complicated to build at leas

I made a Discord for people who are genuinely into AI and want a decent place to talk about it. It’s still new, but the idea is to build a large community without arbitrary bans, hidden moderation dec

Krea 2 Turbo — 4-Step Distillation LoRA (work in progress) A LoRA for Krea 2 Turbo that reduces the minimum usable step count from 8 to 4. This is an update release, following up from my previous post

Hi, I’m having fun using “huihui_ai/Qwen3.6-abliterated:35b-Claude-4.7” for pen testing. It’s uncensored so it does whatever I want. I’m on M4 Pro 14 core 64GB. Just wondering if there are some models

Hey everyone, I'm trying to set up a local LLM on my machine. My goal is to use it with Qwen Code or OpenCode to help me out with small to medium sized tasks, but nothing too big. I tested many differ

I’ve been going back and forth on this for a week and I can’t settle it, so I’m hoping someone here has hands-on numbers. The two configs (German prices, dealer quote, incl. VAT): Config Price Mac Stu

At $10k, you could get - 6.2B tokens with Qwen 3.8 Max (Qwen Pro plan) - 5.7B tokens with DeepSeek V4 Pro OpenRouter - 100B tokens with DeepSeek V4 Flash OpenRouter As a firm believer of local inferen

For some reason I can only use Ollama through home wi-fi, everytime I try to use it while using my phone as a modem I get an error. Vpn doesn't help. Edit: problem fixed, works perfectly if VPN is in

We have been working on some performance optimisations for Qwen3.8 and other models. The main new feature that we introduced is adaptive speculation for Llama.cpp What is it? MTP and DFlash work well

Pretty simple, I tried almost anything I could find with Q4K_M and it refused any harmful test prompt I could find, does anyone know a decent versione/how fo make It work? submitted by /u/circumcised_

Portable rig for Qwen3.8-27B-BF16 200K+ token prompts. My work Panasonic Toughbook + the T1 + power brick + headphones all fit in my lunchbox. Need the BF16 for huge context highly sensitive document

good morning, has anyone already used qwen 3.8 with the tesla v100 or sxm2 or pcie? any information is useful to me context token/s thanks for the help. submitted by /u/Full-Chicken2110 [link] [commen

My best bet is the embedding-offloaded linear where the 51b n-grams track semantics and context, like 3.8 with its loss of real world knowledge bolted back on. Otherwise? Sparse full-attention with de

TL;DR - A ~4M example, ~3B token CoT dataset designed around helping small models think more concisely, accurately and reliably. Hi all! For the past few months I have been working on a dataset design

v4.5.0: Added native desktop control, agent execution budgets, concurrency limits, loop protection, and safer local embedding fallback. v4.6.0: Rebuilt agents around durable parent-led orchestration.

I’ve been experimenting with a custom inference/runtime research project called CRANE V2, mostly because I wanted to answer a stupid question: How far can you push absurdly large MoE models on an ordi

I am not a huge fan of AI, but my friends like to joke and say I am torturing computers because I use all sorts of virtual machines, commands, and once split in half a 1000 defective laptop (accident)

WeMM-Embedding-9B is a universal multimodal embedding model built on Qwen3.5. It accepts text, images, videos, visual documents, and interleaved multimodal inputs, and returns a 4,096-dimensional L2-n

Upgraded ollama from .32.6 to .32.15 ran a test on my qwen3:30b and it went from 90+ t/s down to 45+t/s System has 4 and v620 pro and one rtx3090 Before it was only using the amd, now it's using all 5

Source of Claims: https://x.com/Andy_ShuoYang/status/2090856976880472439 Your gaming PC can now serve frontier models at interactive speed using official checkpoints without extreme quantization! Qwen

Now, some of you might not believe me from the get go, and it's completely fine, But for those who are willing to give Tap a chance, a whole new world of voice dictation is going to unlock for you. Ta

https://preview.redd.it/usib0obv1blh1.png?width=1171&format=png&auto=webp&s=fa7b6e9155fcc461a2d397f9cf75aece4c72235c https://preview.redd.it/w453mok02blh1.png?width=2613&format=png&auto=webp&s=693182d

after 3 months and 800 burned... Unbounded Labs is proud to introduce Bart, our vintage LLM: 2.82B parameters trained from scratch on 20.1B tokens of English written before 1931. You can talk to it ri

after 3 months and 800 burned... Unbounded Labs is proud to introduce Bart, our vintage LLM: 2.82B parameters trained from scratch on 20.1B tokens of English written before 1931. You can talk to it ri

My test is running DeepSeek v4 Flash 0731 at native size on DGX Spark machine (GB10, 128 GB unified memory). The model size is bigger than RAM, so weights will be loaded many times when running. To im

Graph: A cumulative metric of Speed x Context Size - the higher the dot - the better. Helpful for looking for the most balanced solution. The research relies on DFlash2 Q2 work by AnalogAlok: https://

Share what your favorite models are right now and why. Given the nature of the beast in evaluating VLMs (untrustworthiness of benchmarks, immature tooling, intrinsic stochasticity), please be as detai

Hi everybody! Every now and then these days, we’re seeing really huge open-weight models popping up. But since not everybody has a DGX Station at home, I’m interested in really small models. It’s incr

I just finished building my (relatively) low rent local inference machine: * Epyc 7663 * 256GB ECC DDR4-3200 * 1x RTX 5090 32GB Yeah I realize it's weird to throw a 5090 and 256GB of anything together