Search: “hardware”

The NVIDIA Tesla K80 is a legacy GPU that can run modern LLMs through Ollama with community-maintained patches, since official Ollama dropped support for CUDA Compute Capability 3.7 hardware. Each K80

GPU Compass is an open-source tool that tracks and displays real-time GPU pricing information across over 20 cloud providers. The platform likely helps machine learning practitioners and researchers c

Ernie Image Turbo is a text-to-image generation model that can run efficiently on consumer-grade hardware like RTX 30 series GPUs with limited VRAM by using INT8 quantization. The post discusses techn

This r/ollama post addresses a commonly reported behavior where Ollama's GPU usage remains at or near 100% even after a model has finished generating a response. Certain models appear to 'hang' after

Ollama consistently fails to run any model on Apple M5 hardware running macOS 26, with the Metal backend failing to initialize and the runner process terminating with a 500 Internal Server Error. The

This Reddit post from the r/ollama community discusses running local AI/LLM workloads using the NVIDIA RTX PRO 4000 Blackwell GPU via Ollama, a framework for running large language models locally. The

A Reddit post on r/StableDiffusion announces an in-person open source AI art hackathon held in Paris, co-organized with LTX (Lightricks' open source AI video model) and NVIDIA. The event likely invite

This Reddit post shares a Google Colab notebook that enables free AI voice cloning using Alibaba's Qwen3-TTS, which offers voice cloning, voice design, and ultra-high-quality human-like speech generat

This Reddit thread on r/ollama discusses whether the NVIDIA DGX Spark — powered by the GB10 Grace Blackwell Superchip and delivering 1 petaFLOP of performance — is a worthwhile investment for running

TurboOCR is a high-performance OCR project that combines PaddleOCR with NVIDIA TensorRT, implemented in C++ and CUDA, achieving throughput of 270–1,200 images per second using FP16 half-precision infe

A Reddit discussion from the r/ollama community where a user seeks advice on which AI language model to run locally using Ollama. Responses likely include hardware-based recommendations (such as RAM a

I'm trying to figure out the best long-term upgrade path for my setup. Right now my GPUs are spread across several consumer PCs: Main: 5900X, 32GB DDR4, 2× RTX 3090 24GB. Secondary: i3-10320, 16GB DDR

For what it's worth. I'm a mechanical engineer by education and software engineer in practice over the past ~20 years. Playing around with local models has been a recent hobby. Today I decided to do a

Spent the past month tweaking and experimenting with many different numbers to achieve 30tps. Hardware: -Rx6700xt 12gb vram (AMD) -2x16 ddr4 3200 ram -r5 5600x -llama.cpp vulkan sdk -window11 (no wsl

Hello all! Thank you to everyone that contributed their feedback and notes for LlamAmpere the last time I posted. I've continued to chip away at improvements for the 3090 crowd -- this release is mode

SlopSoup TV is a live, never-ending pixel-art TV network. Every script, character, voice, camera cut and schedule decision is made by models. It's made to be very absurd, dark and strange. Hardware: o

TL;DR: For my use case, mradermacher/Signal-3.8-27B-Terse-Coder.i1-Q4_K_M proved out. Ymmv based on your domain-specific tests. Time taken to solve problems compared to frontier models is massive; esp

Workflow & Hardware The workflow was originally based on Pixaroma’s ComfyUI workflow. I made some modifications and added Sol Attn optimization and Comfy Kitchen Sink, including the very useful Model

Follow-up to my post from about two weeks ago. A lot has changed since then, so I wanted to post an update on where the backend is now. Where the green architectures stand When I posted last time, 7 a

I’m building a Polish General purpose legal Model that drafts documents, answers questions using legal sources, and has enough coding ability to handle some automation. The workflow is very tool-heavy

Hello everyone! A little while back I posted about LlamAmpere, a fork of Llama.cpp with Ampere-specific improvements (though it is caught up to main and will support other hardware, too). Thank you to

TL;DR: The Jeff models are a set of Qwen3.5 and Gemma fine-tunes for zero-shot classification: small, efficient, open-weight models with respectable out-of-the-box performance that can be slotted righ

I know Qwen is a key player in the local LLM space and has consistently introduced truly impactful technologies—like n-gram in Qwen-Next and the recent Qwen 3.8 27B, which is an amazing local model. H

Hardware RTX 5090 (32 GB) + RTX 4070 Ti Super (16 GB) = 48 GB VRAM 32 GB DDR5 6200 RAM Arch Linux, KDE on the 5090 (takes ~1-1.5 GB VRAM) daily driver OrcaRouter Qwen3.8-27B Uncensored, block-FP8 (28.

Can LLM agents actually get through a day in the life of a normal user? That question got me reading papers on Android agents and mobile benchmarks over the past few months. A few patterns kept showin

Hi, human here with rambling thoughts to share. Feel free to skip Overall, I don’t feel like I’m missing much; if anything. On my hardware(M1 ultra w 128Gb) it’s probably not as fast as Claude but I’v

Hey everyone, I’m new to running AI models locally and I’d like some advice. My laptop specs are: HP OMEN 15 CPU: Intel i5-8300H RAM: 16 GB GPU: NVIDIA GTX 1050 Ti 4 GB VRAM OS: Windows I’m planning t

Hey everyone, After wrestling with hardware and engine issues for days, I finally got Qwen 3.8 Next running properly on my multi-GPU rig. Thought I’d share the setup journey, benchmarks, and thermal r

RTX 5090 random black screen + 100% fans + hard lock; sometimes no POST until AC power is removed — gaming, ComfyUI and Ollama I am looking for help diagnosing a very intermittent but increasingly fre

Hello, I know some of us may be tempted to rent out our expensive GPUs to recoup some of the cost of self-hosting, and it should be obvious that this can be a risky decision. I decided to try hosting

Another question asked 1 thousand time, but I can't find actual threads. Sorry in case of mistake. Due to interstellar price of hardware like high-end GPU (more than 5k euros for a 5090 and similar pr

I reallise this is not exactly a locally hostable model, at least definitly not on my hardware. But I figured some of you might have some insight on this. I run an agentic coding benchmark. The model

I'm currently using Qwen3.5 4B as the brain of my local AI assistant because my hardware is relatively limited. One thing I really like about it is the speed. On my system, I'm getting around 40–50 to

The full K2 Horizon lineup is out on Artificial Analysis. The AA intelligence vs. parameters plots show that - 0.9B and 375B are bad - 3.7B and 7B are SOTA - 36B A4B is SOTA for hardware with poor mem

Now that the dust has settled a bit - here's a write-up on running Qwen3.8-Flash-Next (125B-A6B MoE + 51B n-gram table) on relatively middle-tier hardware (RTX 4070 12GB + 64GB DDR5-5600 + Gen4 NVMe o

I'm a teacher looking into running an LLM locally. I've been using ChatGPT for a lot of this work, so I started looking at Ollama because I understand it's designed to run LLMs locally. That said, I'm

Another new model dropped in the course of this week that is well deployable on consumer hardware: Nex N2.5 Mini I went with the recommended settings for the best generation quality and ran a few benc

I wish I could spend $15k on my own homelab hardware, but no this is for work lol. Like the title says, we're looking to run DSV4 Flash (and similar tier models) locally at good speeds, both for token

Please, Oh gentle soul, please beautiful soul, please kindhearted soul, with serious hardware and time to kill, please benchmark Unsloth Qwen3.8-27B-Q8_K_XL against Qwen3.8-Next-Flash-Q4_K_XL and prov

https://huggingface.co/XHToken/Spark-X2.5-4B-GGUF https://huggingface.co/XHToken/Spark-X2.5-1.7B-GGUF from XHToken: We are introducing Spark-X2.5-4B and Spark-X2.5-1.7B, two compact, general-purpose l

I feel that reproducibility is now a lost cause in machine learning research for three reasons: Many research is moving towards the physical AI territory, where you need expensive hardwares or even en

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

ik_llama.cpp merged qwen4exp MTP support yesterday (PR #2369, mine, reviewed and tested by four other people on their own hardware). It's on main now, no fork or patch needed. Posting since the last c

I am absolutely blown away. Yes my setup is crap but the fact that it managed to do this in a single take is unbelievable (and I'm a developer). Hardware used: - Windows PC with 4070ti (12GB VRAM, 32G

specs hardware: M4 Max 128GB Studio inference engine: llama.cpp (qwen4exp branch) judge: claude-opus-4-6 AtomicChat/Qwen3.8-Flash-Next-GGUF Qwen3.8-Flash-Next is a great model I benched in my previous

This has been a big 2 weeks for local models with 3.8 27B and Next coming out, a lot of 'frontier labs are done' comments which, in general, could certainly be true. Could be trillions in economic val