Search: “hardware”

I liked the Nvidia that focused on just GPUs for gaming, not on the Nvidia of today which seem want power consolidation. Modelscope is another platform for those that simply want to know an alternativ

I’ve been experimenting with neural rendering outside a game engine and built a native Windows video player around it. It prepares a neural-rendered version of a video, caches it, and lets you switch

It supports images and full videos, native resolution NR or DLSS Super Resolution upscaling by scale factor / target resolution, GPU optical-flow motion vectors, scene cut handling, and all DLSS 5 NR

Based on https://huggingface.co/hardware, the RTX 3090 is the second most used GPU by LLM enthusiasts. Because RTX 3090 has native INT8 tensors cores, it can provide better performance with INT8 W8A8.

Hello! I've just uploaded a recent checkpoint of my model trained from scratch: https://huggingface.co/piotr-ai/polanka_3.7b_exp_wip_260901 It was pre-trained, mid-trained, and fine-tuned on a single

According to this article Samsung has locked up the 70% of it's future ram production in contracts to companies like Microsoft, Google, and Nvidia. Everyone knows this is driving the ram price increas

Hi! The team who collaborated and built optimized open source Minimax H3 here, full details below: https://x.com/haoailab/status/2093391548289540596?s=20 https://reddit.com/link/1w0xkpb/video/zpjrbdb0

Generated about half locally on my 5070ti. But also bought cloud GPU time to speed up overall generation. Vibecoded a rolling cutter to chop the song into chunks and then feed it in as reference audio

Spent a while treating layer placement, KV format and llama.cpp itself as experimental variables. 159 logged experiments. Numbers first, caveats after. Hardware: AMD Ryzen AI MAX+ 395 (Strix Halo, 128

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

Qwen3.8-27B is finally here, and NInfer already has Day-0 support! Weights: https://huggingface.co/neroued/Qwen3.8-27B-NInfer Just update to the latest source and give it a try. On a single RTX 5090,

I've been building an open-source tool for diagnosing world models, the kind that predict future frames from a starting context and a sequence of actions. It compares a rollout against ground truth an

Heeeey all! I just completed some fun tests with Muse Glimmer, I thought I'd let you know. In fact, the summary below was written by Muse itself! I ran a 2× DGX Spark cluster and got Meta's day-old Mu

https://github.com/NVIDIA-NeMo/Switchyard Finally an open source LLM router. An alternative to openrouter fusion and Sakana Fugu. Doesn't look like it does exactly what Sakana Fugu does according to i

The past week I've been running DSv4 inference on my laptop by keeping everything RAM-resident except the MXFP4-experts (since expert pool is ~147GB and won't fit) TL;DR - read speed is the limiter mo

TL;DR: I am building a Windows-first local AI setup for people who want to try local LLMs without spending days choosing models, setting up Ollama, Docker, WSL, Open WebUI, agents, and tool permission

I am not a meteorologist, but I just read a very interesting article: https://arstechnica.com/science/2026/08/deepminds-hurricane-model-bought-forecasters-an-extra-day/ In a paper published on Thursda

Code and instructions available here: https://github.com/albertoZurini/echo-dot-2-playground Hello there! After a few days of experimenting I was able to get a completely local voice pipeline running

LabyrinthBench measures the thing that actually kills long agent runs — whether a model can still use what it learned twenty turns ago — deterministically, with no LLM judge, on your own hardware, wit

Setup: GPU: RTX 3090 24GB RAM: 32GB ComfyUI 0.30.0 PyTorch 2.13.0+cu130 CUDA 13.0 SageAttention enabled (sageattention-2.2.0+cu130torch2.10.0andhigher.post6-cp310-abi3-win_amd64) Spectrum node + Euler

I'm hoping that one of you guys has been working on an inference engine or has somehow found improvements to running DSV4F on RDNA4 multi-GPU setups. I am currently building a custom inference engine

I deployed K3 on 32 H100s at work a couple of weeks ago and then got annoyed that there was no way to poke at it on my own machine. So I wrote an inference engine for it in C99. Nothing clever going o

This post was not written by a clanker. Hey guys, I'm a comp sci major who wanted to introduce a cool project I built for quantizing models to ternary (1.58 bit) with as minimal of loss as possible, a

Hey everyone, I recently open-sourced a new Python package called ganfs (Generative Adversarial Network Feature Selection), and I wanted to share it with the community. The Problem: Selecting the best

Are there any benchmarks on these 4 bit quants, like how Artificial Analysis runs a slew of various benchmarks? If not, how can I run one (5x over for consistency) on them? I'm also very interested in

ML research is progressing at breakneck speed where frontier labs in both academia and industry have access to considerably large computes (GPUs). Where do small labs or independent researchers go in

Hi everyone! 👋 I built and trained the complete Transformer architecture from scratch using pure PyTorch (`torch.nn` primitives) based on the original 'Attention Is All You Need' paper. I trained the

Hey everyone, I have been looking into how people source compute for their Inference workloads (and in general). I wanted to understand some specific pain points here. If you've used online services l

I wanted to challenge myself and see how far I could push Krea 2 and LTX 2.3, so I decided to create a short cinematic action trailer. It ended up being one of the most enjoyable AI projects I've work

I've spent the last year or so playing around with open source MLX and GGUF models on iPhone hardware. Given the limitations in memory, GPU/CPU/ANE, and in turn the context window I've been trying to

Quantized Krea 2 Turbo checkpoints for ComfyUI, up to 2x faster and about a third smaller than the usual FP8 version — no calibration dataset, no quality cliff. How to use it (short version): clone th

I've been building an AI that learns to play the classic Snake game through reinforcement learning. The goal is to reach high scores while keeping training time as low as possible. The current version

This handbook covers the technical aspects of running large language models efficiently at scale, focusing on GPU optimization techniques including GPU internals, key-value (KV) cache management, batc

Nvidia Cosmos 3 is an open physical AI foundation model built on a mixture-of-transformers architecture that combines vision reasoning, world generation, and action prediction for reasoning, simulatio

Xiaomi achieved over 1,000 tokens per second output from a 1 trillion-parameter model using a single standard 8-GPU commodity node through extreme model-system codesign . The approach combines FP4 qua

The Mac mini M4 uses unified memory architecture where CPU and GPU share a single 24GB memory pool, while the RTX 5060 has dedicated VRAM. Mac mini M4 is preferred for large model inference (70B param

Cosmos3-Super-Image2Video is a 64B model for temporally coherent image-to-video generation . NVIDIA's Cosmos platform is designed to accelerate Physical AI development by enabling machines to understa

Cosmos3-Super-Text2Image is a 64 billion parameter omnimodal world model capable of generating high-quality images from text inputs as part of NVIDIA's Cosmos 3 foundation model platform. The model is

NVIDIA's Pixel Diffusion Decoder (PiD) is an open-source decoder that replaces VAE decoders in image generation pipelines without retraining, producing sharper fine details and textures through a lear

This post describes setting up OpenClaw with a self-hosted Qwen 3.6:35b language model backend running on an H100 GPU for approximately $1.60 per hour, eliminating API rate limits. The user shares the

Flux-genotype is a self-evolving AI kernel designed to run on CPU hardware using Ollama, featuring the capability to mutate and adapt its own architecture dynamically. This project demonstrates an app

Setting a GPU power limit using nvidia-smi reduces heat output by approximately 20% with only a 5-8% inference speed loss. Undervolting the GPU can reduce power consumption by 5-15% with zero performa

Ollama not using GPU is commonly diagnosed by running `ollama ps`—if it shows 100% CPU, Ollama isn't detecting the GPU. The issue typically has multiple causes, with common ones including driver probl

This post benchmarks three Qwen3.6 model variants against gpt-oss:120b when running on Apple Silicon hardware, evaluating their performance characteristics and practical usability. It documents both t

Ollama's MLX engine runs on NVIDIA GPUs via CUDA v13 on Windows and Linux. Users have reported that Ollama crashes on RTX 3060 with cuda_v13 in versions 0.13.0 through 0.15.6, while deleting the cuda_

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