Run AI on Your Own Hardware

Speridlabs Idol: Turn One Photo into a 3D Gaussian Splat, and How to Run It

Idol is an open image-to-3D model from Sperid Labs that turns a single photo into a 3D Gaussian splat, generating up to 1,048,576 Gaussians in under 2 seconds on a consumer GPU with 7.2 GB of VRAM. It runs locally through the idol-sdk Python package under Apache 2.0.

LFM3-Omni-2B: Liquid AI's 2.2B Any-to-Any Model, and How to Run It Locally

LFM3-Omni-2B is Liquid AI's 2.17 billion parameter any-to-any model: one checkpoint that reads text, images, video and audio, and answers in text or 24 kHz speech. Built for phones and laptops, it ships as a BF16 checkpoint and a GGUF build as small as 1.05 GB, and runs through llama.cpp, Ollama or a browser WebGPU demo.

SenseNova-Loop-9B-Preview: A Looped Multimodal Model for Long-Context Work, and How to Run It

SenseNova-Loop-9B-Preview is a 9.66 billion parameter multimodal model that accepts text and images and generates text. Its unusual design runs 32 distinct decoder layers through 56 layer-executions per forward pass by looping a shared block of weights twice, giving it deeper computation without a proportional increase in stored parameters.

How to Run WeVisOne-VL-4B Locally: Tencent's All-in-One Vision Model

WeVisOne-VL-4B is a 4.83 billion parameter vision-language model from Tencent's WeChat Vision team, built on Qwen3-VL, released under Apache 2.0. One model handles object detection, referring localization, counting, pointing, OCR, segmentation and promptable alpha matting, and it runs locally through a pip-installable package with either a transformers or vLLM backend.

How to Run Mistral Large 4 (1T-A52B) Locally: The Hardware It Actually Needs

Mistral Large 4 is Mistral AI's largest release yet: a 1 trillion parameter multimodal mixture-of-experts model with 49 billion active parameters per token, a 1 million token context window, and FP8 weights split across 54 safetensors shards. Mistral's own vLLM example asks for 8 GPUs, so this is a datacenter model, not something a single workstation can load.

How to Run Hy-SimulMT-30B-A3B-FP8 Locally: Tencent's Real-Time Speech Translator

Hy-SimulMT-30B-A3B-FP8 is Tencent Hunyuan's 30.39 billion parameter model for real-time Chinese-English speech translation, with an FP8-quantized mixture-of-experts core that cuts the weight footprint roughly in half next to the BF16 release. It runs through Tencent's own SimulInfer server on an NVIDIA GPU with vLLM 0.31 or newer, and streams translated text and cloned speech while the speaker is still talking.

How to Run Chroma1-HD Locally: An Open Base Model for Text-to-Image

Chroma1-HD is an 8.9 billion parameter text-to-image model built on FLUX.1-schnell and released under a fully permissive Apache 2.0 license. It runs through Hugging Face Diffusers on an NVIDIA GPU, and its real selling point is that it's meant as a clean, unrestricted base for finetuning rather than a finished chatbot-style product.

How to Run Nemotron 3.5 Parse Locally: NVIDIA's 630M Document Parsing Model

Nemotron 3.5 Parse is NVIDIA's 630.8 million parameter vision-language model built to turn document images into structured text with layout classes, bounding boxes, and reading order. It runs through Hugging Face Transformers or vLLM, needs roughly 1.3 GB of bfloat16 weights, and hits 15.42 pages per second in NVIDIA's single-pass benchmark on an H100.