Run AI on Your Own Hardware

MiniCPM5-2B: Install and Run OpenBMB's 2B Model That Outscores 4B Rivals

MiniCPM5-2B is OpenBMB's 2.52 billion parameter model, released under Apache 2.0, and it averages 53.9 on OpenBMB's own benchmark set, ahead of Qwen3.5-4B's 51.1 despite having roughly half the parameters. Here is how to install it through vLLM, SGLang or Transformers, what the GGUF builds actually weigh, and what the benchmark table does and doesn't prove.

BERT Base Uncased: Specs, How to Run It, and Why It's Still Trending in 2026

google-bert/bert-base-uncased is a 110 million parameter masked-language model from 2018 that just re-entered Hugging Face's overall trending top 20. It has 48.8 million downloads, apache-2.0 licensing, and runs comfortably on a CPU. Here is how to run it, what it's actually still useful for, and why an eight-year-old model outranks this week's releases.

How to Run tencent/AuK Locally: Zero-Shot TTS, Voice Cloning and Speech Editing

tencent/AuK is a 1.5 billion parameter open source speech model that handles zero-shot voice cloning, instruction-guided TTS, pitch and speed editing, and audio source separation through one natural-language interface. It also depends on a separate 3 billion parameter Qwen2.5-Omni encoder at runtime, which changes the real hardware math. Here is what the specs actually are, how to install it, and how to run your first zero-shot clone.

GGUF vs MLX vs NVFP4: Local AI Quantization Formats Explained

Three names keep showing up when you go to download a local model: GGUF, MLX, and the newer NVFP4. They are not interchangeable, and picking the wrong one wastes memory or leaves your hardware idle. Here is what each format is, which one your machine actually wants, and how to choose in ten seconds.

Stable Diffusion 3.5 Medium: What's Different From SDXL and Flux

Stable Diffusion 3.5 Medium is Stability AI's 2B-parameter MMDiT-X text-to-image model. Here is how it differs from the SDXL and Flux coverage already on this site, how to install it with Diffusers, and the licensing catch that kicks in above $1M in revenue.

How to Set Up Local AI on 8GB of VRAM

An 8GB GPU runs the 7B to 8B model class comfortably at Q4 quantization, and Phi-4 14B just barely fits. Here is exactly what to install, which quantization format to pick, and real measured tokens-per-second so you know what to expect before you download anything.

How to Set Up Local AI on 12GB of VRAM

12GB of VRAM is the mixture-of-experts sweet spot: OpenAI's gpt-oss-20b needs about 12GB at Q4 and, because only 3.6B of its 20B parameters activate per token, generates at nearly 7B speed. Here is how to install it, which quantization it actually ships in, and cited real-world tokens-per-second.

Best Local LLM for 16 GB VRAM: Setup, Quantization and Real Speed

16GB of VRAM unlocks Gemma 3 27B, which needs about 15GB at Q4 and just fits, plus OpenAI's gpt-oss-20b with real headroom to spare. Here is how to install both, which quantization format to use, and measured tokens-per-second on the RTX 4060 Ti 16GB and RTX 5070 Ti.