
NVIDIA DGX Spark Setup and Usage Guide for 2026
A complete guide to setting up the NVIDIA DGX Spark - from unboxing and first boot to running LLM inference, fine-tuning models, and optimizing performance.
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A complete guide to setting up the NVIDIA DGX Spark - from unboxing and first boot to running LLM inference, fine-tuning models, and optimizing performance.

Awni Hannun, the Stanford-trained researcher who co-created Apple's MLX machine learning framework, announced his departure from Apple. His exit is the latest in a devastating exodus of AI talent that has hollowed out Apple's ML research bench over the past year.

LM Studio 0.4.5 introduces LM Link, built on Tailscale's tsnet library, letting users access local AI models on remote hardware through end-to-end encrypted connections with zero port forwarding.

MIT spinoff Liquid AI releases LFM2-24B-A2B, a hybrid mixture-of-experts model that activates only 2.3B parameters per token, fits in 32GB RAM, and hits 112 tokens per second on a consumer CPU.

A comprehensive guide to the best image generation models that run locally on consumer GPUs with 16GB of VRAM, from FLUX and Stable Diffusion to video generation and upscaling.

LLMfit is a Rust-based terminal tool that scans your hardware and scores 157 LLMs across 30 providers for compatibility, speed, and quality. Here is why it matters.

Georgi Gerganov's ggml.ai joins Hugging Face, bringing the most important local inference project under the $13.5 billion AI platform's umbrella.

Georgi Gerganov and the ggml.ai team behind llama.cpp are joining Hugging Face. The deal unifies model hosting, model definition, and local inference under one open-source roof.

A viral tweet exposes an uncomfortable pattern in the local LLM community: endless hardware purchases, near-zero shipped products. The data backs it up.

Rankings of the best open source LLMs you can run on home hardware - RTX 4090, RTX 3090, Apple M3/M4 Max - organized by VRAM tier with real-world token/s benchmarks and quality scores.

Compare the best tools for running large language models locally: Ollama, LM Studio, llama.cpp, GPT4All, and LocalAI. Includes hardware requirements and model recommendations.

A practical tutorial on running open-source language models locally using Ollama, llama.cpp, and LM Studio, with hardware requirements and model recommendations.