Top open-source LLMs in 2026 ranked by capability + the exact hardware you need to run each locally. Llama 3 405B, Qwen 2.5 72B, DeepSeek V3, Mistral Large 2.
Nvidia DGX Spark, the shipping version of Project DIGITS: GB10 chip, 128 GB unified memory and 273 GB/s bandwidth. What size of model it holds, why memory speed limits it, the launch price and who should buy one.
Galaxy AI has been iterating publicly since 2024 — and it shows. The S26 Ultra ships the broadest AI feature set on any phone in 2026. Here’s every feature, tested.
When local GPUs aren’t enough, which cloud do you actually rent from in 2026? Real prices, real availability, and the right provider for each use case.
$500 isn’t enough for the AI hardware Reddit recommends. Here’s what you can actually do with a budget GPU in 2026 — and which one to buy.
AMD finally shipped a chip with massive unified memory in 2026 — 128 GB vs Apple M4 Pro’s 48 GB. But Apple’s per-chip efficiency still matters. Which wins for AI?
Three years into AMD’s push, ROCm 6.3 on the 7900 XTX is finally usable for serious AI. But CUDA isn’t standing still — here’s where each ecosystem actually wins in 2026.
The complete VRAM cheat sheet for every major open LLM in 2026 — at every common quantization level — plus a matrix showing which models fit on 12, 16, 24, 32, 48, and 80 GB GPUs.
The RTX 5090 brings 32 GB of GDDR7 and ~30% more AI throughput than the 4090 — but is it the right buy for image generation, local LLMs, or fine-tuning? Real benchmarks, real verdict.
Apple Intelligence on the A19 Pro versus Galaxy AI on the Snapdragon 8 Gen 4. Which phone does more AI on-device, and which one keeps your data more private?
Llama 3 8B runs surprisingly well on 2026 flagship Android phones — at usable speed, offline, with no API costs. Here’s exactly how to set it up on a Snapdragon 8 Gen 4 device.
Six laptops you can actually buy for serious ML/AI work in 2026 — ranked by real-world performance, sustained thermals, and how long they’ll stay relevant. We picked one overall winner.
