Intel’s Arc Pro B70 has reportedly outrun NVIDIA’s RTX 5090D on a DeepSeek R1 inference workload while costing roughly a quarter as much, according to Wccftech.
NVIDIA has shared updates on Anthropic’s Claude models running on its Blackwell Ultra GPUs, with Microsoft Foundry availability signalling a wider cloud rollout for enterprise AI workloads.
Direct answers to the AI-hardware questions people actually ask: RTX 5080 vs 5090, CUDA vs ROCm, running LLMs locally, VRAM needs, Chinese models, best AI laptops and image generators.
The best GPUs for AI in 2026 compared: RTX 5090, 5080, 5070 Ti, RTX 4090, H100, H200, Mac Studio and AMD — specs, prices, VRAM and price-to-performance.
A no-hype buyer’s guide to small-form-factor machines for local LLMs in 2026 — Apple’s Mac mini, NVIDIA’s DGX Spark, AMD Strix Halo boxes and Intel — with verified specs, prices and token-per-second numbers, plus picks by use case.
The rumored RTX 50 Super refresh could finally bump VRAM where it counts — 24GB on the 5080 Super, 18GB on the 5070 Super. For running local LLMs, that’s the spec that matters. Here’s the honest picture.
NVIDIA’s Vera Rubin is the biggest AI-hardware story of 2026: a six-chip platform that NVIDIA says cuts inference costs up to 10× versus Blackwell. Here’s what it is and why it matters even if you never own one.
The RX 9070 XT costs hundreds less than the RTX 5080 and beats it in some raw AI benchmarks. So is it the value upset of 2026 — or does CUDA and compute still win? Let’s settle it.
AMD’s RDNA4 flagship matches the RTX 5070 Ti on paper and undercuts it on price. But AI isn’t decided on paper — it’s decided by software, and that’s where the verdict gets interesting.
Same Blackwell die, same memory bandwidth — but 96GB versus 32GB and ECC. One costs $2,000, the other $7,500. Here’s exactly when the Pro card earns nearly 4x the price.
$450 separates these two, and for AI it buys both more VRAM and almost double the compute. Here’s whether the RTX 5080 justifies the gap over the 5070 for local AI work.
$200 separates these two — but for AI it’s really 12GB vs 16GB of VRAM, and that single number changes which models you can run. Here’s how they compare on real workloads.
