Head-to-head comparisons of AI hardware
The RX 7900 XTX matches the RTX 4090 on VRAM and undercuts it on price. The catch is software: ROCm versus CUDA. Here’s where AMD’s flagship genuinely competes — and where it still doesn’t.
The RTX 5090 has double the VRAM of the 5080 and double the price. For AI, that VRAM gap decides everything — here’s which Blackwell card fits your work.
It’s the classic 2026 local-AI dilemma: an RTX 5090’s blistering speed and 32 GB, or a Mac Studio M4 Ultra’s enormous unified memory. Here’s which platform wins, and why.
The RTX 5080 and RTX 4080 Super both ship 16 GB of VRAM at a $999 price. The difference is Blackwell’s GDDR7 bandwidth and FP4 — here’s whether it matters for AI.
The RTX 5070 Ti and 4070 Ti Super are the value-enthusiast picks for local AI — both 16 GB. Here’s whether Blackwell’s bandwidth gain is worth choosing the newer card.
Both the RTX 4090 and RTX 3090 ship 24 GB of VRAM — so for AI, the question isn’t whether a model fits, it’s how fast it runs. Here’s the benchmark-backed verdict.
The RTX 4060 Ti 16GB and RTX 3060 12GB are the two entry points to serious local AI. More VRAM or lower price? Here’s the budget-builder’s verdict.
NVIDIA DIGITS and the Mac Studio are the two compact desktop machines purpose-built for running large AI models at home. Here’s how they actually compare.
On a Mac, the AI question is unified memory: how much, and how fast. Here’s how the M4 Max and M4 Ultra Mac Studio configurations compare for running local LLMs.
The H200 is not a faster compute chip than the H100 — it is the same Hopper GPU with far more memory. For large-model inference, that distinction is everything.
The H100 outclasses the A100 on every performance axis — but the A100 still rents for a fraction of the price. Here’s exactly when the older card wins on cost-efficiency.
Both ship with 16 GB GDDR7 and Blackwell. The 5080 costs 33% more — is it 33% better for AI? We tested both side-by-side.
