everyone's obsessing over GPU VRAM for local AI. your RAM and SSD are what actually wreck the experience

A new hardware breakdown puts real numbers on the two components most local-AI buying guides skip entirely — and skimping on either turns a snappy… Aliteq Lena Fischer · AI & Local Compute Editor

Parameter count alone no longer predicts what hardware you need — quantization shrinks weights roughly 4x, and mixture-of-experts models run far lighter than their total parameter count implies.

16GB VRAM is the realistic floor for running 7B–27B models like Qwen2.5 or Mistral Nemo as an agent, not a chatbot.

Agents are harder on hardware than chat: they loop through plan-call-read-plan cycles, often a dozen times per task, multiplying every bottleneck.

When a model doesn't fit in VRAM and spills to CPU, inference drops from 40–60 tokens/second to 2–3 — turning a 30-second task into a 10-minute one.

A dedicated storage budget matters more than most guides admit: 2TB NVMe is a bare minimum, 4TB is realistic once you're accumulating agent state and a model library.

The realistic floor for a serious local-AI or agentic setup in 2026 is 64GB of system RAM and a 2TB Gen4 NVMe drive — not the 32GB and "whatever SSD you have" that a lot of older guides still quote.…

Read the full story

Aliteq everyone's obsessing over GPU VRAM for local AI. your RAM and SSD are what actually wreck the experience Read the full story on Aliteq