@TeksEdge: What is the cheapest sane way to get 128GB+ of memory for Local AI in 2026? A Reddit user did the math, and the choices…

What is the cheapest sane way to get 128GB+ of memory for Local AI in 2026? A Reddit user did the math, and the choices are weird. If your goal is simply ≥128GB of model capacity: 6× used Tesla P40 → 144GB VRAM → ~$1,800–$2,300 → dirt cheap CUDA → old Pascal, no modern Tensor Cores 4× V100 32GB → 128GB HBM2 → ~$3,500–$4,200 → much faster memory → older architecture 128GB Mac Studio → ~$3,800–$4,500 → unified memory → ~400–800GB/s depending on configuration → tiny power bill → MLX/Metal, not CUDA 128GB Strix Halo box → ~$4,000 → unified LPDDR5X → ~273GB/s → ROCm/Vulkan → compact x86 box DGX Spark → 128GB unified memory → ~$4,000–$4,700 → Grace Blackwell + CUDA → ~273–301GB/s 6× used RTX 3090 → 144GB VRAM → ~$5,500–$6,500 → modern CUDA/BF16/FlashAttention → enormous power + cooling problem And then there’s cloud: ~$0.65/hr DGX Spark-class access ~$0.80–$1.80/hr community GPU clouds $3+/hr H100/H200-class systems The interesting lesson: Memory capacity itself is becoming surprisingly cheap. What you're really paying for now is: bandwidth interconnect modern tensor support power efficiency software compatibility simplicity A ~$2K P40 monster can hold more model than a ~$4K Strix Halo.