"The company that built the wall now sells pools. Read the bandwidth-per-gigabyte column and the pools land beside the Mac, not above it — the shape was right, and the trade comes with the shape."
Three NVIDIA Pools
NVIDIA's own answer to the VRAM wall is the same answer Apple shipped in 2020: put a large memory behind a coherent link so the GPU can address it without a copy. Three products carry it. GH200 joins a Grace CPU with up to 480 GB of LPDDR5X to a Hopper GPU with HBM over NVLink-C2C at "900 gigabytes per second", and NVIDIA's page says the part that matters: "the CPU and GPU share a single per-process page table" — one address space, the GPU faulting pages in from the CPU's memory, exactly the journey track's mapped-touched-wired sequence without the copy step. GB200 NVL72 scales that to a rack. DGX Spark, the GB10 desktop, is the consumer-scale version: "128 GB LPDDR5x, coherent unified system memory" at "273 GB/s", the CPU and GPU on one board joined by NVLink-C2C with "5x the bandwidth of fifth-generation PCIe", 140 W. That is a Mac Studio's shape with NVIDIA's software on it, and the platform lesson explains why that combination sells: twenty years of CUDA code runs on it unchanged.
The Trade That Comes With the Shape
The code block's middle column is the one to read: bandwidth per gigabyte. A card is a small memory with an enormous bus — 56 GB/s per GB on a 5090. A pool is a large memory with a modest bus, and the ratio falls as the pool grows: 4.8 on the M5 Max's 128 GB, 2.4 on the M5 Ultra's 512, 1.6 on the M3 Ultra's, 2.1 on the Spark's 128 GB, 0.8 on Vera's 1.5 TB. The Spark's 273 GB/s buys a 27B ceiling of 19 tokens per second, a third of the M3 Ultra's and the same class as AMD's 128 GB part; NVIDIA's own pool, at its own consumer scale, decodes slower than the Mac's. That is not a failing of NVIDIA's engineering; it is the physics of a wide bus and a large memory not fitting in one package at once, which is the whole of Apple's homework track. The Vera CPU makes the trade explicit at the other end: "up to 1.5 TB of memory" on "SOCAMM, detachable, field-replaceable modules" at "up to 1.2 terabytes per second" — a terabyte and a half you can swap, at a bandwidth per gigabyte below the Mac's, on a chip that is a CPU beside a GPU rather than a pool for one.
What the Copy Proves
Two things, and the convergence track takes both further. First, that the architecture was right: when the largest GPU company builds a coherent pool from its CPU's LPDDR to its GPU and sells a desktop with 128 GB of it, the diagram Apple drew for a laptop in 2020 has become the industry's diagram for the wall's other side. Second, that the pool is not Apple's alone, and its advantages — capacity, one address space, no copy — are now available with CUDA attached, at a bandwidth-per-gigabyte that the same physics constrains. The Spark's price tells the last part of the story: launched at $3,999, raised to $4,699 "due to the constrained memory supplies worldwide" per NVIDIA's own forums as reported — the same memory market in which the Mac Studio's 512 GB configuration stopped being sold a week later, with Apple silent and the press citing the DRAM shortage. Everyone who builds a pool now buys the same DRAM.