What the free TPU v5e-8 actually is, why 8 × 16 GB is not one 128 GB pool, what fits on it, and every catch in Kaggle's own words
The companion to the reel. A phone-verified Kaggle account can pick TPU VM v5e-8 from a notebook's accelerator menu and get eight Google tensor chips with 16 GB of high-bandwidth memory each — 128 GB on the board — for twenty hours every week, with no card anywhere in the flow. PAGE 1 — the quota in Kaggle's own words ("TPUs are now available on Kaggle, for free" and "up to 20 hours per week of TPUs and up to 9h at a time in a single session"), which TPU you get and the exact menu path, what the chips are from Google's own spec table, and the five steps from a logged-out account to eight visible devices. PAGE 2 — the board, and the thing that turns a free TPU into an out-of-memory error: 128 GB is eight 16 GB devices, not one flat pool. Google's full v5e spec rows, the cell that prints the truth, and the sharding cell that makes eight chips behave like one board. PAGE 3 — what fits, as arithmetic rather than opinion: parameters × bytes-per-parameter at bf16, int8 and 4-bit for every model size you'd reach for, measured against the 128 GB, with the KV-cache rule of thumb and what a weekly quota is actually good for. PAGE 4 — the clock: 20 hours a week against the GPU tier's 30, the nine-hour session cap, and one public build (ARahim3/kaggle-tpu-lab) that served a 27B at full precision on this exact board — their numbers, quoted and attributed. PAGE 5 — eight catches, each quoted from the page it came from, starting with the sharding one. PAGE 6 — the proof checklist. Every Kaggle and Google figure is quoted from the page it names, read September 17, 2026; the memory table is parameters × bytes-per-parameter. No TPU session was started for this card. Independent guide from Hyperautomation Labs — not affiliated with Kaggle, Google or Anthropic.
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