Product & chips · Sep 1, 2026

A second paper in two days on putting models into flash memory, from a completely unrelated team.

Chips & semiconductors · This week (published August 31)

From the Sep 1, 2026 daily brief

Our August 31 issue covered the Oxford paper: high-bandwidth flash holds 16 times the capacity per stack of the high-bandwidth memory in use today, and dropping it straight in drags performance down, because the tail latency of flash leaves the GPU's scheduler waiting. What is new today is a second team with a different fix. Huawei, ETH Zürich and Huazhong University of Science and Technology jointly published FLINT, which likewise treats flash as a capacity tier alongside high-bandwidth memory for holding model weights. Three mechanisms carry it: a hardware burst-buffer controller that coalesces scattered reads into large sequential ones; moving flash maintenance work off the inference critical path entirely; and replacing the general-purpose address translation table that supports arbitrary writes in an SSD with a compact read-only one (Semiconductor Engineering, 08-31; the paper, arXiv 2608.25062, August 2026). Put the two papers side by side: two unconnected teams in two days, two different approaches, converging on one diagnosis of the bottleneck — inference is now limited by how much memory can hold, not by how fast the arithmetic runs. ⚠️ Both stop at the paper stage, and neither publishes a latency measurement under a real inference load. That remains the signal this line is waiting for.

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A second paper in two days on putting models into flash memory, from a completely unrelated team.

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Customers of chip-design tool vendors have started capping tokens engineer by engineer — and a year ago nobody cared what that line cost.

The new flagship's enterprise edition went live with its first published prices, and its own account of the mechanism doubles as an audit cost.

OpenAI says its first in-house inference chip delivers 1.5 to 1.9 times the throughput per watt of "the commercial systems tested" — and does not say which systems those were.

Meta launched a personal AI agent called Muse, and the part worth recording is not what it does but that a payment network has written terms for a non-human buyer for the first time.