HBF Gives AI Chips Terabytes of Memory

The memory squeeze

AI accelerators are hungry for two things: speed and space. The fastest memory today is HBM, short for High Bandwidth Memory, the stacked chips that sit right next to a GPU and feed it data at high rates. The catch is capacity. A single HBM4 stack tops out around 64GB, and cramming more of it onto a chip gets expensive.

Now Sandisk and SK hynix have a pitch. On Tuesday they formally introduced High Bandwidth Flash, or HBF, a new spec that tries to marry the speed of HBM with the roominess and non-volatility of NAND flash, the same tech inside your SSD. Non-volatile just means the data stays put when the power is off.

What HBF actually is

The initial spec defines HBF packages holding up to 512GB, using either 8-high or 16-high stacks of NAND dies. These are not off-the-shelf flash chips, though. Sandisk has described them as "HBF core dies," specialized silicon built with a fast interface and many arrays that can be read and written at the same time.

Performance comes in three grades, from roughly 0.4 TB/s up to 3.0 TB/s. That wide range hints at a multi-year roadmap rather than a single product. It is also worth a caveat: it is not fully clear whether those figures describe a single package or the whole subsystem. At the top end, 3 TB/s would edge past the 2 TB/s of a single HBM4 stack, though HBF is unlikely to match HBM on latency, the delay before data starts flowing.

To connect HBF to different chips, the companies lean on UCIe, the Universal Chiplet Interconnect Express standard for linking chiplets together. SK hynix names UCIe directly; Sandisk refers to an "xPU-HBF" interface, which may just be its own flavor of the same idea. Hitting over 400 GB/s from one package would need a UCIe link running at up to 64 GT/s across 64 lanes, which makes the base die a genuinely complex chunk of silicon.

Why it matters

The appeal is capacity per dollar. An HBM4 stack maxes out at 64GB; an HBF stack promises up to 512GB. For AI inference, the stage where a trained model actually answers your questions, that extra room could matter more than raw speed. Big models need large memory pools sitting close to the compute, and HBM alone gets pricey when you scale it up. Slower but far larger memory could be a useful middle tier.

The spec is being released through the Open Compute Project, so HBF is an open standard rather than a locked-down proprietary interface. That lowers the barrier for others to build around it. The full electrical, packaging, reliability and software details are defined in the spec but have not yet been officially published by the OCP.

The adoption question

Here is the honest wrinkle. A memory standard is only as good as the chips that use it, and HBF's guest list is thin. Since Sandisk and SK hynix announced their collaboration in 2025, only Google and Tenstorrent have joined the consortium. The heavy hitters, AMD, Nvidia, Intel, Broadcom, Marvell, Micron, Qualcomm, Samsung and Western Digital, have so far stayed on the sidelines.

That does not doom the idea. Google alone builds a lot of AI silicon, and an open standard can gather momentum over time. But memory tiers succeed when the people designing accelerators decide they need them, and right now most of that crowd has not signed on.

What's next

Watch two things. First, whether the OCP publishes the full spec and whether real silicon appears near the lower 0.4 TB/s grade before anyone chases 3 TB/s. Second, and more telling, whether any major GPU or accelerator maker joins Google and Tenstorrent. HBF is a credible answer to a real problem, giving AI chips more memory without an all-HBM budget. Now it needs customers willing to build it in.