# Memory

This page covers what's confirmed about Tensormorph's memory-residency model. Detailed per-kernel memory-traffic diagrams (cache hit rates, on-/off-chip transfer breakdowns) are not a confirmed part of Tensormorph's design - there is no local NVIDIA Nsight Compute reference in this workspace to draw that level of detail from.

Tensormorph's memory story is mostly the story already told in [Level of Detail and Streaming](/docs/core-concepts/level-of-detail-and-streaming): at any moment, only a small fraction of a large model is actually resident in fast memory, and what's resident is governed by a fixed priority order - hovered scalar, current selection, visible tiles, near-visible prefetch, explicit analysis, queries, then background indexing.

## Where to check residency

The **Compute panel** (see [GPU Backend Compatibility](/docs/reference/gpu-backend-compatibility)) is where backend and, by extension, memory placement is visible for a given operation. Beyond that, this page doesn't go further than what's confirmed - a dedicated memory-analysis view comparable to a kernel-level memory-traffic diagram isn't part of the documented design.

## Why this matters at scale

The practical consequence of the LOD/residency model is that Tensormorph's memory footprint doesn't scale with model size the way naively loading a checkpoint would - opening a trillion-parameter model doesn't require a trillion parameters' worth of RAM or VRAM, because most of what's "loaded" at any given moment is coarse statistics and a small working set of tiles, not the full tensor data.

## Related resources

-   [Level of Detail and Streaming](/docs/core-concepts/level-of-detail-and-streaming) - the residency and prefetch model in full.
-   [GPU Backend Compatibility](/docs/reference/gpu-backend-compatibility) - where a given operation's backend (and therefore memory location) is visible.
-   [Sharded Checkpoints](/docs/working-with-models/sharded-checkpoints) - memory considerations specific to a placement-sharded tensor's local vs. full view.