How Does libaom Memory Usage Change at High CPU Presets?

Understanding the memory consumption of the libaom AV1 reference encoder is critical for deploying efficient video encoding pipelines in cloud or resource-constrained environments. While libaom is historically notorious for its massive RAM footprint and slow encoding speeds at low -cpu-used presets (such as 0 to 3), transitioning to high CPU-used presets (typically 5 through 8) substantially shifts its operational profile. At these faster levels, the encoder aggressively deactivates complex structural searches, limiting partition depth and multi-reference matching, which fundamentally decreases the RAM overhead per thread. This article covers the memory characteristics of libaom under these fast presets, analyzing how parameters like threading, resolution scaling, and tile configuration drive memory requirements down when compression speed is prioritized.

Algorithmic Simplifications and Reduced Context Storage

The core reason memory consumption drops as you increase the -cpu-used value from slower quality-focused presets to faster speed-focused presets lies in the exclusion of memory-heavy analysis tools. At low settings, libaom retains massive multi-dimensional evaluation trees in RAM to test combinations of block partitioning, advanced motion vector forecasting, and inter-frame reference frames.

When configuring high presets—specifically -cpu-used 5 through -cpu-used 8—the reference encoder bypasses these intensive calculations. The block partition depth is heavily restricted early in the decision tree, meaning fewer recursive structures must be mapped out in memory. Additionally, the encoder scales down the exhaustive search for reference frames, minimizing the number of active, decoded reference frame buffers that must remain resident in RAM simultaneously.

The Interaction of Resolution, Tiles, and Threads

Even at elevated speed presets, libaom memory usage scales primarily based on the video resolution and the degree of parallelization. Because AV1 uses larger Superblocks (\(128 \times 128\) or \(64 \times 64\) pixels) compared to older codecs, the baseline context size per frame remains higher.

Resolution Scaling: Encoding 4K video at a high CPU-used preset requires significantly more scratchpad memory than encoding 1080p or 720p footage, simply due to the pixel buffer allocations required for frame reconstruction and in-loop filtering (such as CDEF and Loop Restoration).
Threading and Tiling: Utilizing multithreading parameters like -row-mt 1 or explicitly setting multiple tiles via --tile-columns and --tile-rows scales memory consumption up linearly with thread/tile counts. Each independent tile column requires its own context state and row buffers. However, because each thread performs shallow searches at high CPU-used presets, the per-thread memory multiplication factor is drastically lower than it is at slower settings.

Real-Time vs. Good Quality Modes at High Speed Presets

Libaom behaves differently depending on whether it is running in its default standard mode (--usage=good) or real-time mode (--usage=realtime). This distinction heavily influences memory behavior at the absolute highest preset numbers.

Preset Range	Mode Setting	Memory Characteristics
Presets 5 to 6	`--usage=good`	Moderate memory reduction. Good balance of standard AV1 features with optimized, shallower buffer pipelines.
Presets 7 to 8	`--usage=good`	Automatically maps back to preset 6 in standard encoding pipelines, keeping memory consumption static.
Presets 7 to 9	`--usage=realtime`	Maximum memory reduction. Drastically flattens the encoding pipeline, disables heavy in-loop filtering contexts, and minimizes heap allocations to guarantee low, predictable memory overhead fit for live streaming.

Summary for Pipeline Optimization

When designing deployment architectures, assuming libaom will always consume multi-gigabyte blocks of RAM is a misconception tied to archival-tier presets. By choosing higher presets such as -cpu-used 6 or switching to the real-time mode, engineers can drop memory allocation sizes down to manageable fractions of their original peaks. This enables safer parallel execution of multiple concurrent ffmpeg or standalone aomenc processes on modern, multi-core cloud instances without triggering out-of-memory (OOM) faults.