rav1e Memory Footprint vs Other AV1 Encoders

This article analyzes and compares the memory footprint of the rav1e AV1 encoder against its primary competitors, SVT-AV1 and libaom. We explore how rav1e’s Rust-based architecture impacts RAM consumption during video encoding, detailing where it excels in resource efficiency and how its memory usage scales under various threading and resolution configurations compared to other popular AV1 encoding libraries.

Understanding rav1e’s Memory Architecture

The rav1e encoder is written in Rust, a language designed for memory safety and concurrency without a garbage collector. This architectural choice gives rav1e a distinct advantage in memory predictability. Unlike C-based encoders that rely heavily on manual memory management, rav1e reduces the risk of memory leaks and keeps its baseline memory allocation relatively low and stable.

rav1e vs. SVT-AV1: Memory vs. Speed

SVT-AV1 (Scalable Video Technology AV1) is currently the industry standard for speed, but this performance comes at a high memory cost. SVT-AV1 is highly parallelized, utilizing extensive multi-threading and lookahead buffers to process frames concurrently.

• SVT-AV1 Memory Usage: At 1080p and 4K resolutions, SVT-AV1 can easily consume between 4 GB to 16 GB+ of RAM depending on the preset and the number of logical processors used. Its aggressive resource allocation scales rapidly with thread count.
• rav1e Memory Usage: rav1e generally operates with a much tighter memory footprint. For standard 1080p encodes, rav1e typically requires under 1.5 GB of RAM. Even under multi-threaded workloads, rav1e’s memory scaling is flatter and more predictable than SVT-AV1, making it highly suitable for systems with limited RAM or virtual private servers (VPS).

rav1e vs. libaom: Efficiency in Reference Encoding

The libaom encoder is the official reference encoder developed by the Alliance for Open Media. Historically, libaom has been criticized for being slow and resource-heavy.

• libaom Memory Usage: While libaom has improved significantly over the years, its memory footprint remains highly variable. High-quality settings (like end-usage=vbr with high lag-in-frames) and multi-threading via tiles can push memory usage beyond 4 GB for 4K video.
• rav1e Memory Usage: rav1e serves as a faster, safer alternative to libaom with a more controlled memory envelope. While libaom can experience memory spikes when handling complex scenes with high frame lookaheads, rav1e manages its frame buffers more conservatively, resulting in a lower peak memory footprint.

Key Factors Influencing rav1e’s Memory Footprint

To optimize rav1e’s memory consumption, it is important to understand what drives its RAM usage:

1. Resolution: Memory consumption scales with video resolution. 4K encodes require significantly more memory than 1080p or 720p encodes due to the size of the reference frames stored in RAM.
2. Tile Configuration: Splitting a video into tiles allows for parallel encoding, but each additional tile column and row increases the memory overhead. Keeping tile counts low reduces RAM usage.
3. Lookahead Limit: Like other encoders, rav1e analyzes future frames to optimize compression. A larger lookahead buffer improves quality but keeps more frames in memory, increasing the footprint.
4. Thread Count: Increasing the number of worker threads increases memory usage linearly, though rav1e manages this scaling more efficiently than SVT-AV1.