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Tensora is an open-source framework that automatically selects the optimal I/O strategy to load LLM checkpoints using workload-aware heuristic algorithms. It supports multiple storage formats and backends, significantly improving model loading efficiency.
Section 01
Tensora is an open-source framework that automatically selects the optimal I/O strategy to load LLM checkpoints using workload-aware heuristic algorithms. It supports multiple storage formats and backends, significantly improving model loading efficiency.
Section 02
Section 03
During the deployment and inference of Large Language Models (LLMs), checkpoint loading often becomes a performance bottleneck. As model sizes continue to grow, checkpoint files can reach tens or even hundreds of gigabytes, and traditional synchronous loading methods lead to significant startup delays. The optimal I/O strategy varies greatly across different scenarios: synchronous reading may be the fastest for small models with single shards, while large models with multiple shards require advanced techniques like asynchronous I/O or memory mapping.
Developers usually need to manually choose between multiple I/O backends, including synchronous POSIX, Tokio asynchronous, Linux io_uring, and memory mapping, but each solution has its applicable scenarios and limitations. This complexity increases deployment difficulty and easily leads to suboptimal choices.
Section 04
Tensora is an open-source framework specifically designed to solve the performance optimization problem of LLM checkpoint loading. It uses workload-aware heuristic algorithms to automatically select the fastest I/O strategy based on checkpoint size, shard structure, and platform capabilities.
The framework supports two mainstream storage formats:
Section 05
Tensora's architecture design demonstrates high pluggability and supports four main I/O backends:
Section 06
It uses a thread-parallel block reading strategy, suitable for small to medium-sized single-shard checkpoints. Through multi-threaded concurrent reading, it fully utilizes the multi-core capabilities of modern CPUs.
Section 07
Based on Rust's Tokio runtime, it provides high-performance asynchronous I/O capabilities. It is particularly suitable for task scenarios that require file-grouped processing, such as range read operations in ServerlessLLM.
Section 08
It leverages the latest asynchronous I/O interface of the Linux kernel, supporting multi-worker-thread ring submission and batch merging. This is the performance leader for large model multi-shard scenarios (≥4GB).
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