Developer Cuts LLM Token Costs by 96% Using Rust-Based Recursive Language Model

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A developer built RLM-Rust, a Rust implementation of Recursive Language Models (RLM), to reduce the high token costs and latency associated with processing large AI context windows exceeding 100,000 tokens. The project was inspired by an existing Python-based RLM repository, which the developer found too slow for practical use. By rewriting the system in Rust and replacing Python subprocesses with Rhai, an embedded scripting engine, the new implementation ran 37.6% faster and executed in-memory code queries in under 0.001 milliseconds. The approach works by keeping large context data in RAM and querying it locally, avoiding repeated transmission of raw text over HTTP to API endpoints. The open-source project, published on GitHub, reportedly achieved a 96% reduction in token costs and supports multiple LLM providers, though the developer notes it still requires further testing and refinement.

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