How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs

August 13, 2026 | Source: Towards Data Science

Tags: Qwen2.5, LLM inference, tokenization, multi-agent systems, OKF, latency optimization

Researchers show that pre-tokenizing inputs once and sharing integer arrays between three Qwen2.5-Coder models via shared memory cuts time-to-first-token by 28-37%, but only with a full 151,936-entry vocabulary equality check — a vocab_size comparison alone is insufficient and produces silent correctness failures.

Details

This Towards Data Science article presents a working inference optimization for multi-agent pipelines where several LLMs from the same model family process the same document. The core insight: Qwen2.5-Coder models at 7B, 3B, and 1.5B sizes share an identical BPE vocabulary, so tokenizing once and sharing the pre-computed integer array through /dev/shm eliminates redundant tokenization in downstream agents. Measured results across 7 trials per prompt with greedy decoding and 64 new tokens: the 3B model's mean TTFT drops from 69.3ms to 49.9ms (28.0% reduction), and the 1.5B model drops from 49.6ms to 30.9ms (37.8% reduction). Full pipeline wall clock is 41.3 seconds end-to-end. The critical safety finding: comparing vocab_size values is NOT sufficient to guarantee safe token sharing. Feeding a downstream model integer arrays tokenized by a different vocabulary produces fluent, coherent-looking, but factually incorrect output — a silent failure mode. The correct check is a full 151,936-entry get_vocab() dictionary equality comparison. The implementation uses Google's Open Knowledge Format (OKF) with one added field (token_pointer) to pass the shared memory path between agents. Self-imposed limitations are clearly stated: short-block regime only (few hundred tokens), no custom CUDA, and tokenizer equivalence is verified only for the specific checkpoint versions pinned in the repo — not a family-wide standing guarantee.