Collaborative LLM Inference over LEO Satellite Networks: Model Splitting and Pipeline Parallelism
Bibliographic record
Abstract
In this paper, we propose a collaborative large language model (LLM) inference scheme for low Earth orbit (LEO) satellite networks. Specifically, an entire LLM is split into multiple submodels deployed on each satellite, thereby enabling collaborative LLM inference via exchanging intermediate activations between satellites. In addition, the proposed scheme takes advantage of the pipeline parallelism mechanism that overlaps submodel inference with intermediate activation transmission, which can reduce LLM inference delay. Furthermore, we formulate an LLM inference delay minimization problem, which is transformed into a shortest-path search problem. To solve it, a directed acyclic graph (DAG)-based model splitting algorithm is developed, which restructures the DAG to identify the optimal model splitting point via an$A^{\star}$-based algorithm. Extensive simulation results demonstrate that the proposed scheme can reduce inference delay by up to 42 %, as compared to the state-of-the-art benchmarks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".