sLLM: Accelerating LLM Inference using Semantic Load Balancing with Shared Memory Data Structures
Bibliographic record
Abstract
As Large Language Models (LLMs) are increasingly deployed to support a broad spectrum of applications, enhancing inference efficiency and minimizing costs have become critical areas of focus. To address these challenges, researchers have explored optimizing the Key-Value (KV) cache within LLMs. However, existing approaches have not considered the potential benefits of sharing KV caches across multiple requests in a cluster environment. Addressing this gap, we introduce sLLM, a novel system that integrates an efficient shared-memory-based Semantic Load Balancer with a KV cache sharing mechanism. This design significantly reduces the need for recomputation during LLM inference, which enhances inference performance. Our evaluation of the sLLM system showcases its effectiveness: the Semantic Load Balancer achieves up to a 7× reduction in latency when dispatching requests, while the system as a whole can decrease the Time-To-First-Token (TTFT) for LLM inferences by 30 – 58%.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".