Fast Inference for Augmented Large Language Models
LAMPS predicts API call strategies to optimize scheduling, reducing latency by 27%-85% and TTFT by 4%-96%.
Key Findings
Methodology
LAMPS integrates request length, API call duration, and memory consumption predictions to dynamically assign handling strategies (preserve, discard, swap). It employs a predictive model based on opt-125m to estimate pre-API output length and API duration, then uses a greedy scheduling algorithm prioritizing requests with lower memory footprint over time. This approach effectively reduces request completion time under memory constraints. The system is implemented on vLLM, with extensive evaluations across multiple datasets showing significant performance gains over baseline systems like INFERCEPT and vLLM, especially under high load conditions.
Key Results
- Across datasets such as Math, QA, VE, Chatbot, Image, and TTS, LAMPS achieved 27%-85% reduction in end-to-end latency and 4%-96% decrease in TTFT compared to INFERCEPT and vLLM. The prediction errors had minimal impact on scheduling effectiveness, demonstrating robustness. The approach maintained high request throughput even under heavy loads, effectively mitigating long-tail delays caused by API calls.
Significance
This work addresses the critical bottleneck of request scheduling and memory management in API-augmented LLM systems, enabling real-time, large-scale deployment. By intelligently predicting and handling API call strategies, it overcomes limitations of traditional size-based scheduling, leading to faster response times and better resource utilization. The framework paves the way for more responsive AI services, supporting complex multi-API workflows and high concurrency, thus significantly advancing the practical deployment of interactive AI systems.
Technical Contribution
The key technical innovation lies in developing a predictive model that estimates request memory consumption over time, combined with a unified scheduling policy that incorporates API handling strategies. This approach enables request prioritization based on both output length and memory footprint, dynamically adjusting to request characteristics. The integration of these components into a greedy scheduling algorithm offers a practical solution with theoretical backing, improving upon existing methods that treat scheduling and memory management separately. Implementation on vLLM demonstrates real-world applicability and substantial performance improvements.
Novelty
This research is the first to embed API call strategy prediction into a unified scheduling framework for augmented LLMs. Unlike prior work focusing solely on request size or static policies, it dynamically predicts and optimizes request handling strategies, effectively balancing response latency and memory usage. The approach introduces a novel combination of request length, API duration, and memory consumption modeling, enabling more intelligent and adaptive scheduling tailored for API-rich environments.
Limitations
- The prediction models depend heavily on API type and request features; inaccuracies can degrade scheduling quality. Complex or unseen API types may challenge the model's generalization.
- Assumes API durations are relatively predictable; highly variable or unpredictable API response times could reduce effectiveness.
- In extremely high load scenarios, the greedy scheduling may still face bottlenecks, and fairness among requests remains an open issue.
Future Work
Future directions include incorporating reinforcement learning to adaptively refine scheduling policies, extending to multi-API environments, and improving prediction robustness. Exploring distributed scheduling architectures and integrating more sophisticated memory management techniques could further enhance scalability and efficiency in real-world deployments.
AI Executive Summary
The rapid growth of large language models (LLMs) like GPT-4 has revolutionized natural language processing, enabling sophisticated conversational AI and complex task automation. However, deploying these models in real-time, high-concurrency environments remains challenging due to latency and memory constraints, especially when models are augmented with external APIs. Existing scheduling strategies, such as first-come-first-served or size-based algorithms, struggle to handle the variability introduced by API calls, leading to long tail delays and inefficient resource utilization.
To address this, the paper introduces LAMPS, a novel inference framework that predicts API call strategies and integrates them into a unified scheduling policy. By leveraging a lightweight predictor based on the opt-125m model, LAMPS estimates the pre-API output length and API duration for each request. These predictions inform the selection of handling strategies—preserve, discard, or swap—aimed at minimizing memory waste during API calls. The core idea is to prioritize requests not just by size but by their memory footprint over time, balancing response speed and resource efficiency.
The system employs a greedy scheduling algorithm that dynamically reorders requests based on predicted memory consumption, effectively reducing request latency and preventing long-tail delays caused by API interactions. Extensive experiments on datasets such as Math, QA, VE, and others demonstrate that LAMPS consistently outperforms baseline systems like INFERCEPT and vLLM, achieving up to 85% reduction in end-to-end latency and 96% in TTFT. These improvements are crucial for deploying large-scale, interactive AI services that require rapid response times.
The significance of this work lies in its ability to bridge the gap between request prediction and resource-aware scheduling, enabling more efficient and scalable AI systems. By embedding API handling strategies into the scheduling process, LAMPS offers a practical solution to the longstanding challenge of managing memory and latency in augmented LLMs. Looking ahead, future research will explore reinforcement learning-based policy optimization, multi-API support, and distributed scheduling architectures, aiming to further enhance the responsiveness and scalability of AI services in real-world applications.
Deep Dive
Abstract
Augmented Large Language Models (LLMs) enhance the capabilities of standalone LLMs by integrating external data sources through API calls. In interactive LLM applications, efficient scheduling is crucial for maintaining low request completion times, directly impacting user engagement. However, these augmentations introduce scheduling challenges due to the need to manage limited memory for cached information (KV caches). As a result, traditional size-based scheduling algorithms, such as Shortest Job First (SJF), become less effective at minimizing completion times. Existing work focuses only on handling requests during API calls by preserving, discarding, or swapping memory without considering how to schedule requests with API calls. In this paper, we propose LAMPS, a novel LLM inference framework for augmented LLMs. LAMPS minimizes request completion time through a unified scheduling approach that considers the total length of requests and their handling strategies during API calls. Recognizing that LLM inference is memory-bound, our approach ranks requests based on their consumption of memory over time, which depends on both the output sizes and how a request is managed during its API calls. To implement our scheduling, LAMPS predicts the strategy that minimizes memory waste of a request during its API calls, aligning with but improving upon existing approaches. We also propose starvation prevention techniques and optimizations to mitigate the overhead of our scheduling. We implement LAMPS on top of vLLM and evaluate its performance against baseline LLM inference systems, demonstrating improvements in end-to-end latency by 27%-85% and reductions in TTFT by 4%-96% compared to the existing augmented-LLM system, with even greater gains over vLLM.