SpotServe: Serving Generative Large Language Models on Preemptible Instances (2311.15566v1)

Abstract: The high computational and memory requirements of generative LLMs make it challenging to serve them cheaply. This paper aims to reduce the monetary cost for serving LLMs by leveraging preemptible GPU instances on modern clouds, which offer accesses to spare GPUs at a much cheaper price than regular instances but may be preempted by the cloud at any time. Serving LLMs on preemptible instances requires addressing challenges induced by frequent instance preemptions and the necessity of migrating instances to handle these preemptions. This paper presents SpotServe, the first distributed LLM serving system on preemptible instances. Several key techniques in SpotServe realize fast and reliable serving of generative LLMs on cheap preemptible instances. First, SpotServe dynamically adapts the LLM parallelization configuration for dynamic instance availability and fluctuating workload, while balancing the trade-off among the overall throughput, inference latency and monetary costs. Second, to minimize the cost of migrating instances for dynamic reparallelization, the task of migrating instances is formulated as a bipartite graph matching problem, which uses the Kuhn-Munkres algorithm to identify an optimal migration plan that minimizes communications. Finally, to take advantage of the grace period offered by modern clouds, we introduce stateful inference recovery, a new inference mechanism that commits inference progress at a much finer granularity and allows SpotServe to cheaply resume inference upon preemption. We evaluate on real spot instance preemption traces and various popular LLMs and show that SpotServe can reduce the P99 tail latency by 2.4 - 9.1x compared with the best existing LLM serving systems. We also show that SpotServe can leverage the price advantage of preemptive instances, saving 54% monetary cost compared with only using on-demand instances.

Serving Generative LLMs on Preemptible Instances: An Examination of SpotServe

This essay provides an expert overview of the paper "SpotServe: Serving Generative LLMs on Preemptible Instances." The paper presents SpotServe, a pioneering system designed to address the computational and cost challenges of serving generative LLMs using preemptible GPU instances on cloud platforms.

Context and Challenges

Generative LLMs, such as GPT-4 and ChatGPT, have gained prominence due to their advanced capabilities in language understanding and generation. However, their substantial computational requirements pose significant cost challenges for deployment, especially for organizations with budget constraints. This paper targets the cost reduction of serving LLMs by employing preemptible GPU instances which are available at a reduced price compared to on-demand instances but can be preempted by the cloud provider at any time, often with a brief grace period.

SpotServe is introduced as the first system to serve distributed generative LLMs on preemptible instances, addressing three primary challenges:

1. Dynamic Reparallelization: Due to changing instance availability, SpotServe dynamically adjusts the parallelization configuration to maintain optimized performance in terms of throughput and inference latency while balancing monetary costs.
2. Instance Migration: Effective utilization of SpotServe necessitates minimizing instance migration overhead, treated as a bipartite graph matching problem, optimizing for communication cost during migration using the Kuhn-Munkres algorithm.
3. Stateful Inference Recovery during Grace Period: Leveraging the autoregressive nature of LLMs, SpotServe employs a stateful inference recovery mechanism to commit progress at the token level during preemption, allowing inference to resume without recomputation.

Technical Contributions

SpotServe offers several innovative approaches:

• Parallelization Controller: By dynamically adapting parallelization strategies in response to instance availability and workload fluctuations, SpotServe optimizes system throughput and latency through an adaptive configuration optimizer. This involves balancing data, tensor, and pipeline parallelism.
• Efficient Context Migration: SpotServe reduces the overheads associated with migrating GPU instances by opportunistically reusing model parameters and inference states. The use of bipartite graph matching enables efficient device mapping, minimizing data transmission costs during context migration.
• Interruption Handling: The system's interruption arranger intelligently manages inference suspensions and resumes based on conditions of instance preemption or acquisition, using just-in-time arrangements to maximize inference completions within the grace period.

Results and Implications

The evaluation results demonstrate that SpotServe significantly outperforms existing systems, reducing P99 tail latency by factors ranging from 2.4 to 9.1 and achieving monetary savings of up to 54% compared to on-demand instances. These results suggest that SpotServe offers both practical and theoretical advancements in the efficient deployment of large-scale LLMs.

The SpotServe framework introduces a novel paradigm for leveraging preemptible cloud resources for high-performance ML workloads, suggesting potential applications beyond LLMs to other domains demanding cost-effective distributed computation.

Future Research Directions

The deployment of SpotServe points to new avenues for research, including integrating heterogeneous resource types, exploring broader parallelization configurations, and optimizing dynamically for varying workloads beyond latency minimization. Additionally, Speculating on emerging trends in AI, SpotServe may serve as a foundational architecture as cloud providers enhance offerings around preemptible resources.

By addressing critical challenges in LLM serving on preemptible instances, SpotServe sets a precedent for future innovation in distributed AI systems, revealing opportunities for maximizing cost efficiency while maintaining computational performance.