TLDR: BucketServe is a new framework that optimizes Large Language Model (LLM) inference by using a bucket-based dynamic batching strategy. It groups requests by sequence length to minimize memory waste and dynamically adjusts batch sizes, significantly improving throughput and system load capacity (up to 3.58x higher throughput) while maintaining service quality and introducing negligible overhead, especially under diverse and high-concurrency workloads.
Large Language Models (LLMs) are transforming various industries, moving traditional rule-based systems towards more advanced AI-driven solutions. However, serving these powerful LLMs for inference—the process of generating responses—comes with significant challenges. They are incredibly resource-intensive and sensitive to latency, meaning they need a lot of computing power and quick response times to keep users happy.
Existing systems often use static or continuous batching, which can lead to inefficient use of GPU memory and higher latency, especially when dealing with diverse types of requests. These methods also struggle to adapt to fluctuating workloads, leading to less-than-optimal performance and potential failures in meeting service level objectives (SLOs).
Introducing BucketServe
To tackle these issues, researchers have introduced a new framework called BucketServe. This innovative system uses a bucket-based dynamic batching approach to significantly improve LLM inference performance. Imagine grouping incoming requests into “buckets” based on how long their sequences are. This strategy helps minimize wasted memory due to “padding” (where shorter sequences are artificially extended to match the longest one in a batch) and optimizes GPU memory usage by adjusting batch sizes in real-time, preventing out-of-memory errors.
BucketServe also incorporates adaptive bucket splitting and merging, along with priority-aware scheduling. This means it can intelligently manage resources, preventing fragmentation and ensuring that critical requests meet their performance targets.
Addressing Disaggregated Architectures
Modern LLM serving often uses a “disaggregated” architecture, splitting the inference process into two main phases: prefill and decoding. In the prefill phase, the model reads the entire input prompt and prepares the Key-Value (KV) Cache. In the decoding phase, it generates new tokens one by one, continuously updating the KV cache. While this separation allows for specialized optimizations, it also introduces challenges like resource contention, complex scheduling, and difficulties in batching.
BucketServe is built upon existing frameworks like vLLM and extends their capabilities to manage requests in this disaggregated setup. It leverages the static nature of prefill inputs by using bucketing, grouping requests into size-homogeneous buckets (e.g., 0-256 tokens, 256-1024 tokens) to maximize parallelism and minimize padding. For the dynamic decoding phase, it applies continuous batching to handle varying output lengths efficiently.
How BucketServe Works
The system operates with a three-tier architecture. When a request arrives, a Request Bucketing Manager groups it into a bucket based on its sequence length and task type. In low-load scenarios, all requests might go into a single bucket to reduce overhead. Under high loads, the system dynamically adjusts the number and boundaries of buckets to optimize efficiency.
A Dynamic Batching Controller then takes requests from these buckets and forms batches, calculating the optimal batch size based on current GPU memory. This prevents memory errors while maximizing throughput. A P/D Scheduler manages the flow between the prefill and decoding phases, ensuring requests are processed efficiently. Finally, a Global Monitor continuously collects system metrics, providing real-time insights that allow the system to make informed decisions about batch sizes and scheduling policies.
Also Read:
- LaCache: A Smart Memory Solution for Long-Context LLMs
- Optimizing Large Language Model Training Through Dynamic Data Weighting
Impressive Performance Gains
Experiments show that BucketServe significantly outperforms other state-of-the-art systems. It achieved up to 3.58 times higher throughput compared to UELLM and 1.31 times higher than DistServe under heavy workloads. It can also handle 1.93 times more request load while maintaining an 80% service level objective attainment compared to DistServe, and demonstrates 1.975 times higher system load capacity than UELLM.
Crucially, the overhead introduced by BucketServe’s bucketing and dynamic batching mechanisms is negligible, accounting for less than 1% of the total execution time. This highlights its efficiency in optimizing resource utilization and improving overall system throughput without adding significant computational burden.
In essence, BucketServe offers a smart and efficient way to serve large language models, ensuring high performance and reliability even under demanding and varied workloads. You can read the full research paper for more technical details at this link.
