NanoFlow: Towards Optimal Large Language Model Serving Throughput (2408.12757v2)

Abstract: LLMs have resulted in a surging demand for planet-scale serving systems, where tens of thousands of GPUs continuously serve hundreds of millions of users. Consequently, throughput has emerged as a key metric that determines serving systems' performance. Due to large model sizes and memory-intensive self-attention, LLM serving has been commonly assumed to be memory-bound. Through a detailed analysis, we show that despite having memory-intensive components, end-to-end LLM serving is compute bound for most common workloads and LLMs. Alas, most existing serving engines fall short from optimal compute utilization, because the heterogeneous operations that comprise LLM serving--compute, memory, networking--are executed sequentially within a device. We propose NanoFlow, a novel serving framework that exploits intra-device parallelism, which overlaps the usage of heterogeneous resources within a single device. NanoFlow splits inputs into smaller nano-batches and duplicates operations to operate on each portion independently, enabling overlapping. NanoFlow automatically identifies the number, size, ordering, and GPU resource allocation of nano-batches to minimize the execution time, while considering the interference of concurrent operations. We evaluate NanoFlow's end-to-end serving throughput on several popular models such as LLaMA-2-70B, Mixtral 8x7B, LLaMA-3-8B, etc. With practical workloads, NanoFlow provides 1.91x throughput boost compared to state-of-the-art serving systems achieving 50% to 72% of optimal throughput across popular models.

• The paper demonstrates that leveraging intra-device parallelism through nano-batching and execution unit scheduling significantly boosts LLM serving throughput.
• The methodology incorporates a parameter search algorithm to automate pipeline setup for diverse LLM architectures, enhancing adaptability.
• Empirical evaluations on NVIDIA A100 GPUs show NanoFlow achieving throughput improvements ranging from 59% to 72% of optimal performance compared to state-of-the-art systems.

Analyzing NanoFlow: Intra-Device Parallelism for Optimal LLM Serving Throughput

The increasing adoption of LLMs necessitates efficient serving systems capable of handling massive computational demands. The paper NanoFlow: Towards Optimal LLM Serving Throughput presents NanoFlow, an innovative framework designed to enhance throughput by employing intra-device parallelism. This essay provides an expert analysis of the NanoFlow framework, emphasizing its significance, architecture, and empirical results.

Background and Objectives

Given the resource intensiveness of LLMs, achieving optimal throughput has emerged as a paramount performance metric. With models such as GPT-3 necessitating significant computational resources, the efficient utilization of available hardware becomes crucial. Traditional methods focus on inter-device parallelism but often result in underutilization of intra-device resources. Hence, NanoFlow introduces intra-device parallelism as a solution, concurrently leveraging compute, memory, and network resources within a single device to enhance throughput.

Key Innovations

NanoFlow introduces two primary innovations:

1. Nano-Batching: By subdividing requests into small granular operations termed nano-batches, it breaks the sequential dependency inherent in LLM inference. This allows for the overlapping of operations.
2. Execution Unit Scheduling: This strategy partitions device functional units and concurrently executes different operations within each unit. The paper highlights the use of a parameter search algorithm to automate pipeline setup across various models, thereby ensuring adaptability and ease of porting NanoFlow to differing LLM architectures.

Implementation and Evaluation

Implemented on NVIDIA A100 GPUs, NanoFlow was benchmarked across several models, including LLaMA-2-70B, Mixtral 8×7B, LLaMA-3-8B, yielding significant throughput improvements. NanoFlow achieved an average throughput boost of 1.91x compared to existing frameworks while reaching 59% to 72% of optimal throughput across ported models. The comparative analysis against state-of-the-art systems like vLLM, DeepSpeed-FastGen, and TensorRT-LLM further reinforces the efficacy of NanoFlow in providing superior serving throughput.

Detailed Analysis

The authors meticulously derive factors influencing throughput, including hardware specifications (e.g., memory bandwidth, compute capacity), model configurations (e.g., hidden dimension size, number of layers), and user query statistics (e.g., average number of tokens in prompts and outputs). By establishing a theoretical model to determine optimal throughput, the authors show that modern LLM serving workloads are predominantly compute-bound. Thus, the key to maximizing throughput involves ensuring the full utilization of computing resources.

Performance Insights

The empirical evaluations conducted across different datasets (Splitwise, LMSYS-Chat-1M, and ShareGPT) demonstrate NanoFlow's capabilities. In online latency testing, NanoFlow achieved a balanced performance in preserving low latency under high request rates, outperforming existing frameworks, particularly in online request throughput.

A significant portion of the analysis details the contribution of specific techniques to end-to-end performance:

• Nano-Batching Overhead: Introducing nano-batches incurs a minimal overhead yet establishes a foundation for executing interleaved operations.
• Operation Overlapping: Exploiting the overlapping of compute-, memory-, and network-bound operations yielded substantial throughput gains, addressing the underutilization issues prevalent in sequential execution.
• KV-Cache Management: Novel techniques like peak memory estimation, head-parallelism for KV-cache, and asynchronous KV-cache offloading were introduced to optimize memory usage efficiently.

Implications and Future Directions

NanoFlow significantly advances the state-of-the-art in LLM serving systems. By efficiently managing intra-device resources, it not only enhances throughput but also improves overall hardware utilization. This framework presents a paradigm shift in handling LLM inference, suggesting a broader scope for optimizing serving systems through finer-grained parallelism.

Potential future developments could explore extending NanoFlow's principles to other types of accelerators (e.g., AMD GPUs) and integrating more sophisticated resource estimation algorithms. Moreover, adapting NanoFlow to handle dynamic workloads with varying computational patterns could further elevate its applicability in real-world scenarios.

In conclusion, NanoFlow offers a compelling approach to overcoming the limitations of existing LLM serving systems. Its innovative use of intra-device parallelism sets a new benchmark in achieving optimal throughput, marking a significant step forward in the efficient deployment of large scale LLM applications.