LLMs have seen great advance in both academia and industry, and their popularity results in numerous open-source frameworks and techniques in accelerating LLM pre-training, fine-tuning, and inference. Training and deploying LLMs are expensive as it requires considerable computing resources and memory, hence many efficient approaches have been developed for improving system pipelines as well as operators. However, the runtime performance can vary significantly across hardware and software stacks, which makes it difficult to choose the best configuration. In this work, we aim to benchmark the performance from both macro and micro perspectives. First, we benchmark the end-to-end performance of pre-training, fine-tuning, and serving LLMs in different sizes , i.e., 7, 13, and 70 billion parameters (7B, 13B, and 70B) on three 8-GPU platforms with and without individual optimization techniques, including ZeRO, quantization, recomputation, FlashAttention. Then, we dive deeper to provide a detailed runtime analysis of the sub-modules, including computing and communication operators in LLMs. For end users, our benchmark and findings help better understand different optimization techniques, training and inference frameworks, together with hardware platforms in choosing configurations for deploying LLMs. For researchers, our in-depth module-wise analyses discover potential opportunities for future work to further optimize the runtime performance of LLMs.
The paper analyzes the efficiency of LLMs during training, fine-tuning, and inference, highlighting importance of system optimizations and hardware capabilities.
Training LLMs with optimizations like ZeRO, quantization, and FlashAttention proves vital for memory conservation and computational efficiency, with potential underutilization of GPU resources.
Fine-tuning with techniques like LoRA and QLoRA shows a trade-off in efficiency, suggesting combinations of methods could lead to better optimization.
Inference performance varies with hardware, where libraries like TGI and LightLLM excel on different GPU setups, emphasizing the significance of hardware choices.
Future work in the optimization of LLMs could benefit both users, by guiding optimal framework and hardware selection, and researchers, by identifying areas to improve computational costs and efficiency.
LLMs are reshaping the landscape of artificial intelligence with their impressive generalization abilities across numerous tasks. As their size continues to balloon, optimizing the runtime performance during various stages—namely training, fine-tuning, and inference—becomes critical. This analysis dissects the performance of LLMs considering system optimizations and hardware capabilities, rendering insights into the efficiency of current frameworks and providing pointers for future enhancements.
Training an LLM involves significant computational efforts. The study benchmarks the training performance across different model sizes (with billions of parameters) and investigates how individual optimization techniques contribute to system efficiency. Techniques like ZeRO, which employs memory optimization strategies; quantization, which streamlines model weights and activations; and FlashAttention, which enhances kernel compute efficiency, are put under the microscope. Interestingly, techniques like ZeRO perform well in conserving memory without compromising on training speed, while offloading techniques can hamstring training due to added CPU handling. FlashAttention emerges as both a speed booster and compatible with memory-saving methods, advocating for its use. Interestingly, the paper finds optimized systems may not fully utilize GPU resources, indicating room for enhancing performance efficiency.
Once pre-trained, fine-tuning customizes LLMs for specific downstream tasks. The paper evaluates parameter-efficient fine-tuning (PEFT) methods such as LoRA, an adaptation strategy that focuses on a subset of model parameters, and QLoRA, which fine-tunes LLMs while employing quantization for efficient computation. LoRA considerably outpaced QLoRA in throughput, evidencing the trade-off between compute efficiency and additional operations introduced by quantization. In tandem with other techniques like FlashAttention and ZeRO, fine-tuning demonstrates increased throughput, highlighting the potential for method combinations to produce significant efficiency gains.
Deploying fine-tuned LLMs, referred to here as "serving," requires models to effectively respond to end-user queries. Optimized inference libraries are crucial for this stage. By evaluating popular frameworks such as vLLM, LightLLM, and TGI across different hardware settings, the study shows contrasting throughput efficiencies, with TGI excelling on standard GPU platforms and LightLLM leading on high-performance GPUs. Latency analysis further underscores the importance of hardware selection, with advanced platforms like A800 outshining consumer-level alternatives.
The findings from thorough benchmarking and detailed runtime analyses reveal both the expansion room and the avenues to optimize LLM runtime performance—valuable for both end-users and researchers. For end-users, these benchmarks guide the choice of configurations and frameworks best suited to deploying LLMs efficiently. For researchers, the uncovered potentials, especially regarding optimization techniques and GPU resource utilization, beckon further developments to minimize costs and maximize performance.
Navigating the challenges of LLM runtime performance demands a balance between computational costs and model efficiency. With smart optimization strategies and an acute understanding of hardware capabilities, it is possible to push the boundaries of what these sizable models can achieve even further. The benchmarks presented herein furnish the AI community with a concrete foundation for steering LLMs towards an optimized future.