Dissecting Batching Effects in GPT Inference (le.qun.ch)

Recent analysis of batching effects in GPT inference reveals nuanced insights that challenge conventional assumptions about efficiency gains in large language models (LLMs). While batching is widely known to improve throughput in smaller computer vision models, its impact on GPT and other transformer-based LLMs varies across computation stages and operations. The study breaks down GPT’s transformer blocks into dense layers and self-attention components, examining how batching influences them during the initial prompt processing stage and the subsequent auto-regressive token generation. Key findings show that dense layers, which constitute about three-quarters of GPT’s parameters, significantly benefit from batching, especially during auto-regression where input shapes are small (batch size, 1 token). Batching here boosts throughput without notably increasing latency, effectively providing a “free lunch” in efficiency. Conversely, self-attention gains less from batching because its workload scales with batch size and sequence length, leading to roughly linear increases in latency with larger batches. Notably, for shorter sequence lengths, some batching benefits in self-attention do appear, but these diminish as sequences grow longer—highlighting how memory and compute constraints tied to the quadratic complexity of attention impact batching effectiveness. Benchmarks on NVIDIA A100 GPUs with PyTorch 2.0 across various GPT model sizes confirm these trends, underscoring that while the initial prompt stage is naturally well-batched due to sequence length, auto-regressive stages see the most pronounced efficiency improvements from batching dense layers. This comprehensive dissection refines our understanding of where batching improves GPT inference and guides optimized deployment strategies for LLMs, balancing throughput and latency in production AI systems.