How is inference latency reduced in LLMs?

Inference latency in large language models (LLMs) is reduced through a combination of model optimization, hardware/software improvements, and efficient decoding strategies. The goal is to minimize the time required to generate each token while maintaining output quality. These approaches address computational bottlenecks, memory usage, and algorithmic inefficiencies inherent in transformer-based architectures.

One primary method is optimizing the model architecture and parameters. Techniques like quantization reduce the precision of model weights (e.g., from 32-bit floats to 8-bit integers), cutting memory usage and speeding up computations. Pruning removes less critical weights or layers, creating a smaller, faster model without significant accuracy loss. For example, DistilBERT achieves 60% of BERT’s speedup by distilling knowledge into a smaller network. Knowledge distillation trains smaller models to mimic larger ones, balancing speed and performance. Additionally, frameworks like PyTorch or TensorFlow leverage operator fusion (combining multiple operations into one) to reduce overhead from repeated memory accesses. These changes directly reduce the computational workload per token.

Hardware and software optimizations also play a key role. GPUs and TPUs accelerate matrix operations critical for transformers, while libraries like NVIDIA’s TensorRT or OpenAI’s Triton optimize kernels for specific hardware. KV caching stores intermediate key-value pairs during attention computation, avoiding redundant calculations for previous tokens. For instance, the FlashAttention algorithm reorganizes attention computations to minimize memory reads/writes, improving both speed and memory efficiency. Batching multiple requests together (dynamic batching) maximizes hardware utilization, especially when requests vary in length. These low-level optimizations ensure hardware resources are used efficiently.

Finally, decoding strategies reduce the number of steps needed to generate text. Greedy decoding (selecting the highest-probability token at each step) is fast but less diverse, while techniques like speculative decoding use smaller “draft” models to propose token sequences that a larger model verifies in batches, reducing overall steps. For example, the Medusa framework generates multiple candidate tokens per step, which the main model evaluates in parallel. Early-exit strategies allow some layers to skip computation if confidence is high enough. These methods trade minor accuracy compromises for significant latency gains, particularly in longer sequences.