GPU Inference

GPU inference refers to using graphics processing units to execute forward passes through trained neural networks, producing predictions, classifications, generated text, images, or other outputs. Unlike training, which updates model weights through backpropagation, inference uses fixed weights to process new inputs as quickly as possible.

GPUs excel at inference because neural network computations are inherently parallel. A single inference request might involve billions of matrix multiplications, and a GPU's thousands of cores can execute many of these operations simultaneously. This parallelism allows GPUs to process inference requests orders of magnitude faster than CPUs for most deep learning architectures.

The choice of GPU for inference depends on the model architecture, precision requirements, and latency targets. Smaller models may run efficiently on consumer-grade GPUs, while large language models with billions of parameters require high-end datacenter GPUs with large memory capacities. The memory bandwidth of the GPU is often the bottleneck for inference performance, particularly for autoregressive language models.

Inference workloads have different characteristics than training workloads. They tend to be latency-sensitive rather than throughput-oriented, they often process single inputs rather than large batches, and they need to handle variable and unpredictable traffic patterns. These characteristics make serverless GPU platforms particularly well-suited for inference, as they can scale resources dynamically based on demand.

Optimizing GPU inference involves techniques such as model quantization to reduce precision and memory usage, batching to amortize overhead across multiple requests, KV caching to avoid redundant computation in autoregressive models, and choosing the right GPU architecture for the specific model being served.