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FASTERTRANSFORMER
NVIDIA's optimized transformer inference and training backend
Apache-2.0
ABOUT
Deploying large transformer models (BERT, GPT, Llama, T5) for production inference on NVIDIA GPUs requires hand-tuned CUDA kernels for attention, feed-forward layers, and quantization — each model architecture and GPU generation needing separate optimization work. FasterTransformer provides a consolidated library of highly optimized CUDA kernels covering encoder and decoder transformer architectures with INT8/FP8 quantization, sparse attention, and tensor parallelism, delivering up to 3x inference speedup over naive PyTorch implementations across single and multi-GPU deployments.
INTEGRATION GUIDE
1. Deploy large language models (GPT, Llama, BLOOM) for production inference with INT8/FP8 quantization and tensor parallelism across multiple NVIDIA GPUs for maximum throughput
2. Accelerate BERT and BERT-like encoder models for latency-sensitive NLP pipelines (search, classification, question answering) using FasterTransformer's fused multi-head attention kernels
3. Integrate FasterTransformer as a custom backend in PyTorch and TensorRT pipelines to replace naive transformer implementations with optimized CUDA kernels for batched inference
TAGS
transformer-inferencegpu-optimizationcudamodel-servingquantizationnvidiadeep-learning