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An optimized library from NVIDIA for accelerating Transformer inference on GPUs and CPUs with kernels, quantization, and memory optimizations.
DESCRIPTION
FasterTransformer is an open-source library by NVIDIA providing optimized kernels and utilities to speed Transformer model inference. It supports GPU acceleration, mixed precision (FP16/INT8), and memory-efficient techniques like KV caching. Integrations with TensorRT, cuBLAS, and CUDA enable high throughput serving. The library targets production LLM and NMT deployments requiring low latency and high throughput. It helps teams reduce serving costs and scale model deployments.
ALTERNATIVE DEFINITIONS
NVIDIA (GitHub)
FasterTransformer is a toolkit for accelerating transformer inference on GPUs and CPUs, supporting BERT, GPT, and other transformer architectures with optimized kernels.
Emphasizes performance acceleration across architectures and compute targets.
NVIDIA Developer Blog
FasterTransformer provides optimized implementations and memory strategies for transformer-based models to achieve low-latency, high-throughput inference.
Focuses on latency and throughput gains via optimizations and memory strategies.
RELATED TERMS
NVIDIA TensorRT CUDA cuBLAS Transformer Model Inference Optimization Quantization FP16 INT8 KV Cache Model Serving
COMPETING TERMS
TensorRT-LLM ONNX Runtime vLLM
KNOWLEDGE AREAS
AI Infrastructure Platform & Tooling MLOps
TARGET AUDIENCE
AI Engineer MLOps Engineer Hardware Engineer Software Developer Cloud Architect
EXTERNAL SOURCES
- Wikipedia - Transformer (machine learning model)
- NVIDIA - Official repository and documentation
- NVIDIA Developer - Technical guidance on optimizing inference
- NVIDIA TensorRT Docs - Inference platform documentation
TOP QUESTIONS
What is FasterTransformer?
It’s an NVIDIA toolkit that accelerates Transformer model inference using optimized kernels, mixed precision, and memory techniques.
Category: Definition
Why use FasterTransformer for LLM serving?
It reduces latency and increases throughput, lowering serving costs and improving user experience for large models.
Category: Relevancy
How does FasterTransformer achieve speedups?
Through CUDA kernels, TensorRT integration, quantization (FP16/INT8), and KV cache optimizations to minimize memory and compute overhead.
Category: Concept
What models are supported by FasterTransformer?
Common architectures like BERT and GPT variants, with examples provided in the repository.
Category: Capabilities
What are typical deployment mistakes with FasterTransformer?
Mismatched CUDA/cuBLAS versions, improper precision settings, and inadequate batching can limit expected performance gains.
Category: Mistakes
How does FasterTransformer compare to ONNX Runtime?
FasterTransformer focuses on NVIDIA-optimized kernels; ONNX Runtime is a cross-platform engine with broad hardware backends.
Category: Distinction
What hardware is needed for FasterTransformer?
NVIDIA GPUs provide the best results; CPU support exists but GPUs deliver significant acceleration.
Category: Requirements
How do you benchmark FasterTransformer?
Use provided scripts to measure latency/throughput across batch sizes and precisions, aligning with real serving workloads.
Category: Process
Where to learn more about inference optimization?
Explore NVIDIA’s developer blogs and TensorRT documentation for best practices and examples.
Category: Learning