Die Wahl des richtigen Deep Learning Frameworks entscheidet maßgeblich über die Effizienz, Skalierbarkeit und Entwicklungsgeschwindigkeit von KI-Projekten. Ob generative Modelle, Large Language Models (LLMs) oder komplexe Computer-Vision-Anwendungen: Moderne Frameworks bilden die zentrale Schnittstelle zwischen datenwissenschaftlicher Entwicklung und hochperformanter Rechenhardware.
Was: Die führenden Frameworks und Serving-Stacks im Überblick
In der modernen Praxis hat sich ein leistungsfähiges Ökosystem aus Trainings- und Inferenz-Frameworks etabliert, die veraltete Legacy-Strukturen (wie Caffe) abgelöst haben:
PyTorch A flexible open-source framework for research and production deployment
PyTorch has established itself as a leading machine learning framework that seamlessly accelerates the transition from agile research to scalable enterprise production environments.
- Dynamic tensor computation (torch.Tensors): Provides highly efficient tensor operations with strong GPU acceleration and seamless integration with popular Python libraries such as NumPy, SciPy and Cython.
- Dynamic computation graphs (Autograd): Enables tape-based, automatic differentiation and intuitive, linear code execution (‘Python-first’), which significantly simplifies the debugging and development of complex neural networks.
- Performance via torch.compile: Utilises modern compiler technologies (such as TorchInductor and OpenAI Triton) to optimise computational processes and Transformer models directly for modern hardware architectures, achieving significant speed-ups.
- Distributed training & enterprise readiness: Supports highly efficient, distributed multi-node training sessions via the native torch.distributed backend, as well as standardised deployment paths (including via ONNX and TensorRT) for production data centre operations.
Keras The flexible multi-backend deep learning API
Keras has evolved from a simple TensorFlow add-on into a standalone, flexible framework. As a platform-independent, high-level API, Keras decouples model development from the underlying computing engine.
- Free choice of backend (PyTorch, TensorFlow, JAX): The same Keras model can be run with either JAX (for maximum XLA performance), PyTorch (for agile workflows) or TensorFlow as the backend, without having to rewrite the code.
- Avoiding framework lock-in: Developers benefit from a consistent, easy-to-understand syntax for model building, whilst the underlying engine can be flexibly adapted to the specific project objective.
- Seamless data integration: Integrates seamlessly into existing data pipelines such as PyTorch DataLoaders or TensorFlow’s tf.data.Dataset to ensure an efficient data flow to the GPUs.
TensorFlow A comprehensive enterprise framework for production-ready ML pipelines
TensorFlow has proven itself to be a scalable open-source platform specifically designed for reliable, continuous operation and structured end-to-end workflows in enterprises.
- Hybrid execution (Eager & Graph Mode): Combines intuitive, line-by-line execution for rapid testing with automatic optimisation via the XLA compiler for high-performance production graphs.
- Comprehensive enterprise ecosystem: Integrates standardised tools such as the tf.data module for highly efficient data pipelines, as well as Keras as a clean, highly standardised API for model building.
- Cross-platform & edge deployment: Offers robust export paths for cloud infrastructures, on-premises data centres, and resource-efficient deployments in edge and embedded environments.
Jax High-performance compositional computing and scalability
JAX combines a familiar, NumPy-compatible syntax with modern compiler technologies and has become an indispensable tool for computationally intensive research and scale-up processes.
- Composable Transformations (jit, grad, vmap): Enable the free combination of automatic vectorisation, differentiation and just-in-time compilation for maximum mathematical flexibility.
- XLA compiler integration: Translates computations directly into highly efficient, hardware-optimised machine code to harness the full performance potential of modern GPU and TPU architectures.
- Modular ecosystem (JAX AI Stack): Relies on lean, decoupled libraries (such as Flax for neural network design and Optax for optimisation strategies) rather than monolithic framework structures.
vLLM & SGLang High-performance serving engines for large language models
Traditional deep learning frameworks are primarily designed for model training. Specialised serving engines are required for the production deployment of language models (LLMs).
- PagedAttention memory management: Solves the problem of fragmentation in graphics memory (VRAM) by dynamically dividing cache memory into blocks, which massively increases memory throughput.
- Continuous Batching: Dynamic bundling of requests at runtime to minimise idle time on the computing units and maximise token throughput (tokens per second) per GPU.
- OpenAI-compatible APIs: Enable straightforward integration of serving instances into existing software architectures and applications.
Open-source frameworks alone are often not sufficient in the enterprise sector to harness the full potential of modern GPU architectures. Only deep integration with hardware-close acceleration libraries ensures the necessary throughput:
- Optimised libraries: cuDNN, cuBLAS and NCCL accelerate mathematical primitives as well as lossless multi-GPU and multi-node communication.
- Inference acceleration: Tools such as Torch-TensorRT or TensorRT-LLM optimise trained models for production use.
- NGC containers: Pre-tested, monthly-updated container environments from the NVIDIA NGC Catalogue eliminate compatibility issues and accelerate deployment from the workstation to the data centre.
Successful AI projects require a closed-loop process comprising model training, optimisation and high-performance inference serving.
To prevent bottlenecks in data-intensive training pipelines or parallel inference requests, a tailor-made enterprise infrastructure is required. The frameworks must integrate seamlessly with the underlying hardware (such as modern GPU servers or workstations) in order to utilise the computing units to their full potential.
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- Which framework is currently the standard for new AI projects?
PyTorch has established itself as the undisputed standard for research and advanced AI development, whilst TensorFlow continues to be widely used in industrial production environments and established deployment set-ups.
- Why are specialised serving frameworks such as vLLM required for LLMs?
Traditional frameworks are primarily optimised for model training. Serving frameworks such as vLLM or SGLang manage video memory (VRAM) much more efficiently during text generation (including through PagedAttention), which massively increases the token throughput of language models.
- What role do older frameworks such as Caffe play in modern projects?
Earlier frameworks are not architecturally designed to meet the requirements of modern parallel workloads, tensor cores and large language models, and no longer play a role in current practice.