JAX
Composable transformations (grad, vmap, pmap) plus NumPy-like API for high-performance ML research on accelerators.
Why it is included
Powers many frontier research stacks (Flax, Haiku) and remains the clearest OSS path to XLA-level performance from Python.
Best for
Researchers and small teams optimizing custom models and scientific computing on TPUs/GPUs.
Strengths
- Functional AD
- SPMD scaling
- Strong Google ecosystem
Limitations
- Smaller application-dev ecosystem than PyTorch for generic product teams
Good alternatives
PyTorch · TensorFlow
Related tools
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PyTorch
Deep learning framework with strong research-to-production paths.
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TensorFlow
End-to-end platform for machine learning and deployment.
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Keras
High-level multi-backend deep learning API (TensorFlow, JAX, PyTorch) focused on ergonomics and fast iteration.
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vLLM
High-throughput LLM serving with PagedAttention, continuous batching, and OpenAI-compatible APIs for GPU clusters.
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SGLang
Structured generation language for fast serving: RadixAttention, constrained decoding, and multi-turn batching for frontier-class workloads.
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TensorRT-LLM
NVIDIA TensorRT–based library for optimized LLM inference on GPUs with multi-GPU and speculative decoding features.