SGLang
Structured generation language for fast serving: RadixAttention, constrained decoding, and multi-turn batching for frontier-class workloads.
Why it is included
Active research-to-production path competing with vLLM on latency and structured output.
Best for
Labs pushing structured LLM programs and high-QPS chat on GPUs.
Strengths
- Structured gen
- Performance focus
- OpenAI-style endpoints
Limitations
- Newer ecosystem than vLLM for some operators
Good alternatives
vLLM · TensorRT-LLM
Related tools
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vLLM
High-throughput LLM serving with PagedAttention, continuous batching, and OpenAI-compatible APIs for GPU clusters.
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PyTorch
Deep learning framework with strong research-to-production paths.
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rtp-llm
Alibaba’s high-performance LLM inference engine (CUDA-focused) for production serving of diverse decoder architectures.
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TensorRT-LLM
NVIDIA TensorRT–based library for optimized LLM inference on GPUs with multi-GPU and speculative decoding features.
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NVIDIA Triton Inference Server
Multi-framework inference server for TensorRT, ONNX, PyTorch, Python backends—dynamic batching, ensembles, and GPU sharing.
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Ollama
Local LLM runner and model library with simple CLI and API for workstation inference.