NVIDIA Triton Inference Server
Multi-framework inference server for TensorRT, ONNX, PyTorch, Python backends—dynamic batching, ensembles, and GPU sharing.
Why it is included
Widely used OSS serving layer in NVIDIA-centric production ML and LLM hosting stacks.
Best for
GPU datacenters needing one serving plane for heterogeneous model formats.
Strengths
- Multi-backend
- Batching
- K8s integrations
Limitations
- Best story on NVIDIA; other accelerators need extra work
Good alternatives
vLLM · TorchServe · BentoML
Related tools
AI & Machine Learning
TensorRT-LLM
NVIDIA TensorRT–based library for optimized LLM inference on GPUs with multi-GPU and speculative decoding features.
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vLLM
High-throughput LLM serving with PagedAttention, continuous batching, and OpenAI-compatible APIs for GPU clusters.
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ONNX Runtime
Cross-platform inference accelerator for ONNX models: CPU, GPU, and mobile execution providers with graph optimizations.
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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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rtp-llm
Alibaba’s high-performance LLM inference engine (CUDA-focused) for production serving of diverse decoder architectures.
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TensorFlow Serving
Flexible, high-performance serving system for TensorFlow (and related) models with versioning, batching, and gRPC/REST.