Alternatives to Text Generation Inference

vLLM is an Apache-2.0 LLM inference and serving engine focused on high-throughput self-hosted model APIs. It combines PagedAttention, continuous batching, prefix caching, quantization options, OpenAI-compatible serving, structured outputs, metrics, Docker/Kubernetes deployment guidance and integrations with agent and LLM frameworks.

SGLang is an open-source serving framework for large language and vision-language models, designed for low latency and high throughput. It features RadixAttention for automatic KV cache reuse, compressed finite state machines for fast structured output generation, continuous batching, and tensor parallelism. With over 25,000 GitHub stars, it supports models like LLaMA, Mistral, Qwen, and Gemma on NVIDIA and AMD GPUs.

TensorRT-LLM is NVIDIA's open-source library for optimizing LLM inference on NVIDIA GPUs. It provides kernel fusion, quantization (FP8, INT4, INT8), KV cache optimization, and in-flight batching to maximize throughput. Supports multi-GPU and multi-node setups with tensor and pipeline parallelism, and integrates with Triton Inference Server for production deployment of models like LLaMA, GPT, Mistral, and Qwen.