sglang and vllm
These are competitors offering different optimization approaches—vLLM prioritizes memory efficiency and throughput through PagedAttention, while SGLang emphasizes programmability and structured generation through its domain-specific language for LLM control flow.
About sglang
sgl-project/sglang
SGLang is a high-performance serving framework for large language models and multimodal models.
Implements RadixAttention for prefix caching, zero-overhead batch scheduling, and prefill-decode disaggregation to optimize inference latency and throughput. Supports tensor/pipeline/expert/data parallelism with structured output constraints via compressed finite state machines. Runs across NVIDIA, AMD, Intel, and Google TPU hardware with native integrations for reinforcement learning and post-training workflows.
About vllm
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Implements PagedAttention for efficient KV cache management and continuous request batching to maximize GPU utilization. Supports multiple quantization schemes (GPTQ, AWQ, INT4/8, FP8), speculative decoding, and tensor/pipeline parallelism across NVIDIA, AMD, Intel, and TPU hardware. Provides OpenAI-compatible API endpoints and integrates directly with Hugging Face models, including multi-modal and mixture-of-expert architectures.
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