vLLM 검토

TGI is a production-ready inference toolkit designed to efficiently scale LLM inference across many GPUs and nodes, with deep integration into the Hugging Face model ecosystem.

Similar to vLLM, TGI focuses on high-throughput LLM serving with features like smart batching and quantization. TGI is often favored by enterprises using Hugging Face models for its robust orchestration and ecosystem compatibility, while vLLM is known for its PagedAttention mechanism and continuous batching for superior memory efficiency and throughput.

TensorRT-LLM is a library from NVIDIA that maximizes performance for LLM inference on NVIDIA GPUs through low-level optimizations and hardware-specific acceleration.

While vLLM offers broad hardware support, TensorRT-LLM is highly specialized for NVIDIA GPUs, aiming for the absolute highest performance in NVIDIA-centric environments. This specialization can lead to superior speeds on compatible hardware but may offer less flexibility for heterogeneous infrastructure compared to vLLM's wider compatibility.

Ollama simplifies the local deployment, management, and running of large language models on personal machines, supporting both CPUs and Apple Silicon GPUs with minimal setup.

Ollama is geared towards ease of use for local, personal, or small-scale LLM deployments, making it accessible for experimentation. In contrast, vLLM is optimized for high-throughput, production-grade GPU serving, focusing on advanced memory management and scaling for demanding workloads.

SGLang is an inference framework designed to support high-performance LLM serving and structured generation workflows, emphasizing flexibility in how prompts and generation pipelines are structured.

SGLang focuses on optimizing prompt and generation execution, which can be particularly useful for advanced agentic applications and multimodal tasks. While vLLM excels in raw throughput and memory efficiency, SGLang provides more control over the generation process, complementing vLLM's strengths in different use cases.