Comparison of Features and Use Cases for 8 LLM Inference Stacks

This analysis compares the features and target workloads of major open-source LLM inference stacks, including llama.cpp, TensorSharp, Ollama, MLX-LM, MLC LLM, vLLM, SGLang, TensorRT-LLM, and TGI. The focus is on suitability rather than performance rankings, as each stack serves different purposes such as portable local execution, GPU cluster throughput, and model management.

Key Features by Stack

• llama.cpp: A C/C++-based portable GGUF inference engine that supports a wide range of backends from CPU to CUDA and Metal, regarded as the benchmark for local inference.
• Ollama: A Go-based local model installation and management tool that, unlike llama.cpp, emphasizes user experience (UX) and API convenience.
• vLLM & SGLang: Specialized for high-throughput serving for multiple users in data center GPU environments, offering advanced scheduling features such as continuous batching and PagedAttention/RadixAttention.
• TensorSharp: A .NET-specific stack designed to embed inference capabilities directly within C#/.NET applications.
• MLX-LM & MLC LLM: Respectively optimized for Apple Silicon and strong in compiled inference for heterogeneous hardware such as browsers and mobile devices.

Selection Guide by Workload

For single-user desktops or offline devices, llama.cpp, Ollama, and MLX-LM are suitable, while for multiple concurrent users and accelerator server environments, the distributed and batching capabilities of vLLM or SGLang are essential. When implementing agents, combining with external orchestration frameworks (such as LangGraph) is more effective for managing long-running workflows than the inference engine itself. Benchmark figures vary significantly depending on prompt length, quantization, and hardware configuration, so simple comparisons without context should be avoided.