vLLM vs TGI vs llama.cpp: Which Server Should You Use in 2025?

Choosing the right LLM serving framework is a consequential infrastructure decision. vLLM, Text Generation Inference (TGI), and llama.cpp server each occupy different niches — and using the wrong tool for your workload means leaving significant performance on the table. Here's the definitive comparison for 2025.

Quick guide: Single user, maximum speed: llama.cpp. Small team, NVIDIA only: vLLM. Mixed hardware or maximum model compatibility: TGI. For hardware that each framework runs best on, see LLMHardware.io's server GPU guide.

llama.cpp: The Universal Foundation

llama.cpp runs on everything — NVIDIA, AMD, Apple Silicon, CPU-only, even Raspberry Pi. GGUF quantization provides fine-grained quality/size tradeoffs. The server mode's --parallel flag handles moderate concurrency. Limitations: lower multi-user throughput than vLLM/TGI due to less sophisticated batching, no PagedAttention. Best for: personal use, cross-platform deployment, models not in HuggingFace format.

vLLM: Maximum Throughput on NVIDIA

vLLM's PagedAttention and continuous batching deliver 10-20x higher throughput than naive serving for multi-user scenarios. OpenAI API compatibility is excellent. Supports AWQ, GPTQ, FP8, and FP16 formats. Limitations: NVIDIA CUDA only (AMD support experimental), requires HuggingFace-format models, more complex setup. Best for: team deployments, production inference APIs, applications with many simultaneous users.

TGI: Production Grade with HuggingFace Integration

TGI matches vLLM on throughput with slightly broader model support and simpler Docker deployment. Unique features: token streaming is more robust, guided generation (structured JSON output) is built in, and multi-node tensor parallelism is production-tested. AMD ROCm support is more mature than vLLM's. Best for: production deployments where structured output matters, multi-node scaling, AMD GPU clusters. For most NVIDIA single-node deployments, TGI and vLLM are effectively equivalent.

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