vLLM vs TGI vs llama.cpp: Best Local LLM Serving Framework 2026

Choosing the right serving framework can double your throughput. This comparison covers vLLM, Hugging Face TGI, and llama.cpp for local deployment in 2026.

Framework Overview

Framework	Best For	Hardware	Quantization
vLLM	High throughput, batching	NVIDIA/AMD GPU	AWQ, GPTQ, FP8
TGI	Production APIs	NVIDIA GPU	GPTQ, AWQ, BNB
llama.cpp	Single user, CPU/Mac	Everything	GGUF (Q2–Q8)

Throughput Benchmarks (Llama 3 8B, RTX 4090)

Framework	1 User t/s	8 Users t/s total	TTFT (ms)
vLLM (FP16)	78	420	45
TGI (FP16)	72	380	52
llama.cpp (Q4)	128	128*	20

*llama.cpp is single-batch; total throughput doesn't scale with users. For multi-user deployments, vLLM's continuous batching makes it 3–5x more efficient.

More detailed benchmarks across GPU tiers are at LLMHardware.io's serving framework comparison.

When to Use Each

Use vLLM when serving multiple users, running a shared API endpoint, or needing maximum throughput with NVIDIA hardware. Its PagedAttention algorithm handles concurrent requests far more efficiently than sequential serving.

Use TGI when you need the easiest Docker deployment, built-in streaming support, and are already in the Hugging Face ecosystem.

Use llama.cpp for single-user local setups, Apple Silicon, CPU inference, or when you need the widest model compatibility and smallest memory footprint via GGUF quantization.

Quick Start: vLLM with OpenAI-Compatible API

pip install vllm
python -m vllm.entrypoints.openai.api_server   --model Qwen/Qwen2.5-7B-Instruct   --port 8000   --max-model-len 32768

# Test
curl http://localhost:8000/v1/chat/completions   -H "Content-Type: application/json"   -d '{"model":"Qwen/Qwen2.5-7B-Instruct","messages":[{"role":"user","content":"Hello"}]}'