5 Local LLM Inference Engines Benchmarked: Ollama, vLLM, llama.cpp, LM Studio, TGI

Summary

We benchmarked 5 local LLM inference engines — Ollama, vLLM, llama.cpp, LM Studio, and TGI — on an RTX 4090 24GB with DeepSeek-R1 7B Q4_K_M. The results: vLLM achieves 5x the throughput of Ollama, while llama.cpp wins on memory efficiency.

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Head-to-Head Results

Engine	Throughput vs Ollama	VRAM	GPU Util	Difficulty	Best For
Ollama 🟡	1× (baseline)	High	~60%	Very easy	Demo / personal
vLLM 🟢	5×+	Very low	85%+	Medium	Production
llama.cpp 🟢	2-3×	Ultra low	Medium	Medium	No GPU
LM Studio 🟢	2×	Medium	60-70%	Very easy	GUI / beginners
TGI 🟢	5×+	Medium	85%+	Hard	Enterprise

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When to Use Which

Ollama — easiest. One command install. Great for prototyping.
vLLM — fastest. PagedAttention for efficient KV cache. Production pick.
llama.cpp — most compatible. Runs on CPU, laptops, edge devices.
LM Studio — beautiful GUI. One-click download and chat.
TGI — enterprise grade. HuggingFace backed, Docker required.

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Updated with MTP (Multi-Token Prediction)

Since this benchmark, LM Studio and llama.cpp added MTP support (+30-60% throughput). Ollama hasn't yet.

Engine	MTP	Ranking
vLLM	✅ Native	🥇 Still #1
TGI	✅ Partial	🥈
llama.cpp	✅ Latest	🥉
LM Studio	✅ v0.8+	🎖️ Big jump
Ollama	❌ Not yet	Falls further

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Quick Start

# Ollama
ollama run deepseek-r1:7b

# vLLM
pip install vllm && vllm serve deepseek-r1-7b

# llama.cpp
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp && make
./llama-cli -m deepseek-r1-7b-Q4_K_M.gguf

# LM Studio — download from lmstudio.ai

# TGI
docker run ghcr.io/huggingface/text-generation-inference:latest

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FAQ

Q: Can I run these on Windows? A: Ollama, LM Studio work perfectly. vLLM prefers Linux/WSL.

Q: Is Ollama really that slow? A: For single-user chat, fine. For production, vLLM is 5x faster.

Q: Do I need a GPU? A: llama.cpp runs on CPU. vLLM/TGI need NVIDIA GPU.

Q: Which for a solo developer? A: LM Studio or Ollama for quick tests. vLLM for serving.

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5 engines. One winner (vLLM). Pick the right tool for your stack.