vLLM vs TGI vs Ollama: Choosing Your LLM Serving Stack in 2026

\n## ⚡ Choosing Your LLM Serving Stack

The LLM inference landscape has matured significantly. Here’s an honest comparison of the three leading serving frameworks in 2026.

Quick Comparison

vLLM: The Performance King

Best for high-throughput production serving:

# Deploy with Docker
docker run --gpus all -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model meta-llama/Llama-3.1-70B-Instruct \
  --tensor-parallel-size 4 \
  --max-model-len 8192 \
  --enable-chunked-prefill \
  --gpu-memory-utilization 0.9

# Kubernetes deployment
helm install vllm vllm/vllm \
  --set model=granite-34b-code-instruct \
  --set tensorParallelism=2 \
  --set resources.limits.nvidia\.com/gpu=2

Choose vLLM when: Maximum throughput matters, multi-GPU serving, OpenAI API compatibility needed.

TGI: The Hugging Face Native

Best for Hugging Face model ecosystem:

docker run --gpus all -p 8080:80 \
  ghcr.io/huggingface/text-generation-inference:latest \
  --model-id meta-llama/Llama-3.1-8B-Instruct \
  --max-input-length 4096 \
  --max-total-tokens 8192 \
  --quantize awq

Choose TGI when: Using HF models, need built-in safety features (watermarking, content filtering), want HF ecosystem integration.

Ollama: The Developer’s Friend

Best for local development and edge deployment:

# Install and run — that's it
ollama run llama3.1:8b

# API usage
curl http://localhost:11434/api/generate -d '{
  "model": "llama3.1:8b",
  "prompt": "Write a Kubernetes deployment for nginx"
}'

Choose Ollama when: Local development, prototyping, edge deployment, teams without GPU infrastructure expertise.

Benchmark Results (A100 80GB)

Llama 3.1 8B, 512 input tokens, 256 output tokens:

My Recommendation

Production serving (high scale) → vLLM
Production serving (HF models) → TGI
Development / testing → Ollama
Edge / single-GPU → Ollama or vLLM
Multi-GPU inference → vLLM

Most organizations end up running Ollama for dev and vLLM for production. That’s the right call.

Need help choosing and deploying an LLM serving stack? I help organizations build production AI infrastructure. Get in touch.\n