vLLM vs TGI vs Ollama 2026: LLM Inference Comparison

Three tools, three use cases. vLLM is a production inference engine. TGI is Hugging Face’s optimized server. Ollama is a desktop app for running models locally. They overlap, but they target different audiences.

Quick comparison

Architecture

vLLM

vLLM’s core innovation is PagedAttention — managing KV cache like virtual memory pages:

# Start vLLM server
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3-8B-Instruct \
  --tensor-parallel-size 2 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.9

• KV cache is non-contiguous — no memory waste from fragmentation
• Continuous batching adds new requests without waiting for current batch
• Prefix caching for repeated system prompts
• Speculative decoding for faster generation

TGI (Text Generation Inference)

TGI is Hugging Face’s production server with Flash Attention and token streaming:

# Start TGI server
docker run --gpus all -p 8080:80 \
  -v /models:/data \
  ghcr.io/huggingface/text-generation-inference:latest \
  --model-id meta-llama/Llama-3-8B-Instruct \
  --num-shard 2 \
  --max-input-length 4096 \
  --max-total-tokens 8192

• Flash Attention 2 for memory-efficient attention
• Speculation for faster generation
• Grammar-constrained generation (JSON output)
• Watermarking support
• Native Hugging Face Hub integration

Ollama

Ollama is a desktop application that downloads and runs models with one command:

# Install
curl -fsSL https://ollama.com/install.sh | sh

# Run a model (downloads automatically)
ollama run llama3

# API server (OpenAI-compatible)
ollama serve
curl http://localhost:11434/v1/chat/completions \
  -d '{"model": "llama3", "messages": [{"role": "user", "content": "Hello"}]}'

• GGUF quantization (runs large models on consumer hardware)
• Apple Silicon Metal acceleration
• CPU-only inference for machines without GPUs
• Modelfile for custom model configurations
• Automatic model management (download, update, delete)

Performance benchmarks

Measured on 1x A100 80GB, Llama 3 8B Instruct, 128 concurrent users:

TTFT = Time to First Token. ITL = Inter-Token Latency.

vLLM leads in throughput due to PagedAttention’s superior memory utilization. Ollama is not designed for concurrent serving — it excels at single-user interactive use.

GPU requirements

Running Llama 3 8B

Running Llama 3 70B

Kubernetes deployment

vLLM on Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-llama3
spec:
  replicas: 2
  template:
    spec:
      containers:
        - name: vllm
          image: vllm/vllm-openai:latest
          args:
            - --model=meta-llama/Llama-3-8B-Instruct
            - --tensor-parallel-size=1
            - --gpu-memory-utilization=0.9
          resources:
            limits:
              nvidia.com/gpu: 1
          ports:
            - containerPort: 8000

TGI on Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: tgi-llama3
spec:
  replicas: 2
  template:
    spec:
      containers:
        - name: tgi
          image: ghcr.io/huggingface/text-generation-inference:latest
          args:
            - --model-id=meta-llama/Llama-3-8B-Instruct
            - --num-shard=1
          resources:
            limits:
              nvidia.com/gpu: 1
          ports:
            - containerPort: 80

Both work well with NVIDIA NIM, KServe, and HPA for autoscaling.

Decision guide

Choose vLLM when:

• Production inference at scale — highest throughput, best memory efficiency
• You need multi-GPU serving (tensor/pipeline parallelism)
• OpenAI-compatible API is required (drop-in replacement)
• You are deploying on Kubernetes or OpenShift
• Maximum tokens per second per dollar is the goal

Choose TGI when:

• You are in the Hugging Face ecosystem (Hub, Inference Endpoints)
• You need grammar-constrained generation (guaranteed JSON output)
• Intel Gaudi hardware support is needed
• You want managed deployment via Hugging Face Inference Endpoints
• Token watermarking is a requirement

Choose Ollama when:

• Local development and experimentation on your laptop
• You want to run models on Apple Silicon (M1/M2/M3/M4) with Metal
• No GPU available — Ollama runs on CPU with GGUF quantization
• You want the simplest possible setup — one command to start
• Privacy — all inference runs locally, no data leaves your machine
• Building prototypes before deploying to production with vLLM/TGI

Related

• NVIDIA NIM Support Matrix
• NVIDIA NIM Model Profiles
• Model Serving on OpenShift AI with vLLM
• LLM Quality vs Cost vs Safety
• NVIDIA Dynamo Inference Framework