vLLM vs Ollama: Choosing the Right LLM Inference Tool

Both vLLM and Ollama can run the same models — LLaMA 3, Mistral, Qwen, Gemma — and both expose an OpenAI-compatible API. The similarity stops there. The design goals, architecture, and appropriate use cases are fundamentally different.

vLLM was built to serve LLM inference at production scale, extracting maximum throughput from GPU hardware. Ollama was built to make running models on a local machine as simple as ollama run llama3. If you deploy Ollama in production expecting it to handle concurrent users, you'll hit throughput limits quickly. If you ask engineers to install vLLM locally to prototype, you'll spend an afternoon debugging CUDA dependencies.

vLLM

What Makes It Fast

vLLM's core contribution is PagedAttention — a KV cache memory management technique borrowed from OS virtual memory paging. The key-value cache for attention layers is the primary GPU memory bottleneck in LLM serving. Without PagedAttention, the KV cache is pre-allocated per request in a single contiguous block, leading to significant internal and external fragmentation. PagedAttention allocates KV cache in fixed-size pages and maps them non-contiguously, similar to how an OS manages RAM.

The result: 2–4x higher throughput and better GPU utilization compared to naive inference implementations, at the same latency.

Continuous batching is the other key feature. Instead of forming batches at request ingestion time and waiting for the whole batch to finish, vLLM continuously adds new requests to running batches as tokens are generated. This keeps GPU utilization high even with variable request lengths and arrival times.

Deployment

1pip install vllm
2
3# Start a server (requires NVIDIA GPU)
4python -m vllm.entrypoints.openai.api_server \
5  --model meta-llama/Llama-3.1-8B-Instruct \
6  --tensor-parallel-size 1 \
7  --gpu-memory-utilization 0.90

The server starts on port 8000 and exposes OpenAI-compatible /v1/chat/completions and /v1/completions endpoints. Any client using the OpenAI SDK works unchanged:

1from openai import OpenAI
2
3client = OpenAI(base_url="http://localhost:8000/v1", api_key="none")
4response = client.chat.completions.create(
5    model="meta-llama/Llama-3.1-8B-Instruct",
6    messages=[{"role": "user", "content": "Explain PagedAttention"}],
7)

For multi-GPU inference, set --tensor-parallel-size to the number of GPUs:

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.1-70B-Instruct \
  --tensor-parallel-size 4 \
  --dtype bfloat16

Kubernetes Deployment

1apiVersion: apps/v1
2kind: Deployment
3metadata:
4  name: vllm-server
5spec:
6  replicas: 1
7  template:
8    spec:
9      containers:
10        - name: vllm
11          image: vllm/vllm-openai:latest
12          args:
13            - "--model"
14            - "meta-llama/Llama-3.1-8B-Instruct"
15            - "--gpu-memory-utilization"
16            - "0.90"
17          resources:
18            limits:
19              nvidia.com/gpu: "1"
20          env:
21            - name: HUGGING_FACE_HUB_TOKEN
22              valueFrom:
23                secretKeyRef:
24                  name: hf-token
25                  key: token

Ollama

What Makes It Simple

Ollama bundles a model registry, a runtime (backed by llama.cpp), and an HTTP server into a single binary. You pull models like Docker images and run them instantly.

1# Install
2curl -fsSL https://ollama.ai/install.sh | sh
3
4# Pull and run a model
5ollama run llama3.2
6
7# Or use the API
8curl http://localhost:11434/api/chat -d '{
9  "model": "llama3.2",
10  "messages": [{"role": "user", "content": "Hello"}]
11}'

Ollama uses llama.cpp under the hood, which means:

• It runs on CPU (slowly)
• It uses Metal on Apple Silicon (fast on M-series Macs)
• It uses CUDA on NVIDIA GPUs
• It runs on consumer hardware without enterprise GPU management

The OpenAI-compatible endpoint works the same way:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")

Modelfiles

Ollama has a Modelfile concept — similar to a Dockerfile — for customizing model behavior:

FROM llama3.2
SYSTEM "You are a senior platform engineer. Answer questions about Kubernetes, AWS, and DevOps concisely and practically."
PARAMETER temperature 0.3
PARAMETER num_predict 512

ollama create platform-engineer -f Modelfile
ollama run platform-engineer

This is useful for packaging specialized system prompts into named models for team use.

Head-to-Head Comparison

When to Use Which?

Use vLLM when:

• You're serving concurrent users in production
• You have NVIDIA GPU infrastructure (A10G, A100, H100)
• Throughput and GPU utilization matter
• You're building a service that needs to handle dozens of requests/second

Use Ollama when:

• You're prototyping locally
• Your team needs to run models without GPU setup friction
• You're on Apple Silicon and want fast local inference
• You're building a demo or internal tool with low concurrent load (< 5 req/s)

Don't use Ollama for:

• High-concurrency production workloads — it doesn't implement continuous batching and will queue requests under load
• Workloads that need tensor parallelism across multiple GPUs

Don't use vLLM for:

• Local development on a laptop without a CUDA GPU
• Situations where model download and CUDA setup complexity isn't worth it for a prototype

The Hybrid Pattern

The practical pattern for teams building LLM-backed services:

1. Develop locally with Ollama — fast iteration, no GPU needed, OpenAI-compatible so your code works unchanged
2. Stage and produce with vLLM — swap base_url in your client config, get production-grade throughput

Since both expose an OpenAI-compatible API, the transition is a one-line config change. Your application code doesn't need to know which backend is running.

1import os
2from openai import OpenAI
3
4client = OpenAI(
5    base_url=os.getenv("LLM_BASE_URL", "http://localhost:11434/v1"),
6    api_key=os.getenv("LLM_API_KEY", "ollama"),
7)

Set LLM_BASE_URL to your vLLM endpoint in staging/production, leave it as Ollama default in development. Same code, different throughput characteristics.