vLLM vs Ollama: Which Inference Engine Should You Use?

vLLM vs Ollama at a Glance

Choosing the right inference engine is one of the most consequential decisions when deploying an LLM on dedicated GPU hosting. vLLM and Ollama are the two most popular options, but they solve fundamentally different problems. vLLM is built for maximum throughput in production. Ollama is built for ease of use in development.

This guide compares both engines across performance, memory usage, API design, and deployment complexity using data from our open source LLM hosting servers. If you’re looking for a step-by-step setup guide before diving into this comparison, start with our self-hosting an LLM guide.

Architecture & Design Philosophy

vLLM was created to solve the GPU memory waste problem in LLM serving. Its core innovation, PagedAttention, manages the KV-cache the way an operating system manages virtual memory — allocating non-contiguous blocks on demand. This eliminates the memory fragmentation that plagues naive implementations and enables continuous batching, where new requests are inserted into running batches without waiting for all current requests to finish.

On a dedicated vLLM hosting setup, this means you can serve 3-5x more concurrent users on the same GPU compared to a sequential serving approach.

Ollama takes a different approach. Built on top of llama.cpp, it wraps model downloading, quantisation, and serving into a single command-line tool. Think of it as Docker for LLMs — you ollama pull a model and ollama run it. The trade-off is that Ollama processes requests sequentially, which limits throughput under concurrent load.

Throughput Benchmarks

We benchmarked both engines on an RTX 3090 (24 GB) running Mistral 7B Instruct, measuring output tokens per second at varying concurrency levels. All tests ran on identical dedicated GPU servers in our UK datacenter.

At a single concurrent user, the gap is modest — Ollama’s llama.cpp backend is well-optimised for single-stream inference. But as concurrency rises, vLLM’s continuous batching pulls dramatically ahead. At 32 concurrent users, vLLM delivers over 9x the total throughput.

For the full GPU-by-GPU breakdown across more models, see our tokens per second benchmarks. You can also calculate your per-token costs at these throughput levels using the LLM cost calculator.

Memory Efficiency & Model Support

VRAM efficiency determines which models fit on your GPU and how many concurrent requests you can handle. The two engines take different approaches to memory management:

vLLM’s PagedAttention keeps KV-cache memory usage proportional to actual sequence length. Ollama pre-allocates memory for the maximum context length, wasting VRAM on shorter conversations. On a 24 GB card, this means vLLM can maintain roughly 3x as many active conversations.

For model-specific VRAM requirements, our best GPU for LLM inference guide lists exact figures for every popular model. When choosing between GPU tiers, the RTX 3090 vs RTX 5090 comparison covers how VRAM differences affect model selection.

API Compatibility

If you’re replacing OpenAI API calls with a self-hosted model, API compatibility matters. Here’s how each engine handles it:

vLLM natively exposes an OpenAI-compatible API at /v1/chat/completions and /v1/completions. In most cases, switching from OpenAI to vLLM requires only changing the base URL:

# Python — swap OpenAI for self-hosted vLLM
from openai import OpenAI

client = OpenAI(
    base_url="http://your-gpu-server:8000/v1",
    api_key="not-needed"
)

response = client.chat.completions.create(
    model="mistralai/Mistral-7B-Instruct-v0.3",
    messages=[{"role": "user", "content": "Hello"}]
)

Ollama has its own API format at /api/chat and /api/generate. It also offers an OpenAI-compatible endpoint at /v1/chat/completions, but support for advanced parameters like logprobs, tools, and response_format is limited.

For teams building production AI APIs, vLLM’s deeper OpenAI compatibility reduces migration effort and avoids edge-case bugs.

When to Use Each Engine

Choose vLLM when:

• Serving multiple concurrent users or high request volumes
• Building a production API that needs OpenAI compatibility
• Running FP16 or AWQ/GPTQ quantised models from Hugging Face
• Deploying across multi-GPU clusters with tensor parallelism
• Maximising tokens per second per dollar on your hardware

Choose Ollama when:

• Prototyping or testing models quickly before committing to production
• Running a single-user local assistant or development tool
• Working with GGUF-quantised models from the Ollama library
• Prioritising setup simplicity over throughput
• Building internal tools where concurrent load is minimal

Many teams use both: Ollama for rapid experimentation and vLLM for production serving. Our LLM hosting guides cover deployment patterns for each scenario.

Verdict

For production workloads, vLLM is the clear winner. Its continuous batching, PagedAttention memory management, and native OpenAI API compatibility make it the right choice for any deployment serving more than a handful of users. The throughput advantage at scale — up to 9x on identical hardware — translates directly into lower cost per token and higher capacity.

For development and single-user deployments, Ollama is hard to beat. A single curl | sh install followed by ollama run mistral gets you from zero to running model in under two minutes. That speed of iteration matters when you’re evaluating models or building prototypes.

Whichever engine you choose, running it on dedicated GPU hosting gives you bare-metal performance, full root access, and fixed monthly pricing. Use our cost per million tokens tool to estimate your savings versus hosted API providers, or explore provider alternatives in our Together AI alternatives guide.