Ollama and vLLM are both open-source inference engines for running models locally. They solve the same base problem — serving an LLM on your own hardware — but are built for different operating points. Ollama is the choice for straightforward deployment. vLLM is the choice when you need extreme throughput or are running many concurrent requests. Each is the right tool in its context.
Ollama: built for simplicity and model management
Ollama is a single binary that handles model download, caching, quantisation, and serving. You run ollama pull mistral and a few seconds later the model is cached locally and ready to serve. The interface is simple: an HTTP endpoint that accepts prompts and returns completions. Configuration is minimal. It has sensible defaults and the tool stays out of your way.
Ollama manages model versions and quantisations. When a new version of a model is available, you can upgrade with a single command. It handles different precision levels automatically — if a model offers 4-bit and 8-bit variants, you can choose and Ollama fetches the right one.
Deployment is straightforward. A team with one or two GPUs can run Ollama on a single box and point their applications at it. There is no complex orchestration, no configuration to tune for throughput, no scaling strategy. It works.
For a company running a handful of concurrent requests, this simplicity is a major win. Less to maintain, fewer things that can break, easier to debug.
vLLM: built for throughput and concurrency
vLLM is a different kind of engine. It is designed for situations where you must serve many concurrent requests efficiently. It uses advanced scheduling (continuous batching) to queue requests and process them together, minimising the idle time on GPU hardware. It also handles distributed serving across multiple GPUs or machines with less overhead than simple partitioning.
vLLM exposes lower-level configuration: how to batch requests, memory allocation, GPU management. You have more knobs to turn and more things you can optimise. This is power, but it is also responsibility. Deploying vLLM means understanding these parameters and tuning them for your workload.
vLLM also handles edge cases that Ollama does not: speculative decoding (a technique to reduce latency), custom CUDA kernels for inference, and structured output formats. For a team with specialist inference engineers, these capabilities unlock real improvements.
When to choose Ollama
Choose Ollama if your workload is:
- A small number of concurrent requests — a team asking questions interactively, not a pipeline hammering the endpoint.
- Requests arrive sporadically, so the GPU is idle much of the time anyway.
- Your team does not have someone dedicated to inference infrastructure.
- You want the model to just work with minimal configuration.
- Model management and rapid iteration matter (testing different quantisations or versions).
Most companies starting with local models fall into this category. A team of fifty people using a local LLM for research and code completion generates sporadic requests that a single Ollama instance handles easily.
When to choose vLLM
Choose vLLM if:
- You have dozens or hundreds of concurrent requests, often from batch jobs or integrated applications.
- Throughput matters more than simplicity.
- You have engineers who understand inference performance and are willing to tune it.
- You are running on multiple GPUs or machines.
- You need advanced features like continuous batching or speculative decoding.
A large organisation using local models as a core component of its product — embedded in search, in code generation tooling, or in a document processing pipeline — likely needs vLLM's throughput.
The real tradeoff: complexity versus capability
This is not a raw performance comparison. vLLM is faster than Ollama at high concurrency because it is designed for it. But Ollama is not slow at its design point. A single instance serving ten concurrent requests does its job competently.
The real cost is operational. vLLM requires understanding how inference works: batching, scheduling, GPU utilisation. Ollama requires none of that. You trade complexity for capability, and you should only pay that cost if you actually need the capability.
A common path is to start with Ollama, measure your actual load, and only move to vLLM if profiling shows you need it. Do not optimise for a scenario you do not have.
Multi-model serving
If you need multiple models at once (one for code, one for reasoning, one for embeddings), neither engine handles hot-swapping perfectly. Ollama handles this more gracefully than most: a single server exposes every model you have pulled, and it loads and unloads them as requests arrive, keeping several resident at once if VRAM allows. The cost is a delay on the first request to a model that is not currently loaded. vLLM is generally deployed one model per instance, so multiple models means multiple instances and a router in front of them.
For your decision
Start by measuring your actual concurrency. Count the simultaneous requests your system generates at peak, and note how long users are willing to wait. Interactive use by a team, where requests arrive in ones and twos, is squarely Ollama's design point. Sustained parallel load from an automated pipeline is squarely vLLM's. The awkward middle is genuinely a judgement call, and the honest way to settle it is to run your own workload against Ollama first and see whether queueing becomes a problem before you take on the operational cost of vLLM.
Measure before deciding. Do not assume you need the more complex tool.