A familiar pattern: the local AI pilot runs on one developer's laptop, Ollama is installed in ten minutes, answers come back smoothly. Then the plan is to roll it out to thirty colleagues, everyone queries at the same time, and suddenly every request hangs. That is not a bug. It is the difference between a tool for one user and a server for many.
We looked at Ollama recently from a different angle, when the model runner turned into a local agent. Today is not about new features. It is an older question that is turning practical for small and mid-sized firms right now: what carries local AI from one desk to a whole department?
From pilot to whole team
In a pilot, usually one person tests. They type a question, wait for the answer, type the next. That request-then-answer sequence is exactly where Ollama is at home. Simple install, sensible defaults, and it runs on a Mac Studio just as well as on a workstation with a single GPU.
Production looks different. Ten, twenty, fifty people reach for it across the day, often in waves, in the morning when they clear their inbox or late afternoon before they leave. What decides then is not the single answer but how many requests the system serves in parallel without the wait becoming unpleasant. This is where the tools part ways.
Why Ollama shines with one user
Ollama is built so that one request after another passes through cleanly. For a single user's day that is ideal. There is no reason to stand up anything more complicated for a developer working locally on code or text. If you are trying local AI, keep starting with Ollama or LM Studio. The mistake only appears when the pilot setup is declared the department server unchanged.
The reason is architectural. At its core Ollama handles requests one after another. If five arrive at once, four wait while one is served. With a single user that never shows. With twenty the wait becomes the bottleneck, and no tuning flag draws out what the design does not provide for.
What the Red Hat benchmark shows
How wide the gap gets under load was measured by Red Hat in August 2025 in a much-cited benchmark, and the community has confirmed the direction through 2026. On the same NVIDIA A100 card, across concurrency from one to 256 users, vLLM peaked at roughly 793 tokens per second and Ollama at about 41. That is close to nineteen times.
That number often travels without the part that matters. At a single user, the same benchmark puts both in a band of roughly 130 to 180 tokens per second, effectively level. The gap opens up only with concurrency: vLLM's throughput kept climbing with the number of users while Ollama's flattened almost at once. Latency told the same story. Under full load the 99th percentile came in around 80 milliseconds for vLLM and around 673 for Ollama, per the report.
In practice: if you serve one or two people today, you will barely feel a difference between the two. If you serve a department, the difference is almost all you feel.
Continuous batching, the real difference
Behind the number sits a technique called continuous batching, paired with memory-efficient handling of the KV cache. Put simply: vLLM packs many in-flight requests into one shared processing stream on the GPU and fills freed-up compute immediately with the next waiting request. Instead of a queue where each moves up alone, you get a conveyor belt that never stands empty.
That also explains why vLLM has no lead at one user. A conveyor belt carrying a single parcel is no faster than one pair of hands. The design only pays off once many parcels move at the same time. So the choice is not good against bad, it is one user against many.
A word on hardware: vLLM unfolds its advantage on a real server GPU with enough memory, classically NVIDIA. Ollama also runs on Apple Silicon, which is exactly why it is so convenient in a pilot. Planning the production step usually means planning a hardware step too.
Data protection does not change
Important for any firm under GDPR: the switch changes nothing about the data. Both tools run the model locally, the requests never leave the building. Choosing between Ollama and vLLM is a question of operations and throughput, not of sovereignty. If you run local inference for data-protection reasons, you keep that advantage either way. What frames it legally, we gathered on our data sovereignty page.
That is good news, because it keeps two questions apart. First: does the data stay in house. Then, and only then: does the technology carry the number of users. Local AI answers the first regardless of tool. The second decides the architecture.
Which tool for which phase
A workable path for a firm that takes local AI seriously usually looks like this:
- Pilot and single seat: Ollama or LM Studio. Fast to set up, good for trying models and workflows, ideal on a Mac Studio you already own.
- Small team, few concurrent users: Ollama can be enough when usage rarely comes in waves. Check honestly where the peaks fall.
- Department or whole company: vLLM on a dedicated GPU, or a comparable serving stack with batching. Here the heavier setup pays back through stable response times.
The mistake we see most is not the wrong tool in the pilot. It is the switch that never happens afterward. A setup meant for one person is declared the department solution, and then people hunt for the fault in the models when it sits in the build of the server.
For firms operating across the EU, the point cuts the same way in every market: a single-stream tool does not become a multi-user server by adding users to it. The serving layer has to be built for concurrency from the start.
The difference between a working pilot and a working operation is rarely the model. Most often it is the layer beneath it that decides whether twenty people can work at the same time.
Running local AI in a pilot and wondering what the step to the whole workforce looks like? Our pilot project settles architecture, hardware, and throughput on your real use case, with clear acceptance criteria. More on scope on the local AI page.