The question is not whether to run a local LLM — by mid-2026, most European SMBs evaluating on-premise AI have already run Ollama on at least one machine. The question is whether Ollama is still the right choice once the pilot ends and the whole team needs access.
A single benchmark number has reshaped that conversation: 19×.
What the 2026 Benchmarks Show
According to a benchmark published by Red Hat in mid-2025 — one of the most widely cited figures in the local LLM community throughout 2026 — vLLM reached a peak throughput of 793 tokens per second on the same hardware where Ollama delivered 41 tokens per second. The gap, measured under concurrent-load conditions, is approximately 19×.
The reason is architectural. Ollama processes requests sequentially: one user, one request, one response. That design is simple, robust, and fast for a single user — but it creates a queue under concurrent load. vLLM uses PagedAttention, a technique that manages the KV-cache in dynamic memory pages (analogous to OS virtual memory management) and enables true request batching. Throughput scales with load rather than degrading with it.
For teams experiencing 20–30 second response times on a shared Ollama endpoint, this is the explanation — and the benchmark provides the directional signal for what switching would achieve.
Ollama in 2026: Strengths and Limits
Ollama has become the default installation for local AI development. A single command installs the server, another pulls the model — Llama 3.3, Qwen 2.5, Mistral, Gemma 4, or any GGUF-compatible model. The OpenAI-compatible REST API on port 11434 makes integration into existing applications straightforward.
Where Ollama excels:
- One-command setup on macOS, Linux, and Windows
- Native MLX backend for Apple Silicon (Mac Studio M4 Max, M3 Ultra) — competitive single-user throughput
- No Python environment management or GPU drivers required
- Deep integration with Open WebUI, LangChain, Continue.dev, and most LLM frameworks
- Works without a GPU — CPU inference is available, though slow
Where Ollama falls short:
- Sequential request queue by design — no batching
- KV-cache is not shared across sessions
- Throughput degrades sharply above two or three concurrent users
- No built-in autoscaling or load distribution
For a developer evaluating models or building a workflow in isolation, Ollama is close to ideal. For a ten-person team hitting the same endpoint simultaneously, the queue becomes the bottleneck regardless of hardware quality.
vLLM in 2026: Production Serving at Scale
vLLM emerged from academic research and has matured into the de facto standard for serving open-weight models at production scale. Based on practitioner reports and community observations, it is now used in production API infrastructure across a wide range of organisations.
Where vLLM excels:
- Scales from 5 to 100+ concurrent users without proportional latency growth
- Drop-in OpenAI API replacement — no application code changes required when migrating from Ollama
- Supports quantised models (GPTQ, AWQ, FP8) for VRAM efficiency
- Compatible with Llama 3.3, Qwen 2.5, Mistral, DeepSeek R1, Gemma 4, and most open-weight model families
- Active open-source development with frequent releases and a large community
Where vLLM falls short:
- Requires Linux with a CUDA-compatible NVIDIA GPU for production performance
- Setup involves Python environment management and CUDA driver configuration
- Apple Silicon support is limited compared to Ollama's native MLX integration
This creates a clear hardware split. Mac-based teams get the best single-user performance from Ollama with its MLX backend. Organisations with a dedicated NVIDIA GPU server — or planning to procure one — get substantially better multi-user throughput from vLLM.
Hardware Reality for SMBs
The 793 vs. 41 TPS benchmark figures assume GPU hardware in a server context. On Apple Silicon, the picture is different but still instructive.
Based on practitioner-reported results, Ollama on Apple Silicon hardware delivers the following approximate throughput on 4-bit quantised models at single-user load:
- Mac Mini M4 Pro (24–48 GB): models up to 14B, approx. 20–50 tok/s
- Mac Studio M4 Max (96–128 GB): models up to 70B, approx. 25–60 tok/s
- Mac Studio M3 Ultra (192 GB): 70B–105B models, 30+ tok/s
These figures apply to a single concurrent request. With five simultaneous users on the same instance, effective per-user throughput divides accordingly.
For SMBs that want to serve their whole team without procuring an NVIDIA GPU, the practical solution is: one central Mac Studio running Ollama as the backend, with Open WebUI as the shared interface. For workloads that are not highly time-sensitive — document search, translation, asynchronous summarisation — that architecture handles four to six concurrent users well.
Decision Framework
| Scenario | Recommended tool |
|---|---|
| Single developer, model evaluation | Ollama |
| Small team on Apple Silicon, up to 4 concurrent users | Ollama + Open WebUI |
| 5–50 concurrent users, NVIDIA GPU server | vLLM |
| RAG system or shared-context chatbot | SGLang or vLLM |
| Best Apple Silicon throughput for individual use | Ollama with MLX backend |
The decision is rarely all-or-nothing. Many teams run Ollama on developer laptops and a central Mac Studio for team access — and migrate to vLLM when a GPU server becomes available or user volume grows past the Ollama concurrency limit.
Privacy as the Common Thread
Both frameworks process all inference locally. No prompt, no token, no response is transmitted to an external service. That is not a privacy setting — it is the default operating mode. For European businesses handling personal data, client-confidential documents, or regulated records, this addresses Article 25 GDPR (data protection by design and by default) at the infrastructure level rather than through software controls on top of cloud-based services.
For a broader overview of what local AI means for data sovereignty and GDPR compliance, our dedicated page walks through the practical implications.
Where to Go from Here
If your team is currently running Ollama and asking whether it is the right foundation for a wider rollout — or if you want to understand what switching to vLLM would require in terms of hardware and setup — a structured pilot project is the most efficient path forward.
We help with hardware sizing, framework selection, and initial deployment. Our local AI overview outlines the full approach, and our pilot project programme covers the practical steps from evaluation to production.