vLLM beats Ollama by 20x once you hit high concurrency

Stop treating vLLM and Ollama as interchangeable tools. While both get models running on your hardware, they are architecturally opposite. One is a precision tool for local experimentation, and the other is a heavy-duty engine for serving hundreds of users simultaneously. If you're building an AI workflow that needs to scale, choosing the wrong one will lead to a massive performance bottleneck the moment your user count climbs past five people.

The Architectural Divide

vLLM is a full-blown production serving engine. It doesn't just "run" a model; it manages the GPU with an intensity similar to an operating system. The secret sauce is PagedAttention. In standard inference, the KV cache (which stores previous tokens) is a memory hog that creates huge fragments of wasted space. vLLM handles this by storing tokens in non-contiguous blocks, virtually eliminating memory waste.

Then there is continuous batching. Most runners wait for an entire batch of requests to finish before starting the next. vLLM allows requests to enter and exit the batch in real-time. As soon as one user's response is done, another request slides into that slot. This keeps the GPU pinned at maximum utilization, which is why it's the gold standard for LLM agent deployments in the real world.

Ollama, conversely, is a Go-based wrapper around llama.cpp. It is designed for the "developer at a desk" experience. It’s brilliant for pulling a model and chatting in seconds, but it lacks the aggressive scheduling of vLLM. While you can tweak OLLAMA_NUM_PARALLEL, it isn't designed to saturate an A100 or H100 the way a dedicated serving stack does.

Benchmarking the Throughput Gap

When you look at actual numbers—specifically Llama 3.1 8B running on an NVIDIA A100 40GB—the difference is staggering. At a concurrency of 1 (one person chatting), the performance is roughly the same. Ollama is snappy and efficient for a single stream.

However, as the number of simultaneous requests climbs toward 256, the efficiency of PagedAttention and continuous batching kicks in. In verified tests, vLLM peaked at around 793 tokens per second, while Ollama struggled at roughly 41 tokens per second. We are talking about a nearly 20x difference in throughput.

Choosing Your Stack

If you are still undecided, use this logic for your deployment:

• Use Ollama if: You need a beginner-friendly setup, you're developing locally on a Mac or a single Linux box, or your app only serves a handful of internal users. It is the perfect "from scratch" starting point.
• Use vLLM if: You are moving toward a real-world production environment, you have multiple GPUs, or you are building a public-facing API. If throughput and latency under load are your primary KPIs, vLLM is the only serious choice.

For a high-performance AI workflow, the ideal setup is often using Ollama for rapid prototyping and switching to vLLM for the final deployment phase to ensure the system doesn't collapse under pressure.