Ollama vs. vLLM: Why Ollama is Slow for Multiple Users

8/12/2025

Ollama vs. HoML: The Real Reason Ollama is Slower for Multiple Users

If you've been diving into the world of local large language models (LLMs), you've almost certainly come across Ollama. It’s been a game-changer, making it incredibly simple to get powerful models like Llama 3 or Mistral running on your own machine. But if you’ve ever tried to build something a little more ambitious—say, a chatbot for your website or an internal tool for your team—you might have hit a wall. The moment more than one person tries to use it at the same time, things can slow down to a crawl.

So, what gives? Why does Ollama, which feels so snappy for personal use, stumble when faced with multiple users?

The answer isn't as simple as "it's just slow." It's a tale of two different design philosophies. On one side, you have Ollama, built for simplicity & accessibility. On the other, you have what I'm calling "HoML" (Hugging Face Model Loader) stacks—a more general term for production-grade solutions designed to serve models to many users at once.

Honestly, this is a topic that trips a lot of people up. They get started with Ollama, see the magic, & then get frustrated when they try to scale. Let's break down what's REALLY going on under the hood.

The Ollama Philosophy: Simplicity First, Everything Else Second

Ollama's primary goal is to make running LLMs as easy as typing

ollama run llama3

. And it nails this. It’s built on top of

llama.cpp

, a highly optimized C++ library, & it handles all the complicated stuff for you: model downloading, quantization, & running a local server. For a single user on a local machine, the experience is fantastic.

But here’s the thing: Ollama was fundamentally designed as a single-user tool. Its architecture reflects this. When you send a request to Ollama, it processes it. If you send another request while the first one is still running, the second one has to wait in line. It’s like a single-lane road—traffic can only move in one direction at a time.

Now, the Ollama team has made improvements. With version 0.2, they introduced better concurrency controls, like

OLLAMA_NUM_PARALLEL

, which allows a model to process a few requests in parallel. But even with these tweaks, it’s not designed for true, high-concurrency workloads. GitHub issues and Reddit threads are filled with developers noticing this bottleneck. One user on GitHub even pointed out that when they have multiple users trying to access the same model, they all get stuck in a queue on a single GPU, even if other GPUs are sitting idle. This is a classic sign of a system not built for parallel processing from the ground up.

Think of it like a small, trendy coffee shop with one super-talented barista. That barista can make an AMAZING cup of coffee, one at a time. If a few people come in, they can probably juggle the orders. But if a busload of tourists shows up, the line is going to go out the door, & everyone's going to be waiting a long, long time. That’s Ollama with multiple users.

The "HoML" Stack: Building for the Crowd

So, what’s the alternative? This is where the concept of a "Hugging Face Model Loader" stack comes in. This isn't one specific piece of software but rather a combination of tools designed for a different purpose: serving a Hugging Face model in a robust, scalable way.

At its most basic, a HoML stack might involve using the Hugging Face

transformers

library with a Python web server like Flask or FastAPI, often managed by a production-ready server like Gunicorn. This setup immediately gives you more control over how requests are handled, allowing you to spin up multiple worker processes to handle concurrent users.

But the REAL powerhouse in the HoML world, especially for demanding applications, is vLLM.

vLLM is an open-source library from UC Berkeley that is purpose-built for high-throughput, low-latency LLM inference. It's the professional kitchen to Ollama's single-barista coffee shop. It achieves its incredible performance through a few key architectural advantages:

1. PagedAttention: The Memory Game-Changer

This is the secret sauce. When an LLM generates text, it has to keep track of the conversation history (the "KV cache"). Traditional systems allocate one big chunk of memory for this cache for each user. This is hugely inefficient. vLLM, on the other hand, uses PagedAttention, which breaks the KV cache into smaller, non-contiguous blocks, kind of like how a computer's operating system manages RAM.

This means vLLM can manage memory way more efficiently, packing more users onto the same GPU without running out of VRAM. It’s a sophisticated approach that Ollama, with its simpler

llama.cpp

foundation, just doesn't have.

2. Continuous Batching: The Never-Ending Conveyor Belt

Instead of waiting for a batch of requests to finish before starting the next one, vLLM uses continuous batching. As soon as one request in a batch is finished, vLLM swaps in a new one from the queue. This keeps the GPU constantly busy, maximizing throughput. It’s like having a conveyor belt that’s always moving, never stopping to wait for a whole group of items to be ready.

Ollama, by contrast, tends to process requests more sequentially. If one user's request is long & complex, everyone else behind them has to wait.

The Performance Numbers Don't Lie

This isn't just theoretical. The folks at Red Hat did a head-to-head benchmark, & the results are pretty stark.

Throughput (Tokens Per Second): vLLM reached a peak of 793 tokens per second (TPS) under heavy load. A tuned Ollama maxed out at just 41 TPS. That's not a small difference; it's a monumental one.
Latency (Time to First Token): As more users sent requests, Ollama's time to first token (how long you wait for the first word to appear) shot up dramatically. vLLM's latency stayed consistently low. This is because Ollama starts making users wait in a queue, while vLLM's efficient batching handles the load more gracefully.

The conclusion is clear: vLLM is simply in a different league when it comes to serving multiple users. Ollama is designed for ease of use in a single-user context, while vLLM is engineered for raw, scalable performance in a production environment.

So, When Should You Use Which?

Here’s the thing: this isn't about one tool being "better" than the other. It’s about using the right tool for the job.

Choose Ollama when:
- You're developing locally or prototyping an idea.
- You're building an application for a single user (like a personal assistant).
- You need to get a model running QUICKLY with minimal fuss.
- You're working with limited hardware, like a laptop with a decent GPU, or even just a CPU.
Choose a HoML stack (especially with vLLM) when:
- You're building a production application that needs to serve multiple concurrent users.
- High throughput & low latency are critical for user experience.
- You have dedicated server hardware (especially NVIDIA GPUs) that you want to maximize.
- You need to build a scalable, enterprise-grade AI service.

Bridging the Gap: The Rise of Scalable AI Chatbots

This entire discussion is incredibly relevant for businesses today. Many companies are excited about using AI to improve customer service, generate leads, or engage website visitors. The first impulse might be to spin up an open-source model with Ollama. And for an internal proof-of-concept, that's a great start.

But when it's time to put that chatbot on your public website, you're going to face the multi-user problem head-on. Your website could have dozens or even hundreds of visitors trying to interact with your bot simultaneously. An Ollama-based solution would likely buckle under the pressure, leading to slow responses & a terrible user experience.

This is where managed platforms built for scalability come into play. For instance, this is a problem we've thought a lot about at Arsturn. We help businesses build no-code AI chatbots that are trained on their own data. Our platform is designed from the ground up to handle the demands of a real-world business environment. Instead of wrestling with vLLM configurations or worrying about server management, businesses can use Arsturn to create & deploy a custom AI chatbot that provides instant, 24/7 support to all their website visitors, no matter how many there are. It's about taking the power of these advanced models & making it accessible and scalable.

By leveraging a platform like Arsturn, you get the best of both worlds: the customized knowledge of a model trained on your data, combined with a robust infrastructure that ensures every customer gets a fast, personalized experience.

Final Thoughts

So, the next time you hear someone complain that Ollama is "slow," you'll know the real story. It’s not that it's slow; it's that it's a sports car being used to haul lumber. It’s an amazing tool for what it was designed for: making local LLMs accessible to everyone. But for the demanding world of multi-user applications, you need a different set of tools—a heavy-duty truck like vLLM or a fully managed service that handles the heavy lifting for you.

Hope this was helpful & clears up some of the confusion around Ollama vs. HoML performance. It's a pretty fascinating look into the different engineering choices that shape the tools we use every day. Let me know what you think