Does a 2nd GPU Speed Up Ollama? A Deep Dive for LLMs

8/11/2025

So, you're running a local LLM with Ollama & you've got that itch. That "what if I just added one more GPU?" itch. It's a classic thought for anyone diving deep into the world of local AI. You start to wonder if throwing more hardware at the problem will unlock some secret level of performance.

Honestly, the question of whether a second GPU will speed up your Ollama performance is a big one, & the answer isn't a simple "yes" or "no." It's more of a "well, it depends on what you're trying to do."

I've spent a good amount of time digging into this, looking at benchmarks, and reading through the experiences of folks who have tried it. Here's the thing: adding a second GPU can be a game-changer, or it can be a bit of a letdown. Let's break it all down.

The Two Big Reasons to Add a Second GPU

When we talk about adding another GPU to your Ollama setup, there are really two main goals you might have in mind:

Running Bigger Models: This is the most common & straightforward reason. Some of these new models are HUGE, & they just won't fit into the VRAM of a single consumer-grade GPU.
Getting Faster Responses: You've got a model you like, but you want it to generate text faster. You're hoping that two GPUs will work together to churn out tokens at double the speed.

Here's the spoiler: the first goal is a slam dunk. The second one? That's where things get a little more complicated.

Scenario 1: Making Room for the Big Guns (Running Larger Models)

This is where a multi-GPU setup REALLY shines. Let's say you're eyeing a powerful 70-billion parameter model. A quantized version of that might still take up 40GB+ of VRAM. Your trusty RTX 3090 with its 24GB of VRAM isn't going to cut it.

But what if you have two RTX 3090s? Now you've got a combined 48GB of VRAM to play with. This is the magic of a multi-GPU setup with Ollama. It will automatically detect both of your GPUs & split the model's layers across them. For example, if you have a 30GB model and two 16GB GPUs, Ollama can load the model across both, something that would be impossible with a single card.

So, if your main goal is to run those monster models that are too big for a single GPU, then yes, adding a second GPU is EXACTLY what you need to do. It's the key to unlocking the full potential of the open-source LLM world. You'll go from "out of memory" errors to actually being able to run the state-of-the-art models locally.

Some benchmarks with high-end cards like dual Nvidia A100s show this perfectly. They can handle massive 70B and even 110B parameter models with pretty impressive performance, something that would be impossible on a single card.

Scenario 2: The Need for Speed (Faster Inference)

This is where the conversation gets a lot more nuanced. Let's say you're already running a model that fits comfortably on your single GPU, like a 7B or 13B model. You're thinking, "If I add another identical GPU, I should get twice the tokens per second, right?"

Well, not exactly.

Turns out, for a single inference task (like a back-and-forth chat with a model), adding a second GPU can actually slow things down. I know, it sounds counterintuitive, but there's a good reason for it.

The Llama.cpp Backend & The Overhead Tax

Ollama uses a fantastic piece of software called

llama.cpp

as its backend engine.

Llama.cpp

is designed to be incredibly versatile & to run on a wide range of hardware, including CPUs. When you have multiple GPUs,

llama.cpp

splits the model across them.

But here's the catch:

llama.cpp

isn't really built for true parallel processing (something called "tensor parallelism") on a single prompt. So, when you send a request to the model, it has to do a little dance between the two GPUs. It sends some data to the first GPU, some to the second, waits for them to do their thing, & then combines the results.

This "dance" has a cost. The data has to travel across the PCIe bus, which is the connection between your GPUs & the motherboard. This adds a bit of latency. For a single, relatively small model, this overhead can actually be more than the time you save by splitting the work. Some users on Reddit have reported this exact issue, seeing their performance drop when they added a second or third GPU for a single task.

So, if you're just looking to speed up your personal chat sessions with a model that already fits on one card, adding a second GPU might not be the magic bullet you're looking for. You might be better off investing in a single, more powerful GPU.

So, How Do You ACTUALLY Get a Speed Boost?

Okay, so if a second GPU doesn't speed up a single chat, what's the point? The REAL power of a multi-GPU setup with Ollama comes from concurrency. It's not about making one conversation faster; it's about having multiple conversations at the same time without a slowdown.

This is a HUGE deal for anyone looking to use Ollama for more than just personal experimentation. Think about a business that wants to use a local LLM to power a customer service chatbot on their website. They're going to have multiple customers asking questions at the same time.

This is where you can get a massive performance boost. Here are a couple of ways to do it:

1. The "One Ollama Instance per GPU" Method

This is a really clever way to get true parallel processing. Instead of having one Ollama instance that tries to split a model across two GPUs, you can run two separate Ollama instances, each one "pinned" to its own GPU.

You can do this by using the

CUDA_VISIBLE_DEVICES

environment variable. For example, you could start one Ollama server with

CUDA_VISIBLE_DEVICES=0

& another with

CUDA_VISIBLE_DEVICES=1

. Now you have two independent Ollama servers, each with its own dedicated GPU.

Then, you can put a simple load balancer, like Nginx, in front of them. When a request comes in, the load balancer sends it to the first Ollama server. When the next request comes in, it sends it to the second one. This way, you can handle two requests in the time it would normally take to handle one.

This is a game-changer for any kind of production environment. If you're building a tool that makes a lot of parallel calls to an LLM, this setup will fly.

This kind of setup is especially powerful for businesses. For instance, if you're using a tool like Arsturn to build a custom AI chatbot for your website, you could host the Ollama backend on a multi-GPU server. Arsturn helps businesses create these no-code AI chatbots that are trained on their own data, perfect for providing instant customer support & engaging with website visitors 24/7. By running multiple Ollama instances, your Arsturn-powered chatbot could handle a high volume of concurrent users without breaking a sweat, ensuring every customer gets a fast response.

2. Ollama's Built-in Concurrency Settings

Ollama also has some built-in settings to help with concurrency. There are a couple of environment variables you can play with:

1OLLAMA_NUM_PARALLEL
: This setting controls how many parallel requests a model can handle at the same time.
1OLLAMA_MAX_LOADED_MODELS
: This lets you control how many models can be loaded into VRAM at once.

By tweaking these, you can get more out of your multi-GPU setup, especially if you're running different models or handling a lot of simultaneous requests.

A Quick Note on More Advanced Tools

It's also worth mentioning that for REALLY high-performance, multi-GPU setups, some people move beyond

llama.cpp

to more specialized inference engines like

vLLM

ExLlamaV2

. These tools are built from the ground up for tensor parallelism & can squeeze every last drop of performance out of a multi-GPU rig. They're a bit more complex to set up, but if you're building a serious, high-throughput AI service, they're worth looking into.

The Verdict: So, Should You Add That Second GPU?

Alright, let's bring it all home. Here's the bottom line:

If you want to run bigger models that don't fit on a single GPU... YES, absolutely. This is the number one reason to get a second GPU.
If you want to make a single chat session with a small model faster... Probably not. The overhead might actually slow you down. You're better off with a single, faster GPU.
If you want to serve multiple users or run many parallel tasks at once... YES, this is the other big win for multi-GPU setups. With the right configuration, you can get a massive increase in throughput.

For many businesses looking to leverage local LLMs, that last point is the most important one. When you're moving from a personal project to a real-world application, being able to handle concurrent users is everything.

This is where a solution like Arsturn becomes so powerful. You can use their conversational AI platform to build a chatbot that creates meaningful connections with your audience, & then power it with a robust, multi-GPU Ollama setup on the backend. Arsturn helps businesses build these personalized chatbots to boost conversions, & having a scalable backend ensures a great user experience even under heavy load.

So, will adding a second GPU speed up your Ollama performance? It all depends on your definition of "speed." It might not make a single car go faster, but it can turn your one-lane country road into a multi-lane superhighway.

Hope this was helpful! Let me know what you think. It's a pretty interesting topic, & I'm always curious to hear about other people's experiences.