Benchmarking Scikit-Ollama vs. Groq-Backed Scikit-LLM: Speed, Cost, and Accuracy

In this article, you will learn how to build a side-by-side benchmark of local and cloud LLM classifiers using Scikit-Ollama and a Groq-backed Scikit-LLM pipeline.

Topics we will cover include:

• How to set up Ollama, Scikit-Ollama, and Scikit-LLM, and how to create a small sentiment dataset for testing.
• How to write a reusable helper function that measures the execution time and accuracy of a zero-shot classifier.
• How to run the same benchmark locally and through the Groq API, and what the tradeoffs are in latency, cost, setup, and privacy.

Introduction

Looking for a practical, side-by-side comparison of two LLM solutions (local vs. cloud) in the same application scenario? This article presents a head-to-head benchmark of two different solutions:

• A local Ollama-backed machine learning pipeline constructed with the aid of Scikit-Ollama.
• A cloud LLM equivalent, in this case a Groq-backed Scikit-LLM pipeline with the same purpose as the first, local LLM-based scenario.

For the target tasks shown, the comparison will quantify the real tradeoffs across dimensions like speed, accuracy, and real (or potential) cost.

Environment Setup and Data Creation

The first step of the process is to install the required Python libraries, mainly scikit-llm and scikit-ollama. If you want to run the code in a notebook, you’ll also need to install the Ollama engine to download and use open-source LLMs “locally”. If you are using an IDE on your local machine, make sure to have Ollama installed separately from its website and pull (download) the local llama3 model. Either way, the code includes the instructions to start the Ollama server in the background and pull the model.

If the setup process went well, we are ready for the next step: creating the dataset and defining a helper benchmark function. This stage is basically broken down as follows:

First, let’s create a tiny, dummy dataset with texts and their associated ground-truth sentiment labels (y_true):

Second, to make our code reusable and easily extendable to new comparisons under a centralized testing logic, we will define a “universal” helper function called benchmark_estimator. This function is responsible for handling the pipeline’s fit() and predict() methods. As you will observe later, both the local and cloud estimators we instantiate will be passed through the exact same function, thereby measuring aspects like accuracy and execution time fairly.

Scenario 1: Local Zero-Shot Classification with Scikit-Ollama

For the first run of our benchmark, we initialize a ZeroShotOllamaClassifier from Scikit-Ollama, targeting our previously downloaded Llama3 model.

After that, we run the benchmark. How? Simpler than it sounds, as we already have our helper function to do so! We just need to carefully pass the right arguments to the function: a descriptive name for this benchmark estimator, the zero-shot classifier instance we just defined, the data, the ground-truth labels, and the existing class labels for the problem, stored in candidate_labels.

In sum, we are testing the local side of our zero-shot classification pipeline, proving how the model can classify texts entirely on a local machine (or Google Colab session, for that matter) without depending on external servers, guaranteeing absolute data privacy and avoiding API costs.

Results:

Cost is inherently $0, as we are running free, open-source, local models with Ollama. While the model scored four out of five (80%) correctly, be aware that for a more realistic evaluation, we would use a larger dataset than just five examples. We kept the dataset to a minimum size to enable smooth, accessible running for anyone, but feel free to add extra data examples (with their associated true labels) if needed.

Scenario 2: Cloud-Backed Zero-Shot Classification with Scikit-LLM and Groq

To enable the comparison, we will now define — aided by Scikit-LLM — another zero-shot classifier. For this, we use SKLLMConfig.set_gpt_url() to route Scikit-LLM’s native ZeroShotGPTClassifier to a compatible cloud endpoint via custom URL prefixes. Using free cloud models like Groq’s, this approach avoids account quota locks and keeps the benchmark completely free.

You may need to install Scikit-LLM first:

Also, be sure to get an API key from the Groq website and add it to a secret variable (in Google Colab) called GROQ_API_KEY.

Then use the following code to execute the second “actor” of our benchmark demonstration:

Results:

Obviously, once again, the cost is simply a static output of $0, as the Groq API is also free to use as of writing. While the experiment shown here is small for the sake of space, and without attempting to compare accuracy on such a small dataset, we can draw some conclusions that apply if you extend your experiments:

• Latency is mainly limited by GPU VRAM and token generation speed in the local, Ollama-based solution. Meanwhile, it is near-instantaneous in the cloud solution due to its dedicated inference engines.
• Both options we used are free, but if you switch to a different cloud provider, like OpenAI, small costs per inference call may apply.
• The setup overhead is also lower in the case of the cloud solution.
• The greatest advantage of Ollama’s local models is the total data privacy, with potentially sensitive data never leaving local memory.

Wrapping Up

This article walked you through the process of building a fair, standardized benchmark between local inference and cloud inference for LLM-driven pipelines, illustrating it in zero-shot classification scenarios. Both approaches, making use of Scikit-Ollama and Scikit-LLM respectively, adhere to scikit-learn’s classic .fit() and .predict() methods used in traditional machine learning pipelines.