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

A tutorial benchmarks a local Ollama pipeline built with Scikit-Ollama against a cloud Groq-backed Scikit-LLM pipeline on a five-text sentiment classification task, measuring execution time and accuracy for each zero-shot classifier. The comparison uses the llama3 model pulled through Ollama and a small hand-built dataset of five texts labeled Positive or Negative, with accuracy scored via scikit-learn's accuracy_score. The piece frames the tradeoffs between the two setups in latency, cost, setup effort and privacy.

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 https://ollama.com/download 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. python import subprocess import time 1. Installing wrapper libraries pip install -q scikit-llm scikit-ollama 2. Installing Ollama not needed if you use a local IDE and already have it installed and starting background server apt-get update -qq && apt-get install -y -qq zstd curl -fsSL https://ollama.com/install.sh | sh print "Starting Ollama server..." process = subprocess.Popen "ollama", "serve" time.sleep 5 3. Pulling Llama3 model print "Downloading Llama 3..." ollama pull llama3 print "Environment is ready " 1234567891011121314151617181920 import subprocessimport time 1. Installing wrapper libraries pip install -q scikit-llm scikit-ollama 2. Installing Ollama not needed if you use a local IDE and already have it installed and starting background server apt-get update -qq && apt-get install -y -qq zstd curl -fsSL https://ollama.com/install.sh | sh print "Starting Ollama server..." process = subprocess.Popen "ollama", "serve" time.sleep 5 3. Pulling Llama3 modelprint "Downloading Llama 3..." ollama pull llama3 print "Environment is ready " 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 : python import time import numpy as np from sklearn.metrics import accuracy score texts = "I absolutely loved the new update, everything is so fast ", "The customer service was terrible and unhelpful.", "Works exactly as described, highly recommend.", "My screen broke after two days of use.", "It's okay, nothing special but it does the job." y true = "Positive", "Negative", "Positive", "Negative", "Positive" candidate labels = "Positive", "Negative" 1234567891011121314 import timeimport numpy as npfrom sklearn.metrics import accuracy score texts = "I absolutely loved the new update, everything is so fast ", "The customer service was terrible and unhelpful.", "Works exactly as described, highly recommend.", "My screen broke after two days of use.", "It's okay, nothing special but it does the job." y true = "Positive", "Negative", "Positive", "Negative", "Positive" candidate labels = "Positive", "Negative" 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. python def benchmark estimator name, clf, texts, y true, candidate labels, cost note="" : print f"--- Running Benchmark: {name} ---" start time = time.perf counter clf.fit None, candidate labels predictions = clf.predict texts elapsed time = time.perf counter - start time accuracy = accuracy score y true, predictions print f"Time Elapsed : {elapsed time:.2f} seconds" print f"Accuracy : {accuracy 100:.1f}%" print f"Predictions : {list predictions }" print f"Cost : {cost note}\n" 1234567891011121314 def benchmark estimator name, clf, texts, y true, candidate labels, cost note="" : print f"--- Running Benchmark: {name} ---" start time = time.perf counter clf.fit None, candidate labels predictions = clf.predict texts elapsed time = time.perf counter - start time accuracy = accuracy score y true, predictions print f"Time Elapsed : {elapsed time:.2f} seconds" print f"Accuracy : {accuracy 100:.1f}%" print f"Predictions : {list predictions }" print f"Cost : {cost note}\n" 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. python from skollama.models.ollama.classification.zero shot import ZeroShotOllamaClassifier Initializing the local estimator local clf = ZeroShotOllamaClassifier model="llama3" Executing Local Benchmark benchmark estimator name="Local: scikit-ollama Llama 3 ", clf=local clf, texts=texts, y true=y true, candidate labels=candidate labels, cost note="$0.00 Local Hardware " 1234567891011121314 from skollama.models.ollama.classification.zero shot import ZeroShotOllamaClassifier Initializing the local estimatorlocal clf = ZeroShotOllamaClassifier model="llama3" Executing Local Benchmarkbenchmark estimator name="Local: scikit-ollama Llama 3 ", clf=local clf, texts=texts, y true=y true, candidate labels=candidate labels, cost note="$0.00 Local Hardware " Results: Time Elapsed : 80.71 seconds Accuracy : 80.0% Predictions : np.str 'Positive' , np.str 'Negative' , np.str 'Positive' , np.str 'Negative' , np.str 'Negative' Cost : $0.00 Local Hardware 1234 Time Elapsed : 80.71 secondsAccuracy : 80.0%Predictions : np.str 'Positive' , np.str 'Negative' , np.str 'Positive' , np.str 'Negative' , np.str 'Negative' Cost : $0.00 Local Hardware 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: pip install -q --upgrade scikit-llm 1 pip install -q --upgrade scikit-llm 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: python from google.colab import userdata from skllm.config import SKLLMConfig from skllm.models.gpt.classification.zero shot import ZeroShotGPTClassifier try: 1. Fetching free Groq API key from Colab Secrets GROQ API KEY = userdata.get 'GROQ API KEY' 2. Configuring skllm to route traffic to Groq's custom URL instead of OpenAI SKLLMConfig.set openai key GROQ API KEY SKLLMConfig.set gpt url "https://api.groq.com/openai/v1/" 3. Initializing the true scikit-llm classifier using the active model The "custom url::" prefix tells scikit-llm to use the URL provided above cloud clf = ZeroShotGPTClassifier model="custom url::openai/gpt-oss-20b" Executing Cloud Benchmark using the generic helper benchmark estimator name="Cloud: scikit-llm via Groq API gpt-oss-20b ", clf=cloud clf, texts=texts, y true=y true, candidate labels=candidate labels, cost note="$0.00 Free Cloud Tier " except userdata.SecretNotFoundError: print "Please add a free 'GROQ API KEY' from console.groq.com to your Google Colab Secrets." except Exception as e: print f"An error occurred during the API call: {e}" 1234567891011121314151617181920212223242526272829 from google.colab import userdatafrom skllm.config import SKLLMConfigfrom skllm.models.gpt.classification.zero shot import ZeroShotGPTClassifier try: 1. Fetching free Groq API key from Colab Secrets GROQ API KEY = userdata.get 'GROQ API KEY' 2. Configuring skllm to route traffic to Groq's custom URL instead of OpenAI SKLLMConfig.set openai key GROQ API KEY SKLLMConfig.set gpt url "https://api.groq.com/openai/v1/" 3. Initializing the true scikit-llm classifier using the active model The "custom url::" prefix tells scikit-llm to use the URL provided above cloud clf = ZeroShotGPTClassifier model="custom url::openai/gpt-oss-20b" Executing Cloud Benchmark using the generic helper benchmark estimator name="Cloud: scikit-llm via Groq API gpt-oss-20b ", clf=cloud clf, texts=texts, y true=y true, candidate labels=candidate labels, cost note="$0.00 Free Cloud Tier " except userdata.SecretNotFoundError: print "Please add a free 'GROQ API KEY' from console.groq.com to your Google Colab Secrets." except Exception as e: print f"An error occurred during the API call: {e}" Results: Time Elapsed : 2.02 seconds Accuracy : 100.0% Predictions : np.str 'Positive' , np.str 'Negative' , np.str 'Positive' , np.str 'Negative' , np.str 'Positive' Cost : $0.00 Free Cloud Tier 1234 Time Elapsed : 2.02 secondsAccuracy : 100.0%Predictions : np.str 'Positive' , np.str 'Negative' , np.str 'Positive' , np.str 'Negative' , np.str 'Positive' Cost : $0.00 Free Cloud Tier 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.