Groq processed five reviews in 2 seconds, Ollama in 81: what the test leaves out

Suppose you want to automatically sort customer reviews into positive and negative categories. There are two options: run a model on your own computer or send the text through a cloud API. The questions are practical: how long you will wait for a response, how much processing will cost and where customer data will go.

Machine Learning Mastery published a tutorial demonstrating this comparison. In the author's run, the local model processed five reviews in 80.71 seconds, while the cloud model took 2.02 seconds. That is a nearly fortyfold difference. However, it is too early to use these figures to choose infrastructure: the experiment compares different models, and the conditions of the local run are not sufficiently documented.

How the comparison works

The author builds two classifiers with the fit() and predict() methods familiar to scikit-learn users. The first uses Scikit-Ollama and a local Llama 3 model. The second uses Scikit-LLM and sends requests to gpt-oss-20b through Groq's API. Both receive the same five English sentences and two available categories: positive and negative.

A shared function runs the classifier, measures total elapsed time and checks the responses against predefined labels. This is a useful starting point: you can replace the demonstration sentences with reviews from your own store, support requests or short user messages. The interface lets you retain the same measurement logic while switching the backend.

In the reported results, the local model correctly classified four of the five sentences, while the cloud model classified all five correctly. That produced accuracy scores of 80% and 100%. The timing includes calls to fit() and predict(), so the reported seconds describe the entire measured run, rather than the latency of an individual request.

Why there is no winner yet

The main problem is that both the model and the execution environment change at the same time. The experiment therefore does not separate the effect of infrastructure from the differences between Llama 3 and gpt-oss-20b. Using the same measurement function makes the procedure more convenient, but does not by itself make the systems comparable.

The article also provides no local hardware specifications, series of repeated runs or separate results after model warm-up. Without these, it is impossible to know how representative the 80.71-second result would be on another computer. A single total figure also hides the variation in latency, which can affect the user experience.

The accuracy results illustrate the issue even more clearly. The only disagreement involved a review that can be translated as "It's OK, nothing special, but it works." The author labeled it positive; the local model labeled it negative. The task has no neutral class. The entire twenty-percentage-point gap therefore comes down to one ambiguous sentence and the chosen labeling scheme.

Cost is not measured here either. The function simply prints the supplied string "$0". For local execution, this excludes hardware and electricity; for cloud execution, it does not explain the terms of free access or usage limits. The claim of absolute privacy also needs qualification: running in Google Colab, which the tutorial allows, already means using external infrastructure.

What to use in your own project

The article's practical value lies in its experimental framework. Start with a representative sample of real messages and clear labeling rules. If your product receives neutral reviews, decide in advance whether they need their own category. Otherwise, some apparent "model errors" will turn out to be disagreements about what the labels mean.

Next, fix the model versions, execution parameters and hardware. Repeat the measurements, assessing the first request separately from performance after warm-up. For a service, it helps to know not just the average response time, but also the latency of slow responses, behavior under concurrent load and the failure rate.

Finally, calculate costs at your expected request volume and check data transfer requirements. The test demonstrates how to quickly build an initial comparison prototype. The decision to run locally or in the cloud should be based on results for your own task: the five published reviews do not provide sufficient grounds.