Local LLM for marketing: which jobs belong on your laptop, and which licence lets you

A local LLM for marketing is a language model that runs on your own laptop or server, so briefs, creative and customer data never leave your machine and each extra call costs nothing. It is worth running one for repetitive, structured work at volume: tagging creative, checking assets against platform rules, drafting variants. It is not worth it for open-ended strategy, where frontier models are still clearly better. Before you pick one, check two things most comparisons skip: how large the model really needs to be, and what its licence lets a business do.

What changed this week

On 13 October, Liquid AI released LFM2.5-24B-A2B, an open-weights model built for agents that run on the device. It has 24 billion parameters in total, but as a mixture-of-experts model it activates only about 2 billion for each token, which is what makes it fast on a laptop. Liquid reports 184 tokens a second on an Apple M5 Max and 114 on an AMD Ryzen AI Max+ 395, a 131,072-token context window, and 16 languages. It ships as GGUF for llama.cpp, a 4-bit MLX build for Apple Silicon, and works with LM Studio, vLLM and Ollama.

Two caveats from Liquid's own materials. The speed and benchmark figures are Liquid's, with no independent evaluation yet. And Liquid says larger models keep an edge on knowledge-heavy and coding benchmarks, recommending retrieval or web search for knowledge-heavy work. It is also reasoning-only: it always writes out its thinking before answering, which costs time on short, simple jobs.

At the other end of the scale, Mistral announced Mistral Large 4 on 6 October as an open-weights model of about a trillion parameters. Its weights followed on 29 October under the Mistral Ecosystem Open-Weight License, which we cover in our guide to open weights model licences. That is a model for a server room, not a laptop.

Which marketing jobs suit a local model?

The pattern from our own measurements is that small local models are good at narrow, repeated, structured work, especially when the rules are stated in the prompt, and weak where a job needs broad reasoning.

The table is a judgement built on results from one 4-billion-parameter model, not a law. The point is to sort your work before choosing a model, not after.

Open weights is not one licence

"Open" covers very different terms. These are the ones that matter for a business, as each licence states them.

We know the Llama terms from experience: our archived companion model is a Llama 3.1 derivative, so it carries Meta's terms rather than Apache 2.0, and we label that on every page that mentions it. For an agency or a brand above USD 10 million in revenue, the difference between the first two rows is the difference between using a model and negotiating for it. Read the licence file in the model repository, not the headline.

How big does a local marketing model need to be?

Smaller than most people assume, for the right job. When we chose a size for our first model, we trained the same data at 1.7, 4 and 8 billion parameters. The 8B version scored 0.741 against the 4B version's 0.780, so the extra capacity bought nothing and we shelved the 8B tier. The data, not the size, was the constraint.

Three practical rules follow:

1. Active parameters set the speed. A mixture-of-experts model like LFM2.5 is sized by its total parameters but runs at the speed of its active ones. Memory, though, still has to hold the whole model.
2. Quantisation is not free. Shrinking a model to 4-bit weights makes it fit on a laptop. How much it costs varies: an older version of our 4B model lost 0.045 when quantised, mostly on judgement-heavy labels, while the current r25 version lost 0.004 (0.792 to 0.788 on exam v3.1 of our benchmark). We publish the score of the file you can download, not the unquantised one. Measure the file you run.
3. Size up only when a measurement says so. Start with the smallest model that passes your own test set, with the rules written into its prompt, as covered in our post on when to fine-tune an LLM.

Where gloofy fits, and where it does not

gloofy-1-nano is a 4-billion-parameter model fine-tuned from Qwen3-4B for marketing judgement and structured tasks. It ships as a Q4_K_M GGUF file of about 2.5 GB under Apache 2.0, with an Ollama Modelfile, and runs offline through llama.cpp, Ollama, LM Studio or any OpenAI-compatible server. Its specifications and the public benchmark it is measured on are on the homepage, including the models that score above it and the corrections we have made along the way.

It is not a general assistant. It will not out-reason a frontier model, it is not a 131,000-token research agent, and on campaign diagnosis we ship the untrained base model because it beat our own adapter. Pick gloofy when you need consistent marketing labels on your own machine at no marginal cost. Pick a frontier model, or a larger local model with retrieval, for open questions; we compare the options in the best LLM for marketing. If you use an AI model to grade outputs while comparing local options, our notes on using an LLM as a judge cover the biases to control. More on open and small models for marketing collects in the marketing LLMs and open models hub.

What to do this week

• List your team's repeated AI tasks and sort them with the table above before choosing any model.
• Build a test set of 50 to 150 real examples for the top task, and score a small local model on it with the rules in its prompt.
• Open the licence file of every candidate model and check the revenue and user thresholds against your company, and your clients if you are an agency.
• Measure the quantised file you will actually run, not the full-precision benchmark.

FAQ

What is a local LLM for marketing?

It is a language model that runs on your own laptop or server instead of a vendor's cloud. Your briefs, creative and customer data stay on your machine, and each additional call costs nothing. It suits repetitive, structured marketing work such as tagging creative or checking assets against platform rules, rather than open-ended strategy.

Can a small local model replace ChatGPT or Claude for marketing?

Not for open-ended reasoning, research or strategy, where frontier models are clearly stronger. For narrow, repeated jobs with clear rules, a small local model can be good enough and much cheaper. On the last version of our public exam that included them, Claude and Gemini scored above our 4B model, and we recommend it only for structured tasks.

Is LFM2.5-24B-A2B free for commercial use?

Under Liquid AI's LFM Open License v1.0, commercial use is royalty-free for organisations below USD 10 million in annual revenue. Above that threshold the licence no longer covers commercial use and Liquid AI asks companies to arrange a separate commercial licence. Research and qualifying non-profit use have no threshold.

How much accuracy does quantising a model cost?

It depends on the model and the task, so measure it. An older version of our 4B model lost 0.045 when quantised to a 4-bit GGUF, mostly on judgement-heavy labels; the current version lost 0.004. We publish the score of the downloadable quantised file rather than the unquantised one, because that is the model people actually run.