Best Free Local AI Models for Your Computer in 2026

The best free local AI models in October 2026 are Granite 4.2 3B and Gemma 4 E4B for a laptop with 4 to 8GB to spare, Gemma 4 12B and gpt-oss-20b for a 12 to 16GB graphics card, Qwen3.8-27B and Gemma 4 26B A4B for a 24GB card, and gpt-oss-120b for a Mac or workstation with well over 64GB. Every one of them is free to download and carries a licence that allows commercial use. The real question is which one fits your machine, and the answer depends mostly on the size of the file you download.

Last updated: 18 October 2026. Model releases move monthly, so this list is re-checked monthly.

None of these picks were benchmarked by this site. Each fact below comes from the model's own card on Hugging Face, or from the file listing of the developer's own quantized download, read on 18 October 2026. Where a lab has not published its own quantized file, the post says so rather than borrowing a number from a third party.

How do you pick a free local AI model for your memory?#

The file has to fit in your graphics card's memory (or a Mac's unified memory) with room left over, and that one rule settles most of the choice. A quantized model is a compressed copy of the original weights, and its download size is a close guide to how much memory the weights need. Context, meaning the conversation the model holds in its head, takes memory on top of that, and the longer the chat the more it takes.

The chart below puts the 4-bit file of each pick on one scale. The jump from the 24GB tier to the workstation tier is large, which is why most home users should stop at the third group.

Bar chart of the best free local AI models by quantized file size, from Granite 4.2 3B at 2.2 GB to gpt-oss-120b at 63.4 GB — Figure: the download size decides the tier. Context memory comes on top of each file. Source: file sizes in the official or llama.cpp (ggml-org) GGUF repositories on Hugging Face, read 18 October 2026.

For an exact answer on your own card, including context, the VRAM calculator works it out from the real file sizes. If the letters in a file name such as Q4_K_M mean nothing yet, quantization explained decodes them in plain English.

Which models run on a laptop with 4 to 8GB?#

Granite 4.2 3B is the lightest strong pick, and Gemma 4 E4B is the one to choose if you also want it to read images and listen to audio. Both are recent and both use the Apache 2.0 licence.

According to IBM's model card, Granite 4.2 3B has 3 billion parameters, reads 128K tokens of context natively (extendable to 512K), and was released on 25 August 2026. IBM publishes its own quantized files, and the common Q4_K_M version is 2.24GB, small enough to leave plenty of room on an 8GB machine. It is text only, and IBM lists twelve tested languages.

Google's Gemma 4 E4B is larger than its name suggests. The card describes it as 4.5 billion "effective" parameters, or 8 billion counting its embeddings, and Google's own 4-bit file is 5.15GB. In return it accepts text, images and audio, with a 128K context window. This is because Gemma 4's small models use per-layer embeddings, a design Google built for phones and other devices with little memory.

Qwen3.5-4B is the third option, mainly for its languages. Qwen's card lists 201 languages and dialects and a 262,144-token native context, under Apache 2.0. Qwen has not published its own quantized file for it, so no download size is given here. The card also advises keeping at least 128K of context to preserve its thinking mode, which costs memory on a small machine.

Which models suit a 12 to 16GB graphics card?#

Gemma 4 12B is the safest all-rounder for this tier, and gpt-oss-20b is the stronger choice if you want a larger model that still fits. Granite 4.2 8B is a smaller, text-only alternative that leaves room for long documents.

Gemma 4 12B has 11.95 billion parameters, a 256K context window and audio input, according to Google, and Google's 4-bit file is 6.98GB. That leaves several gigabytes for context on a 12GB card.

gpt-oss-20b is OpenAI's open-weight model from August 2025. According to OpenAI, it has 21 billion parameters with 3.6 billion active for each word it writes, and its weights ship in a 4-bit format called MXFP4 that lets it "run within 16GB of memory". The llama.cpp project's conversion is 12.11GB.

Granite 4.2 8B, released alongside the 3B, is 5.35GB at Q4_K_M. Qwen3.5-9B is also worth knowing for its 201 languages, but again Qwen has published no quantized file of its own.

Which models are worth it on a 24GB graphics card?#

Qwen3.8-27B is the headline pick for a 24GB card, and Gemma 4 26B A4B is the one to choose when you want more room left for context. Granite 4.2 30B rounds out the tier for text-only work.

Qwen3.8-27B appeared on 5 August 2026. Qwen's card gives it 27 billion parameters, a native context of 262,144 tokens (extendable to one million), and a thinking mode that is on by default and can be switched off per request. The llama.cpp project's Q4_K_M file is 18.97GB, which fits a 24GB card with a few gigabytes left for context. A long chat will use that margin quickly, so keep context modest or step down a quantization level.

Gemma 4 26B A4B is a mixture of experts. According to Google, it has 25.2 billion parameters, but only 3.8 billion are active at a time, drawn from 8 of its 128 experts plus one shared expert. Google's 4-bit file is 14.44GB, which leaves close to 10GB of a 24GB card for context, against about 5GB for Qwen3.8-27B. It has a 256K context window and takes image input.

Granite 4.2 30B is 17.72GB at Q4_K_M in IBM's own repository.

What can a Mac or workstation with 64GB or more run?#

gpt-oss-120b is the realistic top end for home hardware, and only on a machine with clearly more than 64GB of memory. Mistral Small 4 is the alternative for readers who prefer an all-Apache model from a European lab.

According to OpenAI, gpt-oss-120b has 117 billion parameters with 5.1 billion active, and it fits "a single 80GB GPU". The llama.cpp conversion is 63.39GB, so a machine with exactly 64GB has no room for the operating system or context. Apple's high-memory Macs share one pool of memory between processor and graphics, which is why they are the usual home route to models of this size.

Mistral Small 4 has 119 billion parameters with 6.5 billion active per token and a 256K context, under Apache 2.0, according to Mistral's card. Mistral has not published its own quantized file, so no size is given.

Larger open models exist, and they are not home models. Z.ai's GLM-5.3-Flash, for example, has 320 billion parameters with 18 billion active, according to its card.

Are these free models free for commercial use?#

All of the picks above use Apache 2.0 or MIT, both of which allow commercial use, but "open weights" does not always mean that. Read the licence on the model card before building anything on it.

Two changes this year make the point. Google's Gemma 3 models carried Google's own Gemma licence, while the Gemma 4 cards carry Apache 2.0, the same licence as most of the picks here. In the other direction, not every Qwen model is Apache. Qwen3.8-Flash-Next uses a "qwen-community-1.0" licence, under which commercial users must keep Qwen's notice, and a business that sells the model as a service needs a separate licence from Qwen. That summary is not legal advice, and the licence text on the card is what counts.

What does this list not tell you?#

It does not rank the models against each other, and it does not give speeds. Each lab reports its own benchmarks on its own terms, so the scores are not comparable from one card to the next, and this site has not run these models on its own hardware.

What it does give is a starting point that fits. Download the smallest pick for your tier first, confirm it runs, then move up. The hardware guide explains the three numbers that decide what your machine can hold, and the calculator turns them into a file choice. The next check of this list is due in mid-November 2026, when the monthly releases will have moved again.

Sources#

Last reviewed: 18 October 2026. Model releases, licences and file sizes change with each release, and this list is re-checked monthly. Next check mid-November 2026.