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AI Models You Can Run for Free Without a GPU

Small open models that run on an ordinary laptop or desktop CPU with no GPU and no API fees. Free runners such as Ollama and llama.cpp, tiny and small language models with run commands, reasoning and coding models, embeddings for local RAG, vision and speech models, and memory and quantization tips. Grouped by purpose, not ranked.

56 topics•8 categories•Updated November 2026

1. Start Here: Free Tools to Run Models on a CPU

11 entries

Open-source runners that need no GPU and no API fees. Ollama is the easiest way to begin; the others suit desktop apps, single-file builds or custom setups.

0110 AI Models You Can Run for Free Without a GPU (Python in Plain English)Source article by Arham Ali on running models on a CPU with Ollama; member-only on Medium. 02OllamaDownload and run open models locally with a simple command line. 03Ollama on GitHubSource code, install notes and API docs. 04Ollama model librarySearchable catalogue of models with sizes and run commands. 05Ollama FAQAnswers on memory, GPU use, context length and settings. 06llama.cppC and C++ engine that runs GGUF models efficiently on CPUs. 07LM StudioDesktop app for finding and chatting with local models. 08JanOpen-source offline assistant for your computer. 09GPT4AllLocal chat app built for CPUs and consumer hardware. 10llamafilePackage a model and runtime as one executable. 11Open WebUIBrowser chat interface that works with Ollama.

2. Tiny Models Under 2 Billion Parameters

6 entries

The lightest models, suited to older laptops and low-memory machines. Expect simple questions, short summaries and basic drafting rather than complex reasoning.

12Qwen3.5 0.8BSmallest Qwen3.5 size. Run: ollama run qwen3.5:0.8b. 13Qwen3 0.6B and 1.7BSmall dense Qwen3 sizes. Run: ollama run qwen3:1.7b. 14Gemma 3 270M and 1BGoogle's smallest Gemma 3 models. Run: ollama run gemma3:1b. 15Llama 3.2 1BMeta's 1B model for lightweight tasks. Run: ollama run llama3.2:1b. 16SmolLM2 135M, 360M and 1.7BCompact models from Hugging Face. Run: ollama run smollm2:1.7b. 17TinyLlama 1.1BCompact Llama-style model trained on 3 trillion tokens. Run: ollama run tinyllama.

3. Small Models of 2 to 4 Billion Parameters

7 entries

A step up in quality that still runs on a typical laptop CPU. Good for summaries, drafting, Q&A over documents and light coding help, at slower speeds than a GPU.

18Llama 3.2 3BGeneral assistant model. Run: ollama run llama3.2:3b. 19Phi-4-mini 3.8BMicrosoft small model with function calling and strong maths. Run: ollama run phi4-mini. 20Qwen3.5 2B and 4BLarger Qwen3.5 sizes. Run: ollama run qwen3.5:4b. 21Gemma 3 4BGemma 3 with image input. Run: ollama run gemma3:4b. 22Gemma 3n E2B and E4BGemma models designed for laptops, tablets and phones. Run: ollama run gemma3n:e2b. 23IBM Granite 3.3 2B128K-context instruction model. Run: ollama run granite3.3:2b. 24Mistral 7BLarger 7B model; needs more RAM and runs slowly on CPU. Run: ollama run mistral.

4. Reasoning and Coding Models for CPUs

4 entries

Small models tuned for step-by-step reasoning or code. Reasoning models produce long hidden or visible thinking, so expect slower answers on a CPU.

25DeepSeek-R1 1.5B distillSmall reasoning model. Run: ollama run deepseek-r1:1.5b. 26Qwen2.5-Coder 1.5B and 3BCode-specific models. Run: ollama run qwen2.5-coder:1.5b. 27Qwen3 4BQwen3 with a thinking mode. Run: ollama run qwen3:4b. 28Reasoning Models for Edge DevicesSmall reasoning models for constrained hardware.

5. Embedding Models for Local Search and RAG

8 entries

Turn text into vectors on your own machine. Embedding models are small and run well on CPUs, so a fully local RAG stack needs no GPU.

29nomic-embed-textLong-context text embedding model. Run: ollama pull nomic-embed-text. 30EmbeddingGemmaCompact Gemma-based embedding model. 31all-minilmVery small sentence embedding model. 32BGE-M3Multilingual, multi-granularity embedding model. 33Sentence TransformersLibrary for running embedding models on CPUs. 34Embedding ModelsDirectory of embedding models. 35Open-Source RAG StackComponents for a local retrieval stack. 36Vector Indexing AlgorithmsHow vector search finds neighbours fast.

6. Vision, Speech and Image Models on a CPU

6 entries

Beyond text: small multimodal and speech models. These run slower than text models, so start with small sizes and short inputs.

37MoondreamTiny vision-language model for images. Run: ollama run moondream. 38LLaVAVision-language model for image questions. 39Qwen2.5-VLVision-language model with several sizes. 40whisper.cppOn-device speech recognition in C and C++. 41faster-whisperFast Whisper speech recognition that runs on CPUs. 42stable-diffusion.cppImage generation in C and C++ that works on CPUs.

7. Memory, Quantization and Speed Tips

7 entries

Fit a model into RAM and keep it responsive. As a rough guide, a 4-bit model needs about 0.5 to 0.7 GB of RAM per billion parameters plus room for context; check each model page for its download size.

43Hugging Face: GGUF formatHow GGUF files store quantized models. 44GGUF specificationFile format used by llama.cpp. 45Hugging Face quantization overviewExplains quantization methods and trade-offs. 46llama-benchMeasure tokens per second on your own machine. 47Hugging Face model hubFind GGUF builds of open models. 48AI Model Quantization TechniquesHow lower precision cuts memory. 49LLM Model Formats ExplainedGGUF, safetensors and others.

8. Related Pages on This Site

7 entries

Directories for local LLMs, small models and on-device inference. Where to go next after your first model runs.

50Platforms for Running LLMs LocallyOllama, LM Studio and similar. 51Small Language ModelsDirectory of small models. 52On-Device Inference Enginesuzu, MLX, llama.cpp and more. 53LLM Inference EnginesEngines for servers and desktops. 54Open-Source LLMsOpen model families. 55Edge AIRunning AI near the data. 56LLM Cost Optimization TechniquesCut LLM spend, including by running locally.

Frequently Asked Questions

Can I run AI models without a GPU?

Yes. Small language models, roughly 0.5 to 4 billion parameters, run on an ordinary laptop or desktop CPU using tools such as Ollama and llama.cpp. They are slower than on a GPU and less capable than large cloud models, but they are free to use and work offline.

What is the easiest way to start?

Install Ollama, then run a model with one command, for example ollama run qwen3.5:0.8b or ollama run gemma3:1b. Ollama downloads the model the first time and then chats in the terminal or through a local API. Desktop apps such as LM Studio, Jan and GPT4All offer a graphical alternative.

Which models work best on a CPU?

For very old or low-memory machines try Qwen3.5 0.8B, Gemma 3 1B, Llama 3.2 1B or SmolLM2. For a typical modern laptop try 3 to 4 billion parameter models such as Llama 3.2 3B, Phi-4-mini, Qwen3.5 4B or Gemma 3n. Larger 7B models run but slowly without a GPU.

How much RAM do I need?

As a rough rule, a 4-bit quantized model needs about 0.5 to 0.7 GB of RAM per billion parameters plus memory for the context, so a 1B model fits in a couple of gigabytes and a 4B model in about 4 to 5 GB. Check the download size on each model page and leave room for the operating system.

How fast will it be?

Speed depends on your CPU, memory bandwidth, model size and quantization, and varies widely. Smaller models and lower-bit quantization are faster. Measure on your own machine with a tool such as llama-bench rather than relying on published numbers.

What can small models do well?

They handle simple questions, short summaries, drafting, classification, extraction and light coding help. They struggle with long, multi-step reasoning, deep factual recall and complex code, where larger models are better. Reasoning models also generate long thinking, which is slow on a CPU.

Can I build local search or RAG without a GPU?

Yes. Embedding models such as nomic-embed-text, EmbeddingGemma, all-minilm and BGE-M3 are small and run well on CPUs, so you can build a fully local retrieval stack with a vector index and a small chat model.

Are these models really free?

The software and many model weights are free to download and run, but each model has its own licence, for example Apache 2.0, MIT or a custom licence such as the Gemma terms. Read the licence before commercial use. You still pay for electricity and your own hardware.

Where does this list come from?

The page was inspired by a Python in Plain English article by Arham Ali, which is member-only on Medium; the models and commands here were checked against the public Ollama library pages and project repositories. Links were checked in November 2026, and sites that block automated checks may need a manual click.