Image based on Synthetic analysis
# Introduction
We often talk about small AI models. But what about smaller models that can actually run on a Raspberry Pi with limited CPU power and very little RAM?
Thanks to advanced architectures and aggressive quantization, models with around 1 to 2 billion parameters can now be run on very small devices. When quantified, these models can run anywhere, even on your smart fridge. All you need is LLAMACPC, a quantized model of the hugging face center, and a simple command to get started.
What makes these miniature models interesting is that they are not flimsy or outdated. Many of them improve large-scale models in real-world text generation. Some tools also support calling, vision understanding, and structured output. These are not small and dumb models. They are small, fast and surprisingly intelligent, capable of running on devices that fit in the palm of your hand.
In this article, we’ll explore 7 small AI models that run well on Raspberry Pi and other low-power machines using LLAMA.CPP. If you want to experiment with native AI without GPUs, cloud costs, or heavy infrastructure, this list is a great place to start.
# 1. QWEN3 4B 2507
Qwen3-4b-instruct-25507 is a compact but highly capable non-deductive language model that provides a large leap in performance for its size. With only 4 billion parameters, it shows tremendous gains in learning shares in following, logical reasoning, math, science, coding, and tool use, while also expanding coverage of long-tail knowledge in many languages.
The model demonstrates particularly good alignment with user preferences in subjective and open-ended tasks, resulting in clearer, more helpful and higher-quality text generation. Its support for an impressive 256K native context length allows it to efficiently handle extremely long documents and conversations, making it a practical choice for real-world applications that demand both depth and speed without the overhead of large models.
# 2. QWEN3 VL 4B
QWEN3 – VL – 4B – Instruct The most advanced vision-language model in the Kevin family to date, packing state-of-the-art multimodal intelligence into a highly efficient 4B-parameter form factor. It provides superior text understanding and generation, which, combined with deep visual perception, reasoning, and spatial awareness, enables strong performance in images, video, and long documents.
The model natively supports 256K contexts (expandable to 1m), allowing it to process entire books or hours. Architectural upgrades such as interleaved-MROP, deepstack visual fusion, and precise text-timestamp alignment significantly improve long-horizon video reasoning, fine-tuning, and image-text grounding.
Beyond perception, QWEN3 – VL – 4B – Instruct acts as a visual agent, capable of running PC and mobile GUIs, supporting tools, generating visual code (HTML/CSS/JS, draw.io), and handling complex multimodal workflows with a reasoning base in both text and vision.
# 3. Equine 4.0 1.2b
Exaone 4.0 1.2b is a compact, on-device-friendly language model designed for resource intensive agentic AI and hybrid reasoning. It integrates both non-reliability mode for fast, actionable response and an optional reasoning mode for complex problem solving, allowing developers to dynamically trade-off speed and depth within a single model.
Despite its small size, 1.2B supports the use of a variety of agent tools, enables function calling and autonomous task execution, and offers multilingual capabilities in English, Korean, and Spanish, extending its usefulness beyond monotonous edge applications.
Architecturally, it inherits Exaone 4.0 advancements such as hybrid attention and improved normalization schemes, while supporting 64K token context lengths, making it exceptionally robust for long concurrent understanding at this scale.
Optimized for performance, it is clearly positioned for on-device and low-cost signaling scenarios, where memory footprint and latency matter as much as model quality.
# 4. Minstrel 3b
minstrel-3-3b-instruct-2512 Minstral is the smallest member of the 3 family and is a highly efficient miniature multimodal language model purpose-built for edge and low-resource deployments. It’s an FP8 directive – optimized for given tinned models, especially chat and directives – to advance workloads, while maintaining robust execution on system pointers and structured output.
Architecturally, it combines a 3.4b-parameter language model with a 0.4B vision encoder, enabling text reasoning as well as spatial image understanding.
Despite its compact size, the model supports a large 256K context window, robust multilingual coverage in dozens of languages, and native agent capabilities such as function calling and JSON output, making it suitable for real-time, embedded, and distributed AI systems.
Designed to fit within 8GB of VRAM in the FP8 (and even less when in quantity), the Minstrels 3 3B instruction delivers strong performance per watt and demand for performance per dollar of capacity for production use cases.
# 5. Jamba Reasoning 3b
Jamba-Discussion-3b A compact yet extraordinarily capable 3-billion-parameter reasoning model designed to deliver strong intelligence, long-theoretic processing, and high performance in a small footprint.
Its defining innovation is a hybrid Transformer-Mumba architecture, where very few attention-grabbing layers capture complex dependencies while most layers use the Mumba state-space model for very efficient sequence processing.
This design dramatically reduces memory overhead and improves throughput, enabling the model to run smoothly on laptops, GPUs, and even mobile-class devices without sacrificing quality.
Despite its size, Jamba3B supports up to 256K token contexts, which can scale very long documents without relying on massive attention caches, making it practical and cost-effective for long ratios.
On intelligence benchmarks, it outperforms smaller models like the Gemma 3 4B and Lama 3.2 3B on scores spanning multiple assessments, demonstrating unusually strong reasoning ability for its class.
# 6. Granite 4.0 Micro
Granite-4.0 Micro Developed by IBM’s Granite team, 3B – Parameter Long – is partially manual and designed specifically for enterprise-grade assistant and agent workflows.
Using a mix of criminally licensed open datasets and high-quality synthetic data, Granite-4.0-fine-tuned from Micro-Base emphasizes reliable guidance, a professional tone, and safe responsiveness, reinforced by the default system prompt included in its October 2025 update.
This model supports a very large 128K context window, robust tool-calling and function-execution capabilities, and extensive multilingual support spanning major European, Middle Eastern, and East Asian languages.
Built on a dense decoder – GQA, Rope, Swaglow MLPs, and RMS Norm, Granite-4.0 – consists of a transformer-only architecture with advanced components such as micro-balance and efficiency, suitable as a foundation model under business applications, integrating external systems, reg pipelines, coding tasks, and LLM agents.
# 7. PHI 4 mini
phi-4-mini-instruct A lightweight, open 3.8B-parameter language model from Microsoft designed to deliver robust reasoning and instruction – performing under tight memory and compute constraints.
Built on a dense decoder-only transformer architecture, it is primarily high-quality synthetic “textbook-like” data and carefully filtered, with a deliberate emphasis on reasoning–the crude content of the memorization of raw facts.
The model supports a 128K token context window, making the understanding of long documents and extended conversations at this scale exceptional.
Post-training monitoring combines fine-tuning and direct priority optimization, resulting in precise instruction following, robust safety behavior and efficient function calling.
With a large 200 kilo-token vocabulary and extensive multilingual coverage, the PHI-4-mini-instruct is positioned as a practical building block for research and production systems that must balance latency, cost, and reasoning quality, especially in memory—or compute—structured environments.
# Final thoughts
Miniature models have reached a point where there is no longer a size limit. The Kevin 3 series stands out on this list, delivering performance that rivals much larger language models and even challenges some proprietary systems. If you’re building applications for the Raspberry Pi or other low-power devices, the Quen3 is a great starting point and worth integrating into your setup.
Beyond Kevin, the Axon 4.0 1.2B model is particularly strong in reasoning and solving the idiosyncratic problem, while remaining significantly smaller than most alternatives. Minstral 3B also deserves attention as the latest release in its series, offering an updated knowledge cut-off and solid general-purpose performance.
Overall, many of these models are impressive, but if your priorities are speed, accuracy and tool calling, the Kevin 3 LLM and VLM variants are hard to beat. They clearly show how small, on-device AI is and native evaluation on small hardware is no longer compromised.
Abid Ali Owan For centuries.@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunication Engineering. His vision is to create an AI product using graph neural networks for students with mental illness.