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# Introduction
We frequently discuss small AI fashions. However what about tiny fashions that may really run on a Raspberry Pi with restricted CPU energy and little or no RAM?
Due to fashionable architectures and aggressive quantization, fashions round 1 to 2 billion parameters can now run on extraordinarily small units. When quantized, these fashions can run virtually anyplace, even in your sensible fridge. All you want is llama.cpp, a quantized mannequin from the Hugging Face Hub, and a easy command to get began.
What makes these tiny fashions thrilling is that they don’t seem to be weak or outdated. A lot of them outperform a lot older giant fashions in real-world textual content technology. Some additionally help instrument calling, imaginative and prescient understanding, and structured outputs. These are usually not small and dumb fashions. They’re small, quick, and surprisingly clever, able to working on units that match within the palm of your hand.
On this article, we are going to discover 7 tiny AI fashions that run nicely on a Raspberry Pi and different low-power machines utilizing llama.cpp. If you wish to experiment with native AI with out GPUs, cloud prices, or heavy infrastructure, this checklist is a good place to start out.
# 1. Qwen3 4B 2507
Qwen3-4B-Instruct-2507 is a compact but extremely succesful non-thinking language mannequin that delivers a serious leap in efficiency for its measurement. With simply 4 billion parameters, it exhibits sturdy good points throughout instruction following, logical reasoning, arithmetic, science, coding, and power utilization, whereas additionally increasing long-tail data protection throughout many languages.
The mannequin demonstrates notably improved alignment with consumer preferences in subjective and open-ended duties, leading to clearer, extra useful, and higher-quality textual content technology. Its help for a formidable 256K native context size permits it to deal with extraordinarily lengthy paperwork and conversations effectively, making it a sensible alternative for real-world purposes that demand each depth and velocity with out the overhead of bigger fashions.
# 2. Qwen3 VL 4B
Qwen3‑VL‑4B‑Instruct is essentially the most superior imaginative and prescient‑language mannequin within the Qwen household so far, packing state‑of‑the‑artwork multimodal intelligence right into a extremely environment friendly 4B‑parameter type issue. It delivers superior textual content understanding and technology, mixed with deeper visible notion, reasoning, and spatial consciousness, enabling sturdy efficiency throughout photos, video, and lengthy paperwork.
The mannequin helps native 256K context (expandable to 1M), permitting it to course of complete books or hours‑lengthy movies with correct recall and advantageous‑grained temporal indexing. Architectural upgrades comparable to Interleaved‑MRoPE, DeepStack visible fusion, and exact textual content–timestamp alignment considerably enhance lengthy‑horizon video reasoning, advantageous‑element recognition, and picture–textual content grounding
Past notion, Qwen3‑VL‑4B‑Instruct features as a visible agent, able to working PC and cellular GUIs, invoking instruments, producing visible code (HTML/CSS/JS, Draw.io), and dealing with complicated multimodal workflows with reasoning grounded in each textual content and imaginative and prescient.
# 3. Exaone 4.0 1.2B
EXAONE 4.0 1.2B is a compact, on‑gadget–pleasant language mannequin designed to carry agentic AI and hybrid reasoning into extraordinarily useful resource‑environment friendly deployments. It integrates each non‑reasoning mode for quick, sensible responses and an optionally available reasoning mode for complicated drawback fixing, permitting builders to commerce off velocity and depth dynamically inside a single mannequin.
Regardless of its small measurement, the 1.2B variant helps agentic instrument use, enabling perform calling and autonomous process execution, and gives multilingual capabilities in English, Korean, and Spanish, extending its usefulness past monolingual edge purposes.
Architecturally, it inherits EXAONE 4.0’s advances comparable to hybrid consideration and improved normalization schemes, whereas supporting a 64K token context size, making it unusually sturdy for lengthy‑context understanding at this scale
Optimized for effectivity, it’s explicitly positioned for on‑gadget and low‑price inference eventualities, the place reminiscence footprint and latency matter as a lot as mannequin high quality.
# 4. Ministral 3B
Ministral-3-3B-Instruct-2512 is the smallest member of the Ministral 3 household and a extremely environment friendly tiny multimodal language mannequin function‑constructed for edge and low‑useful resource deployment. It’s an FP8 instruct‑advantageous‑tuned mannequin, optimized particularly for chat and instruction‑following workloads, whereas sustaining sturdy adherence to system prompts and structured outputs
Architecturally, it combines a 3.4B‑parameter language mannequin with a 0.4B imaginative and prescient encoder, enabling native picture understanding alongside textual content reasoning.
Regardless of its compact measurement, the mannequin helps a big 256K context window, sturdy multilingual protection throughout dozens of languages, and native agentic capabilities comparable to perform calling and JSON output, making it nicely suited to actual‑time, embedded, and distributed AI techniques.
Designed to suit inside 8GB of VRAM in FP8 (and even much less when quantized), Ministral 3 3B Instruct delivers sturdy efficiency per watt and per greenback for manufacturing use circumstances that demand effectivity with out sacrificing functionality
# 5. Jamba Reasoning 3B
Jamba-Reasoning-3B is a compact but exceptionally succesful 3‑billion‑parameter reasoning mannequin designed to ship sturdy intelligence, lengthy‑context processing, and excessive effectivity in a small footprint.
Its defining innovation is a hybrid Transformer–Mamba structure, the place a small variety of consideration layers seize complicated dependencies whereas nearly all of layers use Mamba state‑area fashions for extremely environment friendly sequence processing.
This design dramatically reduces reminiscence overhead and improves throughput, enabling the mannequin to run easily on laptops, GPUs, and even cellular‑class units with out sacrificing high quality.
Regardless of its measurement, Jamba Reasoning 3B helps 256K token contexts, scaling to very lengthy paperwork with out counting on large consideration caches, which makes lengthy‑context inference sensible and value‑efficient
On intelligence benchmarks, it outperforms comparable small fashions comparable to Gemma 3 4B and Llama 3.2 3B on a mixed rating spanning a number of evaluations, demonstrating unusually sturdy reasoning skill for its class.
# 6. Granite 4.0 Micro
Granite-4.0-micro is a 3B‑parameter lengthy‑context instruct mannequin developed by IBM’s Granite staff and designed particularly for enterprise‑grade assistants and agentic workflows.
Wonderful‑tuned from Granite‑4.0‑Micro‑Base utilizing a mix of permissively licensed open datasets and excessive‑high quality artificial knowledge, it emphasizes dependable instruction following, skilled tone, and protected responses, bolstered by a default system immediate added in its October 2025 replace.
The mannequin helps a really giant 128K context window, sturdy instrument‑calling and performance‑execution capabilities, and broad multilingual help spanning main European, Center Japanese, and East Asian languages.
Constructed on a dense decoder‑solely transformer structure with fashionable elements comparable to GQA, RoPE, SwiGLU MLPs, and RMSNorm, Granite‑4.0‑Micro balances robustness and effectivity, making it nicely suited as a basis mannequin for enterprise purposes, RAG pipelines, coding duties, and LLM brokers that should combine cleanly with exterior techniques beneath an Apache 2.0 open‑supply license.
# 7. Phi-4 Mini
Phi-4-mini-instruct is a light-weight, open 3.8B‑parameter language mannequin from Microsoft designed to ship sturdy reasoning and instruction‑following efficiency beneath tight reminiscence and compute constraints.
Constructed on a dense decoder‑solely Transformer structure, it’s educated totally on excessive‑high quality artificial “textbook‑like” knowledge and thoroughly filtered public sources, with a deliberate emphasis on reasoning‑dense content material over uncooked factual memorization.
The mannequin helps a 128K token context window, enabling lengthy‑doc understanding and prolonged conversations unusual at this scale.
Submit‑coaching combines supervised advantageous‑tuning and direct choice optimization, leading to exact instruction adherence, sturdy security habits, and efficient perform calling.
With a big 200K‑token vocabulary and broad multilingual protection, Phi‑4‑mini‑instruct is positioned as a sensible constructing block for analysis and manufacturing techniques that should steadiness latency, price, and reasoning high quality, notably in reminiscence‑ or compute‑constrained environments.
# Last Ideas
Tiny fashions have reached a degree the place measurement is now not a limitation to functionality. The Qwen 3 sequence stands out on this checklist, delivering efficiency that rivals a lot bigger language fashions and even challenges some proprietary techniques. If you’re constructing purposes for a Raspberry Pi or different low-power units, Qwen 3 is a superb start line and nicely price integrating into your setup.
Past Qwen, the EXAONE 4.0 1.2B fashions are notably sturdy at reasoning and non-trivial drawback fixing, whereas remaining considerably smaller than most alternate options. The Ministral 3B additionally deserves consideration as the most recent launch in its sequence, providing an up to date data cutoff and strong general-purpose efficiency.
Total, many of those fashions are spectacular, but when your priorities are velocity, accuracy, and power calling, the Qwen 3 LLM and VLM variants are arduous to beat. They clearly present how far tiny, on-device AI has come and why native inference on small {hardware} is now not a compromise.
Abid Ali Awan (@1abidaliawan) is an authorized knowledge scientist skilled who loves constructing machine studying fashions. Presently, he’s specializing in content material creation and writing technical blogs on machine studying and knowledge science applied sciences. Abid holds a Grasp’s diploma in know-how administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college kids scuffling with psychological sickness.
