BLK · SMALL LANGUAGE MODELS≤ 3.5B params · edge · phone · laptop

Small Language Models

Every open-weight model under 3.5B parameters that's worth running locally. Phone-class on-device assistants, laptop-class instruction followers, edge VLMs — all in one catalog with real VRAM math and license clarity.

Models curated

Vendors

Commercial OK

60/65

Benchmarked

0/65

Small language models are how local AI actually lands on phones, dev laptops without dGPUs, and battery-constrained devices. A 1B or 3B model that runs at 30 tok/s on a Pixel 9 beats a 70B model that takes 8 minutes to load on the same machine — operator math, not vendor math.

The frontier has shifted: Qwen 3 0.6B clears 20M HuggingFace downloads, Gemma 3 270M went viral on r/LocalLLaMA, and HuggingFace's own SmolLM2 family hits production speech assistants. This hub catalogs the ≤3.5B tier with the same depth of editorial we give the flagship 70B models — license trap, context ceiling, missing GGUF, all called out.

Each row links to /models/[slug] for the full operator notes: tested hardware, recommended quantization, and the prompting kit that worked in our test runs. Where we've benchmarked, the score sits next to the model. Where we haven't, the row says so — no fake numbers.

FAM · OTHER

Other / from-scratch

28 models

SigLIP SO400M (patch14-384)

428M params · Google

▸ Zero-shot image classification, image-text retrieval, or as a frozen vision tower for a custom VLM on edge/consumer hardware

428M-parameter Shape-Optimized vision-language encoder trained with the sigmoid (not softmax) contrastive loss on WebLI. Hits ~83% zero-shot ImageNet-1k top-1 at 384px — the strongest open contrastive encoder in its size

License: apache-2.0 · OK
Context: —

SmolLM2 135M Instruct

135M params · Hugging Face

▸ In-browser WebGPU chat demo and edge autocomplete

SmolLM2-135M-Instruct is the smallest instruction-tuned model in Hugging Face's SmolLM2 family, a 135M-parameter Llama-architecture model trained for on-device deployment. It uses an 8K context window and is shipped with

License: apache-2.0 · OK
Context: 8K

Florence-2 Large

770M params · Microsoft

▸ Edge-tier unified caption / OCR / detection / grounding pipeline where you want one model instead of four

770M-parameter unified vision foundation model with a DaViT image encoder and BART-style seq2seq decoder. One model, one set of weights — handles captioning, OCR, region/grounding, segmentation, and dense detection via t

License: mit · OK
Context: —

TinyLlama 1.1B Chat v0.3 GPTQ

1.1B params · TheBloke

judged 9.0/10

▸ Lightweight English chatbot on severely VRAM-constrained hardware

GPTQ-quantized build of TinyLlama 1.1B Chat v0.3, trained on SlimPajama, StarCoder, and OpenAssistant data. Runs in roughly 0.8 GB VRAM thanks to 4-bit quantization. English only, 2048-token context window.

License: apache-2.0 · OK
Context: 2K

TinyLlama 1.1B Chat v0.3 AWQ

1.1B params · Zhang Peiyuan

judged 9.1/10

▸ Low-resource English chatbot prototyping

TinyLlama 1.1B Chat v0.3 is a 1.1B-parameter chat model quantized to 4-bit AWQ by TheBloke. It uses the ChatML prompt format and fits comfortably in very low VRAM environments. Context is capped at 2048 tokens.

License: apache-2.0 · OK
Context: 2K

Kanarya 2B

2B params · asafaya

Turkish-from-scratch language model trained by Ali Safaya (Koç University researcher). Named after the kanarya (Turkish for 'canary'). Trained on 250+ GB of Turkish text including Wikipedia, news, and books.

License: Apache-2.0 · OK
Context: 2K

SmolLM2 360M Instruct

360M params · Hugging Face

▸ On-device assistant for Raspberry Pi-class hardware

SmolLM2-360M-Instruct is the middle tier of the SmolLM2 instruct family, a 360M-parameter Llama-architecture model with an 8K context. It is shipped with ONNX and Transformers.js artifacts and aimed at on-device assistan

License: apache-2.0 · OK
Context: 8K

Kanarya 750M

750M params · asafaya

Smaller Kanarya variant — 750M parameters. Runs on CPU or 4GB GPU comfortably. Useful for low-resource Turkish text classification, embeddings, or completion tasks where latency matters more than quality.

License: Apache-2.0 · OK
Context: 2K

VBART Large (Turkish Summarization)

400M params · VNGRS

Turkish BART-style sequence-to-sequence model fine-tuned specifically for summarization. Not a chat model — purpose-built for input-document → Turkish-summary pipelines.

License: Apache-2.0 · OK
Context: 1K

Turkish GPT-2 Large

700M params · ytu-ce-cosmos

GPT-2 Large architecture trained from scratch on Turkish. Reference baseline for measuring how much modern instruction-tuned models actually improve on the GPT-2 era.

License: MIT · OK
Context: 1K

SmolVLM Instruct

2.25B params · Hugging Face

▸ Lowest-VRAM open VLM for image captioning on consumer GPU

SmolVLM-Instruct is Hugging Face's compact vision-language model built on the Idefics3 architecture, pairing SmolLM2-1.7B-Instruct with a SigLIP-SO400M vision encoder. It is engineered for minimum VRAM footprint and ship

License: apache-2.0 · OK
Context: 8K

LFM2.5-230M

230M params · Liquid AI

▸ On-device tool calling and data extraction on phones and Raspberry Pi-class hardware

LFM2.5-230M is Liquid AI's smallest LFM2.5 model: a 230M-parameter, text-only hybrid (14 layers — 8 double-gated LIV convolution blocks plus 6 GQA attention blocks) trained on a 19T-token budget and distilled from LFM2.5

License: LFM Open License · OK
Context: 32K

GPT-2 Spanish

124M params · DeepESP

judged 9.3/10

▸ Spanish text completion or language modeling research

GPT-2 Spanish is a 124M-parameter model trained from scratch on 11.5GB of Spanish text (Wikipedia, books) with a custom Spanish BPE tokenizer. It generates Spanish prose but is not instruction-tuned — it completes text,

License: mit · OK
Context: 1K

GPT-2 Spanish Medium

355M params · DeepESP

judged 9.2/10

▸ Lightweight Spanish text generation or fine-tuning base

A 355M-parameter GPT-2 Medium trained from scratch on 11.5 GB of Spanish text (Wikipedia and books), with a BPE tokenizer built specifically for Spanish. Context window is 1024 tokens. Training data was not filtered for

License: mit · OK
Context: 1K

gpt2-base-french

124M params · ClassCat

judged 9.0/10

▸ French text completion research or academic prototyping

A 124M-parameter GPT-2 base model trained on French Wikipedia (wiki40b/fr) and a CC-100/fr subset, with a 50,000-token BPE vocabulary. It generates French text but has no instruction-following capability. Context window

License: cc-by-sa-4.0
Context: 1K

OpenELM 3B Instruct

3B params · Apple

▸ Academic study of layer-wise scaled transformer architectures

OpenELM-3B-Instruct is Apple's 3-billion-parameter instruct model using a layer-wise scaled transformer with varying FFN multipliers and KV-head counts across 36 layers. It is released under the Apple Sample Code License

License: apple-amlr
Context: 2K

mGPT 1.3B Uzbek

1.3B params · ai-forever

judged 9.4/10

▸ Uzbek-language text generation and corpus experimentation

A 1.3B-parameter GPT-2-style model fine-tuned on Uzbek text for 50,000 steps on a single A100. Covers Uzbek, Russian, and English generation. It is a base model only — no instruction tuning.

License: mit · OK
Context: 2K

Dostoevsky Doesn't Write It GPT2

175M params · evilfreelancer

judged 9.1/10

▸ Dostoevsky-style Russian prose generation for creative or novelty projects

A 175M-parameter GPT-2 model fine-tuned on Dostoevsky's digitized works, built on top of ruGPT3-small. Trained for five epochs, it generates Russian prose in a 19th-century literary register. Context tops out at 1024 tok

License: mit · OK
Context: 1K

mGPT 1.3B Mongol

1.3B params · ai-forever

judged 9.3/10

▸ Mongolian-language text generation and basic NLP prototyping

A 1.3B-parameter GPT model fine-tuned from ai-forever's mGPT base for Mongolian, with English and Russian also supported. Fine-tuning ran for 50,000 steps on Mongolian-specific data, yielding a validation perplexity of 4

License: mit · OK
Context: 2K

Vikhr Qwen 2.5 0.5B Instruct

500M params · Vikhrmodels

judged 9.0/10

▸ Russian-language mobile chatbot or on-device assistant

A 0.5B Russian-language instruct model fine-tuned from Qwen2.5-0.5B on the GrandMaster-PRO-MAX dataset (~150k instructions). Vikhrmodels claims 4x efficiency over the base Qwen2.5-0.5B, and the quantized footprint lands

License: apache-2.0 · OK
Context: 4K

Whisper Large v3 Turbo

810M params · OpenAI

▸ real-time / batch transcription

Distilled Whisper Large v3. ~8x faster decode at near-equivalent accuracy on most languages.

License: MIT · OK
Context: —

SmolLM 3 3B

3B params · HuggingFace

▸ edge-tier reasoning

HuggingFace's small-model line at 3B. Apache 2.0. Designed for edge / educational deployments.

License: Apache 2.0 · OK
Context: 32K

Moondream 2

1.9B params · vikhyat (community)

▸ edge / phone-tier vision Q&A

Tiny vision-language model. ~1.9B; designed for edge / embedded multimodal use cases. Apache 2.0.

License: Apache 2.0 · OK
Context: 2K

SmolLM 2 1.7B Instruct

1.7B params · Hugging Face

▸ edge-tier Apache 2.0 baseline

SmolLM 2 flagship. Open data + open weights at the edge tier.

License: Apache 2.0 · OK
Context: 8K

Whisper Large v3

1.55B params · OpenAI

▸ open speech-to-text baseline

OpenAI's flagship open speech-to-text model. 99 languages, MIT license. The de-facto open ASR baseline.

License: MIT · OK
Context: —

BGE M3

570M params · BAAI

▸ multilingual RAG embeddings

BAAI's multilingual embedding flagship. Dense + sparse + ColBERT-style multi-vector. The de-facto open multilingual embedding pick.

License: MIT · OK
Context: 8K

StarCoder 2 3B

3B params · BigCode

▸ edge-tier code completion

BigCode's StarCoder 2 at 3B. Trained on The Stack v2 with 600+ programming languages.

License: BigCode OpenRAIL · OK
Context: 16K

SmolLM 2 360M Instruct

360M params · Hugging Face

▸ phone / Pi-class chat

Hugging Face's SmolLM 2 at 360M. Apache 2.0; targets phone / Pi-class deployments.

License: Apache 2.0 · OK
Context: 8K

FAM · QWEN

Qwen-based

12 models

Qwen 3 0.6B

600M params · Alibaba

▸ Sub-1B on-device chat and tool-calling agent on phones

Qwen3-0.6B is the smallest dense model in Alibaba's Qwen3 generation, supporting a 40K-token context and dual-mode operation that toggles between explicit reasoning ('think') and fast direct response. It is post-trained

License: apache-2.0 · OK
Context: 40K

Qwen 3 1.7B

1.7B params · Alibaba

▸ Edge laptop assistant with reasoning that fits in 2GB VRAM

Qwen3-1.7B is the mid-tier dense model in Qwen3, sharing the same hybrid thinking architecture and 40K context as the 0.6B but with ~3x the parameters for noticeably stronger reasoning, math, and code. It targets the con

License: apache-2.0 · OK
Context: 40K

Qwen2-VL 2B Instruct

2B params · Alibaba

▸ Lightweight document and chart understanding on a consumer GPU

Qwen2-VL 2B Instruct is Alibaba's compact vision-language model with native dynamic-resolution image handling and multimodal RoPE (M-RoPE) for video and multi-image inputs. It supports 32K-token context and is Apache-2.0

License: apache-2.0 · OK
Context: 32K

VibeThinker-3B

3B params · WeiboAI (Sina Weibo)

▸ Compact MIT reasoning model that runs on a single consumer GPU (~6.7GB)

VibeThinker-3B is a compact open-weight reasoning model from WeiboAI (Sina Weibo), fine-tuned from Qwen2.5-Coder-3B (Hugging Face `WeiboAI/VibeThinker-3B`, 2026-06). It is a dense ~3B-parameter model (runs in roughly 6.7

License: MIT · OK
Context: 128K

Qwen 3.5 2B Turkish SFT

2B params · Tuguberk

Qwen 3.5 2B base with supervised fine-tuning on Turkish instruction-following data. Recent community fine-tune (early 2026) that bridges Qwen 3.5's strong multilingual base with Turkish-specific chat capability.

License: Apache-2.0 · OK
Context: 32K

Qwen3 0.6B Hindi Instruct v1 GGUF

600M params · pankajpandey-dev

judged 9.1/10

▸ Simple Hindi instruction following on CPU-only devices

A 0.6B Qwen3 model fine-tuned on English-to-Hindi instruction pairs and quantized to GGUF. Fits in 370MB and runs on CPU-only hardware. Trained on 2,000 instruction pairs, so scope is narrow.

License: apache-2.0 · OK
Context: 2K

Qwen 2.5 0.5B Instruct

500M params · Alibaba

▸ phone-tier Qwen baseline

Smallest Qwen 2.5. Apache 2.0; phone / Pi-class deployment target.

License: Apache 2.0 · OK
Context: 32K

Qwen 2.5 1.5B Instruct

1.5B params · Alibaba

▸ edge-tier Apache 2.0 chat

Compact Qwen 2.5. The 1.5B Apache-2.0 baseline.

License: Apache 2.0 · OK
Context: 32K

Qwen 2.5 3B Instruct

3B params · Alibaba

▸ edge-tier Qwen 2.5 chat

Mid-edge Qwen 2.5. Note: 3B variant uses Qwen License (not Apache 2.0).

License: Qwen License · OK
Context: 32K

Qwen 2.5-VL 3B

3B params · Alibaba

▸ edge-tier multimodal

Smallest Qwen 2.5-VL. Edge-deployable VLM with strong document Q&A.

License: Qwen License · OK
Context: 32K

Qwen 2.5 Coder 3B

3B params · Alibaba

▸ Apple Silicon laptop coding autocomplete

Compact Qwen 2.5 Coder. Sweet spot for laptop autocomplete and small refactor agents.

License: Apache 2.0 · OK
Context: 32K

Qwen 2.5 Coder 1.5B

1.5B params · Alibaba

▸ IDE autocomplete on integrated GPUs

Smallest Qwen 2.5 Coder. Targets edge / autocomplete on integrated GPUs and Apple Silicon laptops.

License: Apache 2.0 · OK
Context: 32K

FAM · GEMMA

Gemma-based

6 models

Gemma 3 270M

270M params · Google

▸ Fine-tuning base for sub-1W on-device classifiers and routers

Gemma 3 270M is the smallest member of Google's Gemma 3 family, a 270-million-parameter text-only model designed for on-device deployment and task-specific fine-tuning. It carries the Gemma license and Google's acceptabl

License: gemma · OK
Context: 32K

Gemma 2 2B Instruct

2B params · Google

▸ Consumer-GPU local chat with strong safety defaults

Gemma 2 2B Instruct is Google's instruction-tuned 2B model from the Gemma 2 generation, trained with knowledge distillation from larger Gemma models. It targets the consumer-GPU and high-end mobile tier with an 8K contex

License: gemma · OK
Context: 8K

Gemma 4 E2B (Effective 2B)

2B params · Google

▸ phone-tier Gemma 4

Smallest Gemma 4. Designed for phones and Raspberry-Pi-class hardware.

License: Gemma Terms of U · OK
Context: 128K

Gemma 3 1B

1B params · Google

▸ phone-tier Gemma — smallest practical Gemma 3

Smallest text-only Gemma 3 for phones and IoT.

License: Gemma Terms of U · OK
Context: 32K

ColPali v1.3

3B params · ColPali team (Illuin Technology)

▸ Visual-document retrieval for multi-page PDFs with charts, tables, and scans where OCR pipelines fail

3B-parameter visual document retriever built on PaliGemma-3B using a ColBERT-style late-interaction objective. Encodes a PDF page as a grid of patch embeddings, skipping OCR/layout parsing entirely. Sets SOTA on the ViDo

License: mit · OK
Context: —

PaliGemma 2 3B

3B params · Google

▸ task-specific VLM fine-tuning base

PaliGemma 2 — Gemma 2 base + SigLIP vision encoder. Designed for fine-tuning on specific vision tasks.

License: Gemma License · OK
Context: 8K

FAM · LLAMA

Llama-based

5 models

Llama 3.2 3B Instruct

3B params · Meta

judged 7.4/10

▸ battery-powered laptop chat tier

Lightweight 3B model for edge and laptop deployment. Check the quantized artifact and context against available memory. The earlier Apple Silicon speed estimate had no linked measurement.

License: Llama 3.2 Commun · OK
Context: 128K

TinyLlama 1.1B Chat v1.0

1.1B params · TinyLlama

▸ Reproducible SLM research baseline and legacy llama.cpp deployments

TinyLlama-1.1B-Chat-v1.0 is a 1.1B Llama-2-architecture model pretrained on 3 trillion tokens and chat-tuned on UltraChat and UltraFeedback. It was one of the earliest production-grade SLMs and remains a popular base mod

License: apache-2.0 · OK
Context: 2K

Llama 3.2 1B Instruct

1B params · Meta

judged 6.0/10

▸ edge / phone-tier chat — smallest practical Llama

True edge-tier Llama. Runs on a phone or Raspberry Pi. Useful for classification, simple summarization, and on-device agents.

License: Llama 3.2 Commun · OK
Context: 128K

Salamandra 2B

2.25B params · BSC-LT

judged 9.4/10

▸ Fine-tuning base for Spanish or Catalan/Galician/Basque NLP tasks

Salamandra 2B is a base-only transformer trained from scratch by Barcelona Supercomputing Center on 12.875 trillion tokens across 35 European languages and code. At 2.25B parameters and an 8192-token context window, it i

License: apache-2.0 · OK
Context: 8K

Salamandra 2B Instruct

2B params · BSC

judged 9.4/10

▸ Spanish and European multilingual instruction following on low-VRAM hardware

Salamandra 2B Instruct is a transformer model from BSC pretrained from scratch on 12.875 trillion tokens across 35 European languages and code. The instruct variant is fine-tuned for instruction following using the ChatM

License: apache-2.0 · OK
Context: 8K

FAM · GRANITE

Granite-based

3 models

Granite 4.1 3B Instruct

3.4B params · IBM

▸ Edge-deployed function calling and RAG extraction where every GB of memory counts

Granite 4.1 3B Instruct is the smallest of IBM's Granite 4.1 language models, released April 29, 2026 under Apache 2.0. It is a dense, decoder-only transformer — 3.4B parameters, 40 layers, grouped-query attention, RoPE,

License: Apache-2.0 · OK
Context: 128K

Granite 3.1 2B Instruct

2B params · IBM

▸ Enterprise RAG and tool-use with vendor indemnification

Granite 3.1 2B Instruct is IBM's 2B-parameter dense instruct model with a 128K context window, post-trained for enterprise tasks including RAG, function calling, and structured citation generation. It is part of IBM's Ap

License: apache-2.0 · OK
Context: 128K

Granite 3.0 2B Instruct

2B params · IBM

▸ edge-tier IBM Granite

IBM Granite at 2B. Apache 2.0 enterprise-friendly small model with safety tuning.

License: Apache 2.0 · OK
Context: 4K

FAM · MISTRAL

Mistral-based

2 models

Kumru 2B

2.4B params · VNGRS

▸ fast Turkish edge chat

Kumru 2B is a compact Turkish text-generation model from VNGRS. The Hugging Face config reports a Mistral-family architecture with an 8K context window, and the public Ollama build makes it a practical edge-speed Turkish

License: Apache-2.0 · OK
Context: 8K

Ministral 3B Instruct

3B params · Mistral AI

▸ edge-tier long-context — research only

Mistral edge model at 3B. Designed for on-device inference with extended 128k context. Research license only.

License: Mistral Research
Context: 128K

FAM · EXAONE

EXAONE-based

2 models

EXAONE 3.5 2.4B Instruct

2.4B params · LG AI Research

judged 9.3/10

▸ Korean/English bilingual research prototyping on edge hardware

EXAONE 3.5 2.4B Instruct is LG AI Research's bilingual English/Korean model built for low-resource devices. It handles up to 32K context tokens and shows competitive results on Korean-specific benchmarks like KoMT-Bench

License: other (EXAONE AI
Context: 32K

EXAONE 3.5 2.4B

2.4B params · LG AI Research

▸ edge-tier Korean chat

LG AI's edge-tier EXAONE. Strong Korean / English. Research-only license.

License: EXAONE AI Model
Context: 32K

FAM · STEPFUN

StepFun-based

1 model

GOT-OCR 2.0

580M params · StepFun AI

▸ Self-hosted OCR for printed formulas, tables, and dense scientific PDFs to LaTeX/Markdown

580M-parameter end-to-end OCR-2.0 model: a vision encoder paired with a Qwen-based decoder, trained specifically for general OCR including math formulas (LaTeX out), tables (Markdown/HTML out), sheet music, geometric sha

License: apache-2.0 · OK
Context: —

FAM · OLMO

OLMo-based

1 model

OLMo 2 1B Instruct

1B params · AllenAI

▸ Research baseline where full training reproducibility is required

OLMo 2 1B Instruct is AllenAI's 1-billion-parameter instruct model from the April 2025 OLMo 2 release, post-trained with RLVR on math. It is fully open: weights, training data, training code, and intermediate checkpoints

License: apache-2.0 · OK
Context: 4K

FAM · FALCON

Falcon-based

1 model

Falcon 3 3B Instruct

3B params · TII

▸ Multilingual European chat where Falcon license is acceptable

Falcon 3 3B Instruct is TII's 3-billion-parameter instruct model from the Falcon 3 family, supporting English, French, Spanish, and Portuguese with a 32K context window. It uses the Llama architecture for runtime compati

License: falcon-llm-licen · OK
Context: 32K

FAM · DOLPHIN