Best AI Models to Run Locally on Your Device (2026)
AI models are getting increasingly efficient over the years. If the same pace continues, we might get the best models to run locally on your device. The recent announcement of Google Gemma 4 is one of the best examples.
These models are very capable of generating text, summarizing documents, and even parsing text from images. They are a great way to run locally on your device without device limits, ensuring all data stays on your device.
Not just the Google Gemma models, there are plenty of other models you can run locally on your device. In a separate post on TechPP.com, we covered the 10 best AI models to run locally and explained why they are better and what kind of use cases they are best suited for. This guide focuses completely on tracking top models that you can run locally on your device.
| # | Model | Family | Params | Q4 Size | Min RAM / VRAM | Best For | License |
|---|---|---|---|---|---|---|---|
| 1 | SmolLM2 135M | SmolLM | 0.135B | 0.1 GB | 4-8 GB RAM (CPU OK) | Ultra-tiny assistant, on-device / embedded. | Apache-2.0 |
| 2 | SmolLM2 360M | SmolLM | 0.36B | 0.25 GB | 4-8 GB RAM | Tiny but stronger reasoning vs 135M. | Apache-2.0 |
| 3 | SmolLM2 1.7B | SmolLM | 1.7B | 1.2 GB | 8 GB RAM | Best quality in SmolLM2 family; edge devices. | Apache-2.0 |
| 4 | Gemma 3 270M | Gemma | 0.27B | 0.2 GB | 4-8 GB RAM | Tiny Google model, quick queries, mobile. | Gemma (free with conditions) |
| 5 | Gemma 3 1B | Gemma | 1B | 0.7 GB | 8 GB RAM | Small general chat, multilingual. | Gemma (restricted but free) |
| 6 | Gemma 3 4B | Gemma | 4B | 2 GB | 8-12 GB RAM / 4 GB VRAM | Strong small model; 128K context, text+image. | Gemma license |
| 7 | Gemma 3 12B | Gemma | 12B | 6 GB | 16-24 GB RAM / 10-12 GB VRAM | High-quality general + vision, 128K context. | Gemma license |
| 8 | Gemma 3 27B | Gemma | 27B | 13-14 GB | 32+ GB RAM / 18-20 GB VRAM | Flagship consumer-tier long-context model. Usable on single 16-24 GB GPU in Q4. | Gemma license |
| 9 | Granite3-MoE 1B | Granite | 1B | 0.7 GB | 8 GB RAM | IBM MoE, very low-latency on-device. | IBM Granite (commercial-friendly) |
| 10 | Granite3-MoE 3B | Granite | 3B | 1.5-2 GB | 8-12 GB RAM | On-device chat, multilingual. | IBM Granite license |
| 11 | Granite 3.1 dense 2B | Granite | 2B | 1-1.2 GB | 8-12 GB RAM | Text-only, RAG, tool-use support. | IBM Granite license |
| 12 | Granite 3.1 dense 8B | Granite | 8B | 4 GB | 16 GB RAM / 6-8 GB VRAM | Enterprise-style RAG, code, multilingual. | IBM Granite license |
| 13 | Phi-2 | Phi | 2.7B | 1.3 GB | 8-12 GB RAM | Older but efficient Microsoft model. | MIT-like permissive (Phi family) |
| 14 | Phi-3 Mini 3.8B | Phi | 3.8B | 1.8-2 GB | 8-12 GB RAM / 4 GB VRAM | Fast, very good quality for size, great on low-RAM PCs. | MIT/permissive; free for commercial |
| 15 | Phi-4 Mini 3.8B | Phi | 3.8B | 1.8-2 GB | 8-12 GB RAM / 4 GB VRAM | Newer mini; better reasoning & coding vs Phi-3. | Phi family: permissive open weights |
| 16 | Phi-4 14B | Phi | 14B | 7 GB | 24-32 GB RAM / 10-12 GB VRAM | Math/STEM reasoning; efficiency vs much larger models. | Permissive (Phi), commercial OK |
| 17 | Llama 3.1 8B Instruct | Llama | 8B | 4 GB | 16 GB RAM / 6-8 GB VRAM | Strong daily driver chat/coding, 128K context. | Llama license (open, some MAU limits) |
| 18 | Llama 3.1 70B | Llama | 70B | 35 GB | 64+ GB RAM / 40 GB VRAM | Near-frontier quality if you have big GPU(s). | Llama license |
| 19 | Llama 3.2 7B-class | Llama | 7B | 3.5-4 GB | 16 GB RAM / 6-8 GB VRAM | Excellent 7B-tier general model on laptops. | Llama license |
| 20 | Llama 3.2 11B | Llama | 11B | 5-6 GB | 24 GB RAM / 8-10 GB VRAM | RAG/Q&A, strong instruction following. | Llama license |
| 21 | Llama 3.3 8B | Llama | 8B | 4 GB | 16 GB RAM / 6-8 GB VRAM | Best all-around 8B in 2026 for many devs. | Llama license |
| 22 | Llama 3.3 70B | Llama | 70B | 35 GB | 64+ GB RAM / 40 GB VRAM | Best overall local open model if you have 40 GB VRAM. | Llama license |
| 23 | Qwen 2.5 7B | Qwen | 7B | 3.5-4 GB | 16 GB RAM / 6-8 GB VRAM | Multilingual 7B; very strong coding & chat. | Apache-2.0 (commercial) |
| 24 | Qwen 2.5 Coder 32B | Qwen | 32B | 16 GB | 32-48 GB RAM / 20-24 GB VRAM | Top local coding model (HumanEval > 90%). | Apache-2.0 |
| 25 | Qwen 3 1.7B | Qwen | 1.7B | 0.8-1 GB | 8 GB RAM | Tiny but capable multilingual model. | Apache-2.0 |
| 26 | Qwen 3 4B | Qwen | 4B | 2 GB | 8-12 GB RAM / 4 GB VRAM | Balanced small model with 32K context. | Apache-2.0 |
| 27 | Qwen 3 8B | Qwen | 8B | 4 GB | 16 GB RAM / 6-8 GB VRAM | Strong general + coding, 128K context. | Apache-2.0 |
| 28 | Qwen 3 30B A3B (MoE) | Qwen | 30B (3B active) | 15 GB | 32-48 GB RAM / 19-24 GB VRAM | MoE: high quality but efficient per-token compute. | Apache-2.0 |
| 29 | Qwen 3.5 27B | Qwen | 27B | 13-14 GB | 32-48 GB RAM / 18-20 GB VRAM | High-end dense model; great long-context. | Apache-2.0 |
| 30 | Qwen 3.5 72B MoE | Qwen | 72B (14B active) | 36 GB | 64+ GB RAM / 48 GB VRAM | Best overall open local model on a big GPU. | Apache-2.0 |
| 31 | Qwen 3 VL 7B | Qwen | 7B | 3.5-4 GB | 16 GB RAM / 8 GB VRAM | Vision-language: screenshots, OCR, image Q&A. | Apache-2.0 |
| 32 | Mistral 7B v0.3 | Mistral | 7B | 3.5-4 GB | 16 GB RAM / 6-8 GB VRAM | Classic 7B workhorse; fast and efficient. | Apache-2.0 (Mistral permissive) |
| 33 | Mistral Small 3 (7B) | Mistral | 7B | 3.5-4 GB | 16 GB RAM / 6-8 GB VRAM | Very fast 7B; excellent for dev workflows. | Apache-2.0 |
| 34 | Mistral NeMo (8-12B) | Mistral | 8-12B | 4-6 GB | 24 GB RAM / 8-10 GB VRAM | Business/RAG, document analysis. | Apache-2.0 for newer releases |
| 35 | Mixtral 8x7B | Mistral | 8x7B (MoE) | 26 GB (GGUF) | 32-48 GB RAM / 24 GB VRAM | Strong MoE model for analysis & code. | Apache-2.0 |
| 36 | Mixtral 8x22B | Mistral | 8x22B | 80 GB | Multi-GPU / 80 GB RAM | Very large MoE; server-class local deployment. | Apache-2.0 |
| 37 | Mistral Large 2 (22B) | Mistral | 22B | 11 GB | 32-48 GB RAM / 18-20 GB VRAM | Top open model for code generation. | Apache-2.0 |
| 38 | CodeLlama 7B | Llama | 7B | 3.5-4 GB | 16 GB RAM / 6-8 GB VRAM | Lightweight multi-language coding helper. | Llama license |
| 39 | CodeLlama 13B | Llama | 13B | 6-7 GB | 24-32 GB RAM / 10-12 GB VRAM | Better long-form coding, complex refactors. | Llama license |
| 40 | CodeLlama 34B | Llama | 34B | 17 GB | 48+ GB RAM / 20-25 GB VRAM | Heavy code understanding & large repos. | Llama license |
| 41 | DeepSeek Coder 33B | DeepSeek | 33B | 16-17 GB | 48+ GB RAM / 20-24 GB VRAM | Advanced coding & debugging; top tier. | MIT/permissive (DeepSeek) |
| 42 | DeepSeek R1 Distill Qwen 32B | DeepSeek | 32B | 16 GB | 48+ GB RAM / 20-24 GB VRAM | Reasoning-focused distilled model; strong chain-of-thought. | MIT/permissive |
| 43 | DeepSeek V3.2 (70B) | DeepSeek | 70B | 35 GB | 64+ GB RAM / 40 GB VRAM | Frontier-level open dense model. | MIT/permissive |
| 44 | DeepSeek-V4 (236B MoE) | DeepSeek | 236B (MoE) | 118+ GB | Multi-GPU (2x48 GB+) | Strongest open reasoning; overkill for most PCs. | MIT/permissive |
| 45 | Solar 10.7B | Other | 10.7B | 5-6 GB | 24 GB RAM / 8-10 GB VRAM | Good analytical & summarization model. | Open weights (check per-model terms) |
| 46 | Vicuna 13B | Other | 13B | 6-7 GB | 24-32 GB RAM / 10-12 GB VRAM | Conversational fine-tune of Llama; still popular offline. | Research/non-commercial; check license |
| 47 | OpenHermes 2.5 (7B) | Other | 7B | 3.5-4 GB | 16 GB RAM / 6-8 GB VRAM | Instruction-following chat model, good alignment. | Open weights (Apache-2.0-style) |
| 48 | Command R (35B) | Cohere | 35B | 17-18 GB | 48+ GB RAM / 20-24 GB VRAM | Enterprise RAG & tools; text-only. | Open weights; generally commercial-friendly |
| 49 | Command R+ (104B) | Cohere | 104B | 52 GB | Multi-GPU or 80+ GB RAM | Higher quality but non-commercial / research-only. | CC-BY-NC (non-commercial) |
| 50 | Nemotron 3 Nano 30B | NVIDIA | 30B | 15 GB | 48+ GB RAM / 20-24 GB VRAM | NVIDIA open model; strong general + code. | NVIDIA open license (generally free) |
Q4 = 4-bit quantized GGUF. RAM/VRAM figures are minimums for smooth inference. Actual requirements vary with context window length and system overhead.
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