Advances in AI mean even low-end machines can now handle powerful language models. Running the best LLM models to run locally gives tech-savvy beginners AI capabilities (writing, coding, analysis) without relying on the cloud. Local LLMs on CPU-only devices offer privacy (no data leaves your PC) and zero subscription fees. Modern lightweight LLMs (2025) are specifically optimized to run on CPUs, making them practical on standard laptops or edge devices. For example, a small 1–2 billion parameter model is often ideal when you prioritize inference speed and efficiency. Below we review five such models (DeepSeek R1, SmolLM2, Llama 3.2, Qwen2.5, Gemma 3), highlighting their features and CPU performance.
DeepSeek R1 (1.5B)
DeepSeek R1 1.5B is a reasoning-focused LLM distilled from Alibaba’s Qwen2.5 models. It’s designed for math and logic tasks and is remarkably efficient. Key points:
- Size & Hardware: 1.5 billion parameters (~1.1 GB model file) with ~16 GB RAM needed for inference. A modern quad-core CPU can run it (though a GPU speeds it up).
- Performance: Excels in math/logic. On a challenging math benchmark (AIME 2024), the 1.5B model scored 28.9% vs. only ~9–16% for GPT-4o/Claude3.5. In other tests it also beats those larger models. Overall, R1 1.5B “outperforms larger models like GPT-4o and Claude-3.5” on reasoning tasks.
- Use Cases: Good for code generation, problem-solving, and concise conversation. It is “distilled” (smaller) yet retains high reasoning power. For users with limited resources, its smaller size means faster local inference.
DeepSeek R1’s advantage is efficiency: it runs on CPU-only setups (with enough RAM) and delivers surprisingly strong output quality for its size.
SmolLM2 (1.7B)
SmolLM2-1.7B is a state-of-the-art compact model from Hugging Face, explicitly trained for on-device use. It was over-trained on ~11 trillion tokens, including specialized math, code, and instruction data. Key points:
- Size & Performance: 1.7 billion parameters, requiring only a few gigabytes of GPU/CPU RAM. Despite being small, SmolLM2 outperforms other recent compact models; the authors report it beats both Qwen2.5-1.5B and Llama 3.2-1B in accuracy.
- Features: Instruction-tuned for general text generation. It includes math and code data, so it handles technical queries well. It’s open-source (with models on Hugging Face) and geared for efficiency.
- Advantages: Among small LLM models, SmolLM2 currently leads in quality. If you want the best local AI model for nuanced text tasks on a CPU, SmolLM2 is a top choice (with a moderate resource increase over 1.5B models). Its outputs are more fluent and knowledgeable compared to most other 1–2B models.
Llama 3.2 (3B)
Meta’s Llama 3.2 (3 billion) is a lightweight new release optimized for edge devices and mobile CPUs. It comes in 1B and 3B text-only variants (plus much larger vision models). Highlights:
- Edge-Optimized: Llama 3.2 is explicitly designed for on-device use. It supports ARM, Qualcomm, and similar processors, and runs efficiently on laptops and phones. The 3B version is the largest in this article and needs more RAM (~10+ GB), but still far less than huge models.
- Capability: Despite its compact size, Llama 3.2 delivers strong performance. Benchmarks show the 3B model scoring ~63.4 on MMLU (higher than some competing mini-models) and excelling at multilingual and reasoning tasks. It even outperforms Gemma 2B on certain tests, though it trails behind some quantized competitors.
- Use Cases: Ideal for general-purpose LLM tasks like summarization, translation, or chat, with the benefit of Meta’s multilingual training. Its smaller context (32K tokens) is still ample for most text. Llama 3.2’s open license and broad community support (e.g. Ollama, Llama.cpp) make it a go-to model for local CPU deployment.
- Benchmarks: In vision-and-text comparisons, Llama 3.2 3B “outperforms models such as Claude 3 Haiku and GPT4o-mini on specific image and text tasks”. For example, it achieved 77.4 on IFEval vs. 61.9 for Gemma 2B and 59.2 for Phi-3.5-mini.
Overall, Llama 3.2 (3B) is a powerful mid-range local LLM – larger than the others here, so it uses more CPU/RAM, but it delivers some of the best quality if your machine can handle it.
Qwen 2.5 (1.5B)
Qwen 2.5-1.5B is part of Alibaba Cloud’s open Qwen family (Apache 2.0 license). The 1.5B version is an instruction-tuned model optimized for long-context and multilingual tasks. Key features:
- Multilingual & Long-Context: Supports 32,768 input tokens (approx. 13–14 pages) and up to 8K output length. It’s trained on 18T tokens and handles 29+ languages (including Chinese, English, Spanish, etc.).
- Generalist Performance: Qwen2.5 1.5B has solid all-around ability. It may not beat SmolLM2 in benchmarks, but it’s well-rounded. Users like it for generating structured output and chat. As a CPU model, it behaves similarly to DeepSeek R1 (both are 1.5B); expect comparable speed and memory (around 1.1–1.3 GB weights, ~15–16 GB RAM).
- Advantages: The open license and multilingual support make it attractive for diverse tasks. It handles code and math fairly well (build on Qwen2 improvements) and can run entirely offline. For someone needing wide language coverage and very long context on a CPU, Qwen2.5 1.5B is a strong option.
Gemma 3 (1B)
Gemma 3 1B is Google’s new open-language model (from Gemini 2.0) available in a very small size. It’s text-only, but it excels in efficiency and context window. Highlights:
- Ultra-Lightweight: Only 1 billion parameters, with 128K token context! Full model uses ~2 GB (bf16) and quantizes down to 0.5 GB with int4 precision. This is extremely low, so Gemma 3 1B can run on even modest hardware (e.g. a laptop with 8–12 GB RAM). Especially with their new Quantization Aware Training (QAT), their 4bit and 8bit models performance are almost idenctical to full precision.
- Capabilities: Despite being tiny, it’s surprisingly capable. Gemma 3 models support 140+ languages and new features like function-calling. It can digest very long documents (128K tokens) and still generate coherent answers.
- Limitations: The 1B variant does not support image input – it’s text-only. Its outputs are less rich than larger Gemma models, but for many tasks (Q&A, summarization) it suffices. Quantization-aware training makes Gemma surprisingly robust even at 4-bit precision.
- Use Cases: Ideal for truly low-end scenarios – e.g. running on a small PC or even embedded device. If your main goal is maximum context and minimal resource use (in return for simpler output), Gemma 3 1B is the best fit.
Gemma’s key advantage is scale: at only 0.5–2 GB size and 128K tokens, it’s perfectly suited to CPU-only setups. It’s an open source LLM for CPU with Google’s backing, making it a prime choice for privacy-focused local AI.
Model Specifications & Performance
Below is a summary comparison of these models’ sizes and CPU requirements:
| Model | Params (B) | Approx. FP16 RAM | Notes (CPU Suitability) |
|---|---|---|---|
| DeepSeek R1 (Distilled) | 1.5 | ~3.5 GB | Quad-core CPU OK; ~16 GB RAM needed (1.1 GB weight). Strong at math/reasoning. |
| SmolLM2 | 1.7 | ~4.0 GB | State-of-art small model; ~16–24 GB RAM recommended. Very high-quality output. |
| Llama 3.2 (Text) | 3.0 | ~6.5 GB | Moderate CPU (6–8 cores) needed; ~32 GB RAM if unquantized. Optimized for on-device. |
| Qwen 2.5 (Instruct) | 1.5 | ~3.5 GB | Similar to DeepSeek R1 in footprint. ~16–24 GB RAM; excels at long-context (32K tokens). |
| Gemma 3 (Text) | 1.0 | 2.0 GB (0.5 GB int4) | Tiny model: ~8–12 GB RAM is enough. Supports huge context (128K). Best for minimal setups. |
(Memory figures are approximate for full precision weights. Quantized (int8/4) versions use significantly less RAM. CPU speed varies by chip and quantization; generally, smaller models (1B) decode faster on a given CPU.)
Running Local LLMs with Kolosal AI
To simplify running these models locally, consider using Kolosal AI. Kolosal AI is an open-source desktop platform that lets you download, run, and chat with local LLMs on your own machine. It is lightweight and prioritizes speed and privacy: “Kolosal AI puts the full potential of large language models on your device with a lightweight, open-source application”. With Kolosal (or similar tools like Ollama), you can easily load any of the above models without complex setup. This way, even beginners can harness CPU-only LLMs with a friendly GUI and minimal configuration.
FAQ
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What is the best LLM for CPU-only devices?
Generally, very small models (1–3B parameters) work best on CPU-only hardware. In practice, our top CPU-friendly picks include Gemma 3 (1B) or DeepSeek R1 (1.5B) for maximum speed, and SmolLM2 (1.7B) for quality. As noted, the 1.5–1.7B range often hits the sweet spot: it’s “ideal” if you want fast inference and low resource use. -
How much RAM do I need to run a local LLM?
It depends on the model size. As a rule of thumb, a 1–2B model typically requires on the order of 8–16 GB RAM, while a 4–7B model needs 16–32 GB. For example, DeepSeek R1 (1.5B) needs ~16 GB. SmolLM2 (1.7B) and Qwen2.5 (1.5B) are similar. The very small Gemma 1B can run in under 8 GB (0.5–2 GB model size). Always allocate extra for the OS and inference process. -
Can I run LLMs locally without a GPU?
Yes. All models listed here can run on CPU-only machines. Modern CPUs (with 4+ cores) are surprisingly capable of handling small LLMs. In fact, CPU inference is often acceptable if you use 4-bit or 8-bit quantization: as one expert notes, “a 4-bit quantized 7B parameter model often performs better than an 8-bit 3B model” on CPU. For best performance on CPU, use quantized weights and a multi-threaded backend (like llama.cpp or Ollama). -
Are lightweight LLMs open source?
Most of them are. For example, Meta’s Llama 3.2 and Google’s Gemma models are released with open weights, and Alibaba’s Qwen2.5 is Apache-licensed. The new SmolLM2 is also publicly released along with its training data. This means you can download and run them locally without license fees. Being open-source also allows community tools like Hugging Face and Kolosal AI to provide easy access.
Conclusion
In summary, the best LLM models to run locally on a CPU in 2025 are those with 1–3 billion parameters. Each of the above models strikes a different balance between size and capability. SmolLM2 (1.7B) offers state-of-the-art accuracy among small models, while Gemma 3 (1B) maximizes efficiency and context length. DeepSeek R1 and Qwen2.5 (both 1.5B) provide excellent reasoning with modest resources, and Llama 3.2 (3B) leverages Meta’s optimizations for edge devices. By choosing any of these, you can get powerful AI on your own hardware without cloud dependencies.
Remember, running local LLMs avoids monthly API costs and keeps your data private. For many users, tools like Kolosal AI make setup straightforward.
For those with more powerful machines, we also have a guide covering mid-range and high-end LLM models on our blog. Check out our related article on mid-range and high-end LLMs to learn how to leverage even larger AI models on robust hardware.