What are Small LLMs for On-Device Chatbots?
Small LLMs for on-device chatbots are compact, efficient large language models optimized to run directly on edge devices such as smartphones, tablets, and IoT devices without requiring cloud connectivity. These models typically range from 7B to 9B parameters, striking an optimal balance between conversational capability and computational efficiency. They enable real-time dialogue, multilingual support, and task-specific reasoning while maintaining user privacy and reducing latency. By running locally, these models democratize access to AI-powered conversational interfaces, enabling developers to build responsive, privacy-preserving chatbot applications across a wide range of devices and use cases.
Meta-Llama-3.1-8B-Instruct
Meta Llama 3.1 is a family of multilingual large language models developed by Meta, featuring pretrained and instruction-tuned variants in 8B, 70B, and 405B parameter sizes. This 8B instruction-tuned model is optimized for multilingual dialogue use cases and outperforms many available open-source and closed chat models on common industry benchmarks. The model was trained on over 15 trillion tokens of publicly available data, using techniques like supervised fine-tuning and reinforcement learning with human feedback to enhance helpfulness and safety.
Meta-Llama-3.1-8B-Instruct: Multilingual Excellence for On-Device Chat
Meta Llama 3.1 8B Instruct is a powerful multilingual large language model optimized for dialogue use cases. With 8 billion parameters, this instruction-tuned variant is specifically designed for efficient on-device deployment while maintaining competitive performance against larger models. Trained on over 15 trillion tokens using advanced techniques including supervised fine-tuning and reinforcement learning with human feedback, it delivers enhanced helpfulness and safety. The model supports a 33K context length and excels in text and code generation tasks, making it ideal for building responsive, multilingual chatbots that run locally on edge devices. With a knowledge cutoff of December 2023, it provides up-to-date conversational capabilities.
Pros
- Optimized for multilingual dialogue with 8B parameters.
- Trained on 15 trillion tokens with RLHF for safety.
- Outperforms many open-source chat models on benchmarks.
Cons
- Knowledge cutoff at December 2023.
- May require optimization for smallest edge devices.
Why We Love It
- It delivers industry-leading multilingual chat performance in a compact 8B package, making it the perfect foundation for on-device conversational AI applications.
Qwen3-8B
Qwen3-8B is the latest large language model in the Qwen series with 8.2B parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue). It demonstrates significantly enhanced reasoning capabilities, surpassing previous QwQ and Qwen2.5 instruct models in mathematics, code generation, and commonsense logical reasoning.
Qwen3-8B: Dual-Mode Intelligence for Smart On-Device Assistants
Qwen3-8B is the latest innovation in the Qwen series, featuring 8.2B parameters with a groundbreaking dual-mode capability. This model seamlessly switches between thinking mode for complex logical reasoning, mathematics, and coding tasks, and non-thinking mode for efficient general-purpose dialogue. It significantly outperforms previous generations in mathematical reasoning, code generation, and commonsense logic. The model excels in human preference alignment for creative writing, role-playing, and multi-turn dialogues. With support for over 100 languages and dialects, strong multilingual instruction following, and an impressive 131K context length, Qwen3-8B is ideal for sophisticated on-device chatbot applications that demand both conversational fluency and deep reasoning capabilities.
Pros
- Unique dual-mode switching for reasoning and dialogue.
- Enhanced math, coding, and logical reasoning capabilities.
- Supports over 100 languages and dialects.
Cons
- Slightly larger parameter count may require more resources.
- Dual-mode complexity may need specific implementation.
Why We Love It
- Its innovative dual-mode architecture makes it the most versatile on-device LLM, seamlessly handling everything from casual chat to complex problem-solving in a single compact model.
THUDM/GLM-4-9B-0414
GLM-4-9B-0414 is a small-sized model in the GLM series with 9 billion parameters. This model inherits the technical characteristics of the GLM-4-32B series but offers a more lightweight deployment option. Despite its smaller scale, GLM-4-9B-0414 still demonstrates excellent capabilities in code generation, web design, SVG graphics generation, and search-based writing tasks. The model also supports function calling features, allowing it to invoke external tools to extend its range of capabilities.
THUDM/GLM-4-9B-0414: Lightweight Powerhouse with Tool Integration
GLM-4-9B-0414 is a compact yet powerful model in the GLM series with 9 billion parameters. Inheriting technical characteristics from the larger GLM-4-32B series, this lightweight variant offers exceptional deployment efficiency without sacrificing capability. The model demonstrates excellent performance in code generation, web design, SVG graphics creation, and search-based writing tasks. Its standout feature is function calling support, enabling it to invoke external tools and extend its capabilities beyond native functions. With a 33K context length and competitive performance in benchmark tests, GLM-4-9B-0414 achieves an optimal balance between efficiency and effectiveness, making it ideal for on-device chatbot applications in resource-constrained scenarios where tool integration is valuable.
Pros
- Inherits advanced features from larger GLM-4 models.
- Excellent code generation and creative design capabilities.
- Supports function calling for external tool integration.
Cons
- Slightly higher pricing on SiliconFlow at $0.086/M tokens.
- May not match specialized reasoning models in pure math tasks.
Why We Love It
- It brings enterprise-grade function calling and tool integration to on-device deployment, enabling chatbots that can interact with external systems while maintaining efficiency.
Small LLM Model Comparison
In this table, we compare 2025's leading small LLMs optimized for on-device chatbot deployment. Meta-Llama-3.1-8B-Instruct excels in multilingual dialogue with industry-leading training. Qwen3-8B offers innovative dual-mode capabilities with the longest context window. THUDM/GLM-4-9B-0414 provides unique function calling for tool integration. This side-by-side comparison helps you choose the right model for your specific on-device chatbot requirements, balancing performance, efficiency, and specialized capabilities.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | Meta-Llama-3.1-8B-Instruct | meta-llama | Chat | $0.06/M Tokens | Multilingual dialogue excellence |
| 2 | Qwen3-8B | Qwen3 | Chat | $0.06/M Tokens | Dual-mode reasoning & 131K context |
| 3 | THUDM/GLM-4-9B-0414 | THUDM | Chat | $0.086/M Tokens | Function calling & tool integration |
Frequently Asked Questions
Our top three picks for 2025 are Meta-Llama-3.1-8B-Instruct, Qwen3-8B, and THUDM/GLM-4-9B-0414. Each of these models stood out for their exceptional balance of conversational capability, resource efficiency, and suitability for on-device deployment in chatbot applications.
Our in-depth analysis shows several leaders for different needs. Meta-Llama-3.1-8B-Instruct is the top choice for multilingual conversational applications with its 15 trillion token training and RLHF optimization. For applications requiring advanced reasoning alongside efficient dialogue, Qwen3-8B's dual-mode capability and 131K context make it ideal. For chatbots that need to integrate with external tools and services, THUDM/GLM-4-9B-0414's function calling support is the best option.