What are Open Source LLMs for Smart IoT?
Open source large language models for Smart IoT are specialized AI systems designed to run efficiently on edge devices, embedded systems, and resource-constrained hardware. These models enable intelligent automation, natural language interfaces, predictive maintenance, and real-time decision-making directly on IoT devices. Optimized for low latency, minimal memory footprint, and energy efficiency, they empower developers to deploy sophisticated AI capabilities across smart homes, industrial sensors, wearables, and connected devices without relying on constant cloud connectivity. They foster innovation in edge computing, democratize access to powerful AI for IoT applications, and enable a wide range of use cases from voice-controlled appliances to autonomous manufacturing systems.
openai/gpt-oss-20b
gpt-oss-20b is OpenAI's lightweight open-weight model with ~21B parameters (3.6B active), built on an MoE architecture and MXFP4 quantization to run locally on 16 GB VRAM devices. It matches o3-mini in reasoning, math, and health tasks, supporting CoT, tool use, and deployment via frameworks like Transformers, vLLM, and Ollama—making it ideal for edge IoT deployments.
openai/gpt-oss-20b: Efficient Edge Intelligence for IoT
gpt-oss-20b is OpenAI's lightweight open-weight model with ~21B parameters (3.6B active), built on a Mixture-of-Experts (MoE) architecture and MXFP4 quantization to run locally on 16 GB VRAM devices. It matches o3-mini in reasoning, math, and health tasks, supporting Chain-of-Thought (CoT), tool use, and deployment via frameworks like Transformers, vLLM, and Ollama. With a 131K context length, this model is perfectly suited for Smart IoT applications requiring on-device intelligence, real-time processing, and minimal computational overhead. Its efficient architecture enables deployment on edge devices while maintaining exceptional reasoning capabilities for complex IoT scenarios.
Pros
- Runs on just 16 GB VRAM, perfect for edge devices.
- MoE architecture with only 3.6B active parameters for efficiency.
- Supports CoT reasoning and tool use for IoT automation.
Cons
- Smaller parameter count may limit some complex tasks.
- Requires quantization awareness for optimal deployment.
Why We Love It
- It delivers powerful AI capabilities on resource-constrained IoT hardware, enabling true edge intelligence with minimal infrastructure requirements at an affordable SiliconFlow price of $0.04/M input tokens and $0.18/M output tokens.
meta-llama/Meta-Llama-3.1-8B-Instruct
Meta Llama 3.1 8B is a multilingual instruction-tuned model optimized for dialogue use cases, trained on over 15 trillion tokens. With 8B parameters and 33K context length, it delivers exceptional performance on industry benchmarks while maintaining efficiency ideal for IoT gateways, edge servers, and smart device controllers.
meta-llama/Meta-Llama-3.1-8B-Instruct: Balanced Performance for Smart Devices
Meta Llama 3.1 is a family of multilingual large language models developed by Meta, featuring pretrained and instruction-tuned variants. 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. With support for text and code generation, 33K context length, and a knowledge cutoff of December 2023, this model strikes an optimal balance between capability and efficiency for Smart IoT applications—from voice assistants to intelligent home automation systems.
Pros
- 8B parameters optimized for efficiency and performance.
- Multilingual support for global IoT deployments.
- Trained with RLHF for safe, helpful responses.
Cons
- Knowledge cutoff at December 2023.
- May require fine-tuning for specialized IoT domains.
Why We Love It
- It provides production-ready dialogue capabilities with multilingual support at IoT-friendly scale, backed by Meta's robust training methodology and available at competitive SiliconFlow pricing of $0.06/M tokens.
THUDM/GLM-4-9B-0414
GLM-4-9B-0414 is a lightweight 9 billion parameter model that demonstrates excellent capabilities in code generation, function calling, and tool invocation. Despite its smaller scale, it shows competitive performance in benchmark tests while maintaining efficiency ideal for resource-constrained IoT scenarios, edge computing, and embedded smart systems.
THUDM/GLM-4-9B-0414: Agentic IoT Intelligence
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. With 33K context length, this model shows a good balance between efficiency and effectiveness in resource-constrained scenarios, providing a powerful option for users who need to deploy AI models under limited computational resources. It's particularly well-suited for Smart IoT applications requiring tool integration, API calls, and autonomous device management.
Pros
- Function calling for IoT device control and automation.
- 9B parameters for efficient edge deployment.
- Code generation for on-device scripting and logic.
Cons
- Smaller than flagship models in the series.
- May need optimization for specific IoT protocols.
Why We Love It
- It brings agentic capabilities to IoT environments, enabling devices to autonomously interact with tools and services while maintaining exceptional efficiency at an affordable SiliconFlow price of $0.086/M tokens.
AI Model Comparison for Smart IoT
In this table, we compare 2025's leading open source LLMs optimized for Smart IoT applications. The openai/gpt-oss-20b excels with its ultra-lightweight MoE architecture for edge devices, meta-llama/Meta-Llama-3.1-8B-Instruct provides balanced multilingual dialogue capabilities, and THUDM/GLM-4-9B-0414 offers function calling for agentic IoT automation. This side-by-side comparison helps you select the optimal model based on your device constraints, processing requirements, and IoT use case.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | openai/gpt-oss-20b | openai | Lightweight MoE | $0.04/$0.18 per M tokens | Runs on 16GB VRAM edge devices |
| 2 | meta-llama/Meta-Llama-3.1-8B-Instruct | meta-llama | Efficient Dialogue | $0.06 per M tokens | Multilingual RLHF-trained |
| 3 | THUDM/GLM-4-9B-0414 | THUDM | Function Calling | $0.086 per M tokens | Agentic tool invocation |
Frequently Asked Questions
Our top three picks for 2025 Smart IoT applications are openai/gpt-oss-20b, meta-llama/Meta-Llama-3.1-8B-Instruct, and THUDM/GLM-4-9B-0414. Each of these models stood out for their efficiency, compact parameter counts, and specialized capabilities suited for resource-constrained edge devices and intelligent automation systems.
Our analysis shows different leaders for specific IoT needs. For ultra-lightweight edge devices with minimal VRAM (16GB), openai/gpt-oss-20b is the top choice with its efficient MoE architecture. For IoT systems requiring multilingual voice interfaces and dialogue, meta-llama/Meta-Llama-3.1-8B-Instruct excels with RLHF training. For agentic IoT applications requiring function calling and tool integration, THUDM/GLM-4-9B-0414 provides the best balance of capability and efficiency.