In recent years, AI Field has been fascinated by the success of large-scale language models (LLMS). Initially designed for natural language processing, these models have evolved into powerful inference tools that can tackle complex problems in human-like incremental thinking processes. However, despite its extraordinary inference capabilities, LLM has major drawbacks, such as high computational costs and slow deployment speeds, making it impractical for practical use in resource-constrained environments such as mobile devices and edge computing. This has led to increased interest in developing smaller, more efficient models that can provide similar inference capabilities while minimizing cost and resource demands. This article discusses the rise of these small inference models, their potential, challenges, and the future impact of AI.
Changes in perspective
For much of the recent history of AI, this field follows the principles of “scaling methods.” This suggests that model performance improves predictably as an increase in data, computational power, and model size. This approach has provided a strong model, but also has significant trade-offs such as infrastructure costs, environmental impacts and incubation period issues. Not all applications require the full functionality of a large model with hundreds of billions of parameters. In many practical cases, such as on-device assistants, healthcare, education, and so on, similar results can be achieved when the smurer model can be effectively inferred.
Understand AI reasoning
AI inference refers to the ability of a model to follow a logic chain, understand causes and effects, guess the meaning, estimate the steps in planning a process, and identify inconsistencies. For linguistic models, this often means not only obtaining information, but also manipulating and inferring information via a structured, step-by-step approach. This level of inference is usually achieved by fine-tuning the LLMS, performing multi-step inference before reaching the answer. Although effective, these methods require important computational resources, are slow to deploy and costly, and can cause concerns about accessibility and environmental impact.
Understanding small inference models
Small-scale inference models are intended to replicate the inference capabilities of large-scale models, but are more efficient in terms of computing power, memory usage, and latency. These models often employ a technique called knowledge distillation. Here, a smaller model (“student”) learns from a larger, pre-trained model (“teacher”). The distillation process involves training small models with the data generated by larger models with the aim of transferring inference capabilities. The student model is fine-tuned to improve performance. In some cases, reinforcement learning using special domain-specific reward functions can be applied, further enhancing the model’s ability to perform task-specific inference.
Rise and advances in small inference models
A notable milestone in the development of small inference models came with the release of DeepSeek-R1. Despite being trained on a relatively modest cluster of older GPUs, the DeepSeek-R1 achieved performance comparable to larger models such as Openai’s O1 on benchmarks such as the MMLU and GSM-8K. This result led to a rethinking of traditional scaling approaches, which assumes that larger models are inherently superior.
The success of DeepSeek-R1 can be attributed to an innovative training process combining large-scale reinforcement learning without relying on monitored tweaks in the early stages. This innovation led to the creation of DeepSeek-R1-Zero, a model that demonstrated impressive inference capabilities compared to larger inference models. Further improvements, such as the use of cold start data, have enhanced model consistency and task execution, particularly in areas such as mathematics and code.
Furthermore, distillation techniques have proven important in developing smaller, more efficient models from larger models. For example, DeepSeek has released a distilled version of the model, with sizes ranging from 1.5 billion to 70 billion parameters. Using these models, researchers trained the much smaller model DeepSeek-R1-Distill-Qwen-32B, which outperformed Openai’s O1-MINI on various benchmarks. These models can now be deployed on standard hardware, making them a more viable option for a wide range of applications.
Small models may match GPT-level inference
To assess whether small-scale inference models (SRMs) match the inference power of larger models (LRMSs) such as GPT, it is important to assess performance on standard benchmarks. For example, the DeepSeek-R1 model recorded approximately 0.844 in MMLU tests, comparable to larger models such as the O1. In the GSM-8K dataset focused on primary school mathematics, the DeepSeek-R1 distillation model achieved the highest level of performance, surpassing both O1 and O1-MINI.
In coding tasks such as LiveCodeBench and CodeForces tasks, the DeepSeek-R1 distillation model runs similarly to O1-MINI and GPT-4O, demonstrating strong inference capabilities for programming. However, larger models still have advantages for tasks that require greater understanding of models and handling longer context windows.
Despite its strengths, small models struggle when they struggle with extended inference tasks or when faced with distributed data. For example, in LLM chess simulations, DeepSeek-R1 makes more mistakes than large models, suggesting a limitation in its ability to maintain focus and accuracy over time.
Tradeoffs and practical meanings
When comparing SRM with GPT-level LRM, the trade-off between model size and performance is important. Smaller models require low memory and computational power, making them ideal for situations where edge devices, mobile apps, or offline inferences are required. This efficiency reduces operational costs and models like DeepSeek-R1 run up to 96% cheaper than larger models like the O1.
However, there are several compromises to these efficiency improvements. Smaller models are typically fine-tuned for specific tasks, allowing for limited versatility compared to larger models. For example, DeepSeek-R1 is excellent at mathematics and coding, but does not have multimodal features, such as the GPT-4o, which allows interpretation of images that can be processed by large models.
Despite these limitations, the practical applications of small inference models are enormous. Healthcare allows you to power diagnostic tools that analyze medical data on standard hospital servers. Education can be used to develop personalized tutoring systems and provide step-by-step feedback to students. Scientific research can help with data analysis and hypothesis testing in fields such as mathematics and physics. The open source nature of models such as DeepSeek-R1 promotes collaboration, democratizes access to AI, and enables small organizations to benefit from advanced technology.
Conclusion
The evolution of language models into smaller inference models is a major advance in AI. Although these models may not yet be perfectly matched with the wide range of features of large language models, they offer important benefits in efficiency, cost-effectiveness and accessibility. By balancing inference power and resource efficiency, small models are set up to play a key role in a variety of applications, making AI more practical and sustainable for practical use.
