What SLMs Are & How They Repurpose NuNet’s Fund8 Proposal
As we step into a more resource-conscious future, Small Language Models (SLMs) emerge not only as a solution but as a strategic asset in NuNet’s Fund8 Phase 1 ‘One User Per GPU’, optimizing both performance and GPU utilization. Understanding the basic concepts behind language models, particularly the distinctions between SLMs and LLMs, is crucial, as it highlights the unique advantages smaller models bring to efficient computing, especially in environments constrained by hardware capabilities.
Understanding Language Models
A language model is an artificial intelligence model that aims to predict and generate human-like language. A large language model or an LLM, is an artificial intelligence model trained on large datasets with this same goal. Conversely, Small Language Models or SLMs are designed with the same aim but are trained on smaller datasets.
What are the Benefits?
- Outperform many LLM benchmarks
- Quantization advantage due to lower size
- Focused Accuracy
- Minimized Data/Costs/Resources
- Computationally Efficient
- Enhanced Privacy
- Energy Efficient
Techniques to Make SLMs as Competent as LLMs
Among others, techniques such as Retrieval Augmented Generation and Multimodal capabilities expand SLMs’ functionality, enabling them to perform complex tasks traditionally handled by larger models but with greater efficiency and lower resource dependency. Over the past two years, advancements in quantization and knowledge distillation have significantly reduced the resource intensity of SLMs, addressing previous limitations in real-time applications and making them ideal for NuNet’s decentralized framework.
- Retrieval Augmented Generation
- Multimodal capabilities
- Browsing
- Image/Real-time Vision
- Image/Video Generation
- LangChain
- Knowledge Distillation
- Quantization
NuNet Fund8 Phase 1: One User Per GPU
Two years ago, in early 2022, we proposed that this phase would involve enabling NuNet containers to support GPU access, monitor resource usage, and make GPUs directly available to the processes running inside the containers. As the ChatGPT era ascended and the movement in open-source alternatives at Hugging Face grew, our use-case became increasingly relevant and desirable. Today, the AI community has made remarkable progress in addressing many challenges we’ve faced since our Fund8 proposal. The main challenges now perfectly align with what SLMs have to offer.
Challenges Overcome
- Inability to Handle Complex and Diverse Tasks
- High Resource Usage in Model Deployment and Operation
- Overfitting to Training Data
- High Costs of Training and Maintenance
- Scalability and Speed Limitations in Real-time Applications
- Risks to Data Privacy and Security
- Environmental and Operational Energy Costs
SLMs now address all these challenges which weren’t quite possible two years ago, making them perfect for the ‘One User Per GPU’ concept which Fund8 proposed and fulfilled in Phase 1.
Exploring the One User Per GPU Concept with SLMs
The architecture of NuNet’s GPU sharing model is uniquely positioned to capitalize on the efficiency and scalability of SLMs, demonstrating a seamless integration of advanced AI technologies with practical deployment scenarios. This concept offers a plethora of customization features due to the small size of SLMs and their cost-effective and lower system requirements. It also addresses latency issues because users can now access models as efficient as ChatGPT 3.5 through a single entry-level GPU with 8 GB VRAM. This strategy, combined with effective quantization techniques, eliminates the need for another GPU to load and run the model.
Privacy is also a critical aspect, and personalizing user experience through a One User Per GPU approach minimizes data sharing, making it very peer-to-peer and ad-hoc. Imagine a scenario where a user in a remote location accesses a customized SLM through a single entry-level GPU, achieving results comparable to those from more sophisticated setups. This is the promise of NuNet’s ‘One User Per GPU’ approach, made possible through the strategic use of SLMs.
Wrapping Up
The integration of SLMs within NuNet’s Fund8 initiative not only enhances current computational models but also sets a precedent for future AI deployments, where efficiency and customization are paramount. It gives us great pleasure to announce that the same NuNet Fund8 concept can now be repurposed by embracing SLMs that can offer a lightweight, customizable, and private user experience in a decentralized manner.
About NuNet
NuNet is building an innovative, open-source, decentralized computing platform pioneering the new era of DePIN (Decentralized Physical Infrastructure). Find out more via:
