Local GenAI Solutions
Powering AI Capabilities Closer to You
Local GenAI as a solution for everyone more confidential, cheap and configurable
What are Local GenAI Solutions?
Local GenAI solutions are artificial intelligence models and systems that have been deployed and are run on local hardware, as opposed to being hosted on a remote server. Unlike cloud-based AI assistants (or cloud-based generative artificial intelligence models), such as ChatGPT, which require an internet connection to function, local GenAI solutions can operate independently on someone’s own device or local environment. [Add an image showing the difference between cloud-based and local AI solutions].
The significant technical aspect of local GenAI is that the AI model and processing are executed and entirely contained in a controllable local environment. This means that an AI model can function without an internet connection and without information from a local network being sent over the internet for processing. This can provide the user with a significant benefit in terms of control, privacy, and security. This is contrasted with using cloud-based AI assistants (or generative models) like ChatGPT, which requires the user to send a prompt or information to the cloud servers, which raises significant user concerns around data privacy and security.
How Do Local GenAI Solutions Work?
The primary base for local GenAI solutions lies in the establishment and development of large language models (LLMs) that have been made open source, such as Meta AI’s new Llama model designed for operating like an LLM. These LLMs can be downloaded, deployed, and run on a local device or hardware used to enable AI functionality without any need for cloud-based infrastructure.
Platforms like Ollama have sprung up to make the deployment and use of local large language models (LLMs) easy and accessible for developers and users to incorporate into their own applications and workflows. Using these open-source models and deployment tools, local GenAI offerings flaunt AI-infused capabilities that highlight the brilliance of users while leaving the framework of cloud-based architectures.
Local LLM Trends
The boom of open-source LLMs is reshaping the AI space with drastic shifts to local LLMs and decentralized AI. Cloud-based AI services have been dominated by tech giants like OpenAI and Google, but availability of open-source LLMs has ushered in a new wave of local AI experiences that can be customized locally using open-source software.
The shift of LLMs to local AI models targeting users looking for more control, privacy, and flexibility in deploying AI. As users and organizations become aware of potential risk associated with cloud-based AI, there is more demand for solutions that can run locally and be tuned to a specific context or need.
Benefits of Local LLMs
Local LLMs have major benefits over cloud-based LLMs:
– Confidentiality and Privacy: Keeping AI processing like LLMs and user data in the local setting removes the risk of exposing user data to cloud or unsanctioned processes attaching to AI processing.
– Cost Reduction: Deploying AI capabilities on local hardware can be cheaper than cloud-based services, particularly if users have defined computation needs or limited internet connectivity.
– Customization and Flexibility: Because local LLMs can utilize existing customized applications, there is more ability to control the use of and interactions with deployment specific to the business.
– No Internet Access Required: Local LLMs can exist as AI user capabilities without requiring any internet services, which means AI-supported capabilities exist outside of close or disconnected environments.
That said, cloud-based services often have better access to all updates of models, particularly if the models have more deployable learning degree of freedom than localized AI user experience.
Local LLM Hardware
Running local LLMs typically require hardware than can run the computational intensity of LLMs. The hardware used for LLMs typically come in the form of high-performance CPUs, GPUs, and sufficient RAM (Random Access Memory) to access the data needed to run.
Hardware used for LLM will depend on the LLM model size, complexity, and level of performance. More substantial LLMs or predictive models typically require more powerful hardware, such as access to servers or GPU- accelerated systems, which can inflate the overall cost to deploy local GenAI.
Lean-Link Customized Local LLM Solutions
Lean-Link provide a customized hardware solutions to power local LLMs for deployment. Our team has constructed optimal server and GPU models for our systems to be cost effective and provide performance for businesses to have local GenAI performance for their users without additional costs.
We are thoroughly aware of what the right hardware specifications are and how to optimize an AI model for a specific need. Lean-Link will work to meet your hardware requirement while ensuring sufficient resources are available to run the intended LLM.
Summary and Conclusion
Local GenAI constitutes an important step away from relying on cloud-based solutions and towards self-hosted and on-premises AI capabilities. These locally-stored solutions provide several powerful benefits that are proving to drive their adoption.
Local GenAI are inextricably tied to open-source large language models (LLMs), such as Llama, and can be downloaded to existing local hardware to run and maintain the model without constant internet connectivity. This means users and organizations can exert greater levels of ownership, security and confidentiality over their own data in addition to eliminating cloud service costs.
Flexibility and customization options are very attractive aspects of local LLMs. Localized GenAI can be integrated into custom-built applications and workflows supporting business and organizational needs. Providers, such as Lean-Link, are adapting these language models for industry-specific applications when processing sensitive data, as well as executing and maintaining mission-critical AI operations in remote, disconnected environments.
Although cloud-based AI services have provided access to some of the most updated features and other highly-valued AI functionalities, the matter of data ownership and security has become an increasing concern. The local LLM solutions can serve as an alternative that allows the user to have ownership, when leveraging AI capabilities by removing the layer of cloud-based services.
We are also aware of the heightened demand for locally-based GenAI and have rolled out and customized specialized hardware optimized for it in a cost-effective manner for our clients. We are able to do this because of our own hardware design and AI model optimization experience, as we look to provide clients with a local LLM solution that meets their requirements for performance, scalability and affordability.
The trends of localizing AI as part of the greater movement toward decentralized AI are only likely to accelerate in the face of expanding developments and interruptions within the overall AI landscape. The power of local GenAI, offers businesses and the community, the accessible design and full potential functionality of AI, while maintaining the essential aspects of control and privacy over their data ownership.
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