Open Source software has a number of benefits over commercial products, not least the fact that it can be downloaded for free. This means anyone can analyse the code and, assuming they have the right hardware and software environment configured, they can start using the open source codelyly.
With artificial intelligence (AI), there are two parts to being open. The source code for the AI Engine Itself Can Be Downloaded from a repository, Inspected and run on Supitable Hardware Just Like Like Other Open Source Code. But Open also applies to the data modelWhich means it is entryly feasible for someone to run a local ai model that has alredy been trained.
In other words, with the right hardware, a developer is free to download an ai model, disconnect the target hardware from the internet and run it locks of Query Data Being Leaked Ice.
And since it is open source, the AI model can be installed locally So it does not increase the costs associateed with cloud-hosted ai models, which are generally charged based on the volume of queries measured in tokens submitted to the ai engine.
How Does An Open Model Difer from Commercial Ai?
All software needs to be licensed. Commercial Products Are Increasing On A Subscription Basis and In the Case of Large Language Models (LLMS), the cost correlates to the amnt of usage, bled on the Volume of the Volum Sumeed in terms of hours of Graphics Processing Unit (GPU) Time used by the model when it is Queried.
Like all open source software, an llm that is open source is Subject to the terms and conditions of the licensing scheme used. Some of these licenses put restrictions on how the software is used but, generally, there are no license fees as associateed with running an open model locky.
However, there is a charge if the open model is run on public cloud infrastructure or accessed as a cloud service, which is usually calculated based on the volume of tokens submitted to the llm programmatically Using Application Programming Interfaces (APIS).
What are the benefits of open source ai models
Beyond The Fact that they can be downloaded and deployed on-Premise without addition s.
Just like other open source projects, an ai model that is open source can be checked by anyone. This should help to improve its quality and Remove Bugs and Go Some Way to Tackling Bias, when the source data on which a model is trained is not diverse enough. The following podcast exploes ai models further.
How to get started with open models
Most ai models offer free or low-cost access via the web to enable people to work directly with the AI System. Programmatic Access Via APIS is often charged based on the volume of tokens submitted to the model as input data, such as the number of words in a natural language in a natural language Query. There can also be a charge for output tokens, which is a measure of the data produced by the model when it responses to a Query.
Since it is open source, an open model can be downloaded from its open source repository (“Repo”) on Github. The repository generally contains different builds for target systems – Such as Distributions of Linux, Windows and Macos.
However, this approach is how developers tend to use open source code, it can be a very involved process and a data scientist may just want to “try” rduous process of getting The model up and running.
Step in Hugging face, an ai platform where people who want to experience with ai models can research whats is available and test them on datats all from one place. There is a free version, but hugging face also provides an enterprise subscription and various pricing for ai model developers for hosting and running their models.
https://www.youtube.com/watch?v=jbfuwwl0tyy
Another option is olma, an open source, command-line tool that provides a relatively easy way to download and run llms. For a full graphical user interface to interact with llms, it is negassary to run an ai platform such as open webui, an open source project available on GITHUB.
How open source ai models support corporate it security
Cyber Security Leaders have raised concerns over the ease with which employees can access popular llms, which presents a data leakage risk. Among the widely reported leaks is samsung electronics' use of chatgpt to help developers debug code. The code – in effect, Samsung Electronics Intellectual Property – was uploaded into the chatgpt public llm and effectively being subsuated into the model.
The tech giant Quickly Took Steps to Ban the Use of Chatgpt, but the growth in so-called copilots and the rain of agentic ai have the potential to leak data. Software Providers Deplying Agentic Technology will ofteen claim they keep a customer's private data entrely Separate, which means meaning to used to train the ai model. But unless it is indeed trained with the latest thinking, shortcuts, best practices and mistakes, the model will quickly become stale and out of date.
An ai model that is open can be Run in a Secure SandboxEite on-love or hosted in a Secure public cloud. But this model represents a snapshot of the ai model the developer released, and similar to ai in enterprise software, it will Quickly go out of date and become irrelevant.
However, Whatever Information is Fed Into it remains with the confins of the model, which allows organisations willing to invest the resources needed to return the model using this information. In effect, new enterprise content and structured data can be used to teach the ai model the specific of how the business operates.
What hardware do you need
There are YouTube videos demonstrating that an llm such as the Chinese Deepsek-R1 Model can run on an nvidia jetson nano embedded edge device or even a raspberry pi, using a suitable adapter and a relatively modern gpu card. Assuming the GPU is supported, it also also needs planty of video memory (VRAM). This is a government for best performance, the llm needs to run in memory on the gpu.
Inference Requires Less Memory and Less GPU CORES, but the More Processing Power and Vram Aawailable, the faster the model is alive to respond, as a measure of tokens it can process percent per secondd. For training llms, the number of gpu cos and Vram requirements go up significant, which equates to extremely costly on-premise ai servers. Even if the gpus is run in the public cloud with metered usage, there is no getting away from the high costs needed to run infeRENCE workloads continuously.
Nevertheless, The Sheer Capacity of Compute Power Awailable from the Hyperscales means that it may be cost effective to upload training data to an open llm model hosted in a public cloud.
How to make open source ai models more affordable to run
As it name sugges, a large language model is large. LLMS require huge datasets for training and immense farms of powerful servers for training. Even if an ai model is open source, the sheer cost of the hardware means that only those organizations that are prepared to make upfronts upronts in hardware or reserve Austionalise llms full.
But not everyone needs an llm and that is where there is so much interest in models that can run on much cheaper hardware. These so-called Small Language Models (SLM) Are Less Compute Intensive, And Some will take on edge devices, smartphones and personal computers (See box,