All You Need To Know About Running LLMs Locally

26 Feb 202410:29

TLDRThe video provides an in-depth guide on running AI chatbots and LLM (Large Language Models) locally, which can be a cost-effective alternative to subscribing to AI services. It discusses various user interfaces like uaba, Silly Tarvin, LM Studio, and Axel AO, each with its unique features and use cases. The transcript also covers the importance of choosing the right model based on its parameters and the available hardware, including the use of quantization methods to reduce model size for efficient GPU usage. Additionally, it touches on the concept of context length in AI models and how it affects their performance. The video further explores CPU offloading, hardware acceleration frameworks, and fine-tuning techniques like Kora for specific tasks. It concludes with a mention of extensions for integrating LLMs with databases and the potential for local model usage to save costs in the current job market.


  • πŸš€ The job market in 2024 has seen an increase in hiring opportunities despite previous concerns.
  • πŸ’° Subscription-based AI services like chatbots are popular, but running AI models locally can save money and offer more flexibility.
  • πŸ’» Choosing the right user interface for AI chatbots is crucial and options include uaba, silly Tarvin, LM Studio, and Axel AO.
  • 🌐 Uaba is a versatile text generation web UI that is widely used and supports most operating systems.
  • πŸ“š Hugging Face provides a model browser and allows users to download free and open-source models.
  • πŸ” Models come in various sizes, indicated by the number of parameters, which can help users determine if they can run the model on their GPU.
  • 🧠 Models with 'MoE' in their name are mixture of experts models, which can be optimized for less VRAM usage.
  • πŸ”’ Different model formats like ggf, awq, safe tensors, EXL 2, and gptq offer ways to reduce model size and memory usage.
  • πŸ’Ύ Condensation methods help make models smaller, which can be beneficial for running them on systems with limited resources.
  • πŸ’Ό CPU offloading allows models to run on CPU and system RAM, which can be useful for systems with less VRAM.
  • πŸ† Fine-tuning AI models with tools like Kora can make them more specialized without needing to retrain the entire model.
  • πŸ“ˆ Hardware acceleration frameworks and tools like NVIDIA's TensorRT can significantly increase model inference speed.

Q & A

  • What was the general expectation for the job market in 2024?

    -The general expectation for the job market in 2024 was that it was going to be challenging, described as a 'job market hell.'

  • Why might someone consider running AI chatbots and LLM models locally?

    -Running AI chatbots and LLM models locally can be beneficial because it allows for more control, potentially saving money by not subscribing to services, and can offer privacy benefits by not requiring data uploads.

  • What are the three modes offered by the uaba text generation web UI?

    -The three modes offered by the uaba text generation web UI are default (for basic input/output), chat (for dialogue format), and notebook (for text completion).

  • What is Silly Tarvin and how does it differ from uaba?

    -Silly Tarvin is a front-end interface for using AI chatbots that focuses more on the visual presentation, offering a more engaging user experience. It differs from uaba, which is a more basic and popular UI that offers most of the basic functionalities needed.

  • What is LM Studio and what are its key features?

    -LM Studio is an interface with native functions that make finding models easier, such as the Hugging Face model browser. It also provides quality of life information, like whether a model can run or not, and can be used as an API for other apps.

  • What does Axel AO offer for those interested in fine-tuning AI models?

    -Axel AO is a command-line interface that offers the best support for fine-tuning AI models, making it the preferred choice for those who want to deeply engage in model fine-tuning.

  • How can one find and download models from Hugging Face?

    -One can find and download models from Hugging Face by using Uaba's built-in downloader, which involves copying and pasting the last two URL slugs of the desired model.

  • What does the 'b' in a model's name signify?

    -The 'b' in a model's name signifies the number of billion parameters the model has, which can be an indicator of whether the model can run on a GPU or not.

  • What is the significance of context length in AI models?

    -Context length is crucial as it includes instructions, input prompts, and conversation history. The longer the context length, the more information the AI can use to process prompts, which is essential for tasks like summarizing papers or tracking previous conversations.

  • What is CPU offloading and how does it help in running large models?

    -CPU offloading is a feature that allows models to be offloaded onto the CPU and system RAM. This helps in running large models that might not fit entirely into VRAM by using the CPU and RAM to handle the rest of the model data.

  • How can one participate in the giveaway for an RTX 480 super?

    -To participate in the giveaway for an RTX 480 super, one needs to attend at least one virtual GTC session and show proof of attendance, which can be a selfie or a unique gesture indicating participation, using the provided link in the video description.

  • What is the importance of data formatting when fine-tuning AI models?

    -Data formatting is crucial when fine-tuning AI models because it needs to follow the original dataset format used to train the model. Proper formatting ensures that the fine-tuned model will produce results that are aligned with the desired outcomes.



πŸ€– AI Services and Local Model Deployment

The first paragraph discusses the unexpected job market situation in 2024 and the challenges of AI service subscriptions. It introduces the concept of running AI chat bots and large language models (LMs) locally as an alternative to paid services. The importance of choosing the right user interface (UI) is emphasized, with options like uaba (text generation web UI), Silly Tarvin (frontend experience), and LM Studio (native functions and API support) highlighted. The paragraph also touches on different model formats and their impact on memory usage, as well as strategies for running models with large parameter counts.


πŸ’‘ Context Length and Model Optimization

The second paragraph delves into the importance of context length for AI models to function effectively, explaining how it affects the model's ability to process information and solve queries. It outlines the technical aspects of context length in terms of tokens and VRAM usage, and how models like MixR and DeepSeek implement techniques to reduce memory consumption. The paragraph also discusses CPU offloading as a method to run large models on systems with limited VRAM. Additionally, it covers hardware acceleration frameworks and tools like VM Inference Engine and Nvidia's TensorRT, as well as the use of fine-tuning with tools like Kora for specific applications. The importance of quality training data in the fine-tuning process is also stressed.


🎁 Giveaways and Community Support

The third paragraph shifts focus to an upcoming event, the virtual GTC session, which is recommended for attendance due to its valuable content. It also mentions a giveaway for an Nvidia RTX 480 super, where participants are required to attend a GTC session and provide proof. The paragraph concludes with a shoutout to various supporters and a teaser for the next video content.




LLMs, or Large Language Models, are advanced AI models designed to process and generate human-like text. They are a central theme of the video as it discusses how to run these models locally, which can be cost-effective and offer greater control over their usage.

πŸ’‘AI Services

AI Services refer to the subscription-based platforms that provide access to AI functionalities, like coding assistance or email drafting. The video discusses the potential cost-saving benefits of running AI models locally instead of relying on these services.


uaba, mentioned in the transcript, seems to be a typo or a specific term related to the user interface for interacting with AI models. It's relevant as the video suggests using it for its well-rounded functionalities across different operating systems.


Fine-tuning is the process of further training a pre-existing AI model on a specific task to improve its performance. The video emphasizes its importance for customizing AI models to perform specific functions, like teaching coding or providing tech support.

πŸ’‘Hugging Face

Hugging Face is a company that provides a platform for developers to share, discover, and use AI models. The video recommends using Hugging Face's model browser for finding and downloading models to run locally.


A GPU, or Graphics Processing Unit, is a type of hardware often used to accelerate the processing of AI models due to its parallel computing capabilities. The video discusses considerations for running models on GPUs, including parameter count and memory usage.

πŸ’‘Context Length

Context Length refers to the amount of information an AI model can take into account when generating a response. It's crucial for the model's ability to understand and respond accurately to prompts. The video mentions how context length affects memory usage and model performance.


Quantization in AI models is a method to reduce the size of the model by decreasing the precision of the numbers it uses. This can enable larger models to run on hardware with limited resources, as discussed in the video in relation to formats like ggf and EXL 2.

πŸ’‘CPU Offloading

CPU Offloading is a technique that allows certain computations to be performed on the CPU instead of the GPU, freeing up VRAM for other tasks. The video explains how this can enable users with limited VRAM to still run large models.


TensorRT is an AI inference engine developed by NVIDIA that optimizes and accelerates deep learning models for deployment. The video mentions its use for increasing the speed of AI model inference.

πŸ’‘Fine-Tuning Data Set

A Fine-Tuning Data Set is a collection of data used to further train an AI model for a specific task. The video stresses the importance of the data set's format and quality, stating that 'garbage in, garbage out' applies to fine-tuning.


In 2024, despite concerns about the job market, there's an increase in hiring opportunities and a growing reliance on AI services.

AI services like 'green Jor' offer coding assistance and email writing capabilities for a monthly fee.

Free AI chatbots like 'chat gbt' are available, raising questions about the value of paid services.

The importance of choosing the right user interface for AI chatbots is emphasized, with options like uaba, silly Tarvin, LM Studio, and Axel AO.

Uaba is a popular text generation web UI with modes for basic input/output, chat, and notebook.

Silly Tarvin focuses on the front-end experience and requires a backend like uaba to run AI models.

LM Studio offers native functions like a model browser and quality of life info for model compatibility.

Axel AO is a command line interface that supports fine-tuning AI models.

Hugging face provides a space to browse and download free and open-source models.

Models have different parameter counts, which can indicate their suitability for running on a GPU.

Different model formats like ggf, awq, safe tensors, EXL 2, and gptq offer various optimization techniques.

Context length is crucial for AI models to solve questions effectively, with 8,000 tokens context length requiring around 4.5GB VRAM.

CPU offloading allows models to run on systems with limited VRAM by offloading parts of the model to system RAM.

Hardware acceleration frameworks like VM inference engine and Nvidia's tensor rtlm can significantly increase model speed.

Nvidia's app 'Chat with RTX' allows local model connections to documents and other data for increased privacy.

Fine-tuning AI models with tools like Kora can focus on specific tasks without retraining the entire model.

When fine-tuning, it's important to follow the original data set format and ensure the quality of the training data.

Extensions like rag can integrate LM with databases for advanced functionalities like querying local files.

Nvidia is hosting a giveaway for an RTX 480 super, with participation in a virtual GTC session as a requirement.