All You Need To Know About Running LLMs Locally

26 Feb 202410:29

TLDRThe video discusses the practicality of running AI language models (LMs) locally, contrasting the subscription-based AI services with the potential of free, locally-run alternatives. It introduces various user interfaces like UABA, Silly Tarvin, LM Studio, and Axel AO, each with its unique features and use cases. The video also covers the importance of choosing the right model based on its parameters and the ability to run on a GPU. It explains different model formats like ggf, awq, safe tensors, EXL 2, and their impact on model performance and memory usage. The concept of context length in AI models is discussed, emphasizing its role in providing necessary information for the model to process prompts accurately. The video also touches on CPU offloading as a method to run large models on systems with limited VRAM. It highlights the use of fine-tuning for specific tasks and the importance of quality training data. Finally, it mentions the NVIDIA RTX 480 super giveaway for attendees of a virtual GTC session, encouraging viewers to participate.


  • 🚀 The 2024 job market has more hiring opportunities despite the subscription-based AI services becoming more prevalent.
  • 🤖 Subscription to AI services like 'green Jor' is a monthly expense some find unnecessary when alternatives to run AI models locally exist.
  • 🌐 Choosing the right user interface is crucial for different levels of expertise, with options like uaba, silly Tarvin, LM Studio, and Axel AO available.
  • 🛠️ uaba is a versatile text generation web UI that supports various modes and is compatible with multiple operating systems and hardware.
  • 📚 LM Studio offers native functions like Hugging Face model browser, making it easier to find and use AI models as an API for other applications.
  • 🎯 Axel AO is the command-line interface of choice for fine-tuning AI models, providing robust support for this specific task.
  • 📈 Hugging Face provides a vast array of free and open-source models, with model names indicating their size and capabilities.
  • 🔧 Model formats like safe tensors, ggf, EXL 2, and awq are designed to optimize model size and performance for different use cases and hardware.
  • 🧠 Understanding context length is essential for AI models as it affects the amount of information the model can use to process prompts effectively.
  • 💡 CPU offloading allows running large models on systems with limited VRAM by offloading parts of the model to the CPU and system RAM.
  • 🔍 Fine-tuning AI models can be done efficiently with Kora, which focuses on training a fraction of the model's parameters, saving time and resources.

Q & A

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

    -The initial expectation for the job market in 2024 was that it was going to be very challenging.

  • Why might someone consider running AI models locally instead of using a subscription service?

    -Running AI models locally can be more cost-effective and provides greater control over the usage time and the specific models used, without the constraints of subscription services.

  • 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 (basic input/output), chat (dialogue format), and notebook (text completion).

  • What is the main focus of the Silly Tarvin interface?

    -Silly Tarvin focuses on the front-end experience of using AI chat bots, offering features like chatting, role-playing, and visual novel-like presentation.

  • How does LM Studio differ from other interfaces mentioned?

    -LM Studio is an interface with native functions like the Hugging Face model browser for easier model discovery and quality of life features that inform users about model compatibility and usability.

  • What is the advantage of using Axel AO for fine-tuning AI models?

    -Axel AO offers the best support for fine-tuning AI models, as it allows users to fine-tune without training the entire model, which is more efficient and less resource-intensive.

  • What does 'Cuda out of memory' error indicate?

    -A 'Cuda out of memory' error indicates that the GPU memory is insufficient to run the model, which can happen when the model's parameter count is too large for the available GPU memory.

  • How can CPU offloading help in running large models with limited GPU memory?

    -CPU offloading allows models to be offloaded onto the CPU and system RAM, enabling the execution of models that would otherwise be too large for the available GPU memory, albeit with a trade-off in speed.

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

    -Context length is crucial as it refers to the amount of information, including instructions, input prompts, and conversation history, that a model can use to process a prompt. A longer context length allows the AI to utilize more information for tasks like summarizing papers or tracking previous conversations.

  • What are some hardware acceleration frameworks that can improve the speed of running AI models?

    -Some hardware acceleration frameworks include VM Inference Engine, which is great for handling parallel requests and can increase model speed significantly, and Nvidia's TensorRT, which can improve inference speed for certain models.

  • How does fine-tuning a model differ from training a model from scratch?

    -Fine-tuning involves adjusting a pre-trained model to better suit a specific task, using only a fraction of the model's parameters. This is more efficient and less costly than training a model from scratch, which requires training all parameters.

  • What is the importance of data formatting when fine-tuning a model?

    -Data formatting is crucial as it must follow the original format of the dataset used to train the model. This ensures that the fine-tuning process produces a model that can effectively handle the intended tasks and data structures.



🤖 AI Services and Local Deployment Overview

The first paragraph discusses the unexpected job market conditions in 2024 and the rise of AI services, particularly subscription-based ones. It introduces the concept of a 'green Jor' bot for coding and email writing, and questions the value of such services when free alternatives exist. The importance of choosing the right user interface for AI models is emphasized, with options like text generation web UI (uaba), Silly Tarvin for a better front-end experience, LM Studio for straightforward execution, and Axel AO for fine-tuning. The paragraph also covers various model formats and optimization methods, including safe tensors, ggf, awq, EXL 2, and gbq, and their impact on model performance and memory usage.


🧠 Context Length and Model Optimization Techniques

The second paragraph delves into the significance of context length for AI models, explaining how it affects the model's ability to process prompts and maintain conversation history. It discusses the technical aspects of context length in terms of tokens and VRAM usage, and how certain models can optimize this with techniques like gqa. The paragraph also explores CPU offloading as a method to run large models on systems with limited VRAM. It mentions various hardware acceleration frameworks and tools, including VM inference engine, Nvidia's tensor rtlm, and the chat with RTX app, which offers local document scanning and video content analysis. The importance of fine-tuning AI models with tools like Kora is highlighted, along with the necessity of quality training data and adherence to the original dataset format.


🎁 NVIDIA RTX 480 Super Giveaway and Community Acknowledgment

The third paragraph announces a giveaway for an NVIDIA RTX 480 Super, with the requirement to attend a virtual GTC session and provide proof of attendance. It provides instructions on how to participate, including taking a selfie or showing a unique gesture during the session. The paragraph also acknowledges various individuals who have supported the speaker through Patreon or YouTube and encourages following on Twitter for future updates.




LLMs, or Large Language Models, are advanced AI systems designed to understand and generate human-like text. They are a core focus of the video, which discusses running these models locally for various applications without relying on subscription-based AI services.

💡AI Services

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


Running AI models 'locally' means operating them on one's own computer or server rather than through a cloud-based service. This approach is highlighted in the video as a way to gain more control and potentially save on subscription costs.


UI stands for User Interface. The video mentions different UI options for interacting with AI models, such as uaba, silly Tarvin, and LM Studio, which cater to various user preferences and needs.

💡Hugging Face

Hugging Face is mentioned as a platform where users can find and download free and open-source models for their AI applications. It is a key resource for those looking to run LLMs locally.

💡Model Parameters

Model parameters are the internal variables of an AI model that are learned from data. The number of parameters, often expressed in billions, can indicate the complexity and potential performance of a model. The video discusses how the parameter count can affect the ability to run a model on a GPU.


Quantization is a technique used to reduce the precision of a model's parameters, allowing for smaller model sizes and potentially enabling them to run on hardware with less memory. The video explains how different quantization methods can impact model performance.

💡Context Length

Context length refers to the amount of information an AI model can take into account when generating a response. It includes instructions, input prompts, and conversation history. The video emphasizes the importance of context length for the model's effectiveness.

💡CPU Offloading

CPU Offloading is a technique that allows models to be processed by the CPU and system RAM when the GPU memory (VRAM) is insufficient. The video discusses how this feature can enable users with limited GPU resources to run large models.


Fine-tuning involves training an AI model on a specific task or dataset to improve its performance for that particular application. The video explains that fine-tuning can make a model more specialized without the need to retrain the entire model.


Gradio is a UI library for quickly creating web interfaces for machine learning models. The video mentions Gradio in the context of user interfaces for AI models, suggesting it as an option for those who prefer a different interface than uaba.

💡Hardware Acceleration

Hardware acceleration refers to the use of specialized hardware, like GPUs, to speed up the processing of AI models. The video discusses various frameworks and technologies, such as NVIDIA's TensorRT, that can be used to accelerate model inference.


The job market in 2024 is experiencing an increase in hiring opportunities despite previous concerns.

AI Services are becoming more prevalent, offering services like coding assistance and email writing for a monthly fee.

Running AI chatbots and language models (LMs) locally can be a cost-effective alternative to subscription-based AI services.

UABA is a popular user interface for text generation, offering modes like default, chat, and notebook.

Silly Tarvin focuses on the front-end experience for AI chatbots, providing a visually appealing interface.

LM Studio offers native functions like the Hugging Face model browser for easier model discovery.

Axel AO is a command-line interface that provides the best support for fine-tuning AI models.

Hugging Face allows users to browse and download free and open-source models.

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

Model formats like GGF, AWQ, and GPTQ allow for reduced memory usage, making it possible to run larger models.

Context length is crucial for AI models to understand and process prompts effectively.

CPU offloading allows models to run on systems with limited VRAM by utilizing system RAM.

Hardware acceleration frameworks like VM Inference Engine can significantly increase model speed.

NVIDIA's TensorRT can enhance inference speed on RTX GPUs.

Chat with RTX is a local UI that can connect a model to local documents for privacy and convenience.

Fine-tuning AI models with tools like Kora can be more efficient than training from scratch.

The quality of fine-tuning depends on the organization of the training data and the fit of the chosen model.

Different fine-tuning techniques serve various purposes, from generating morally aligned responses to preferred human-like answers.

Running local LMs can save money and offer performance without relying on subscription services.

NVIDIA is giving away an RTX 480 Super to one lucky participant who attends a virtual GTC session.