Which nVidia GPU is BEST for Local Generative AI and LLMs in 2024?

Ai Flux
9 Feb 202419:06

TLDRThe video discusses the advancements in open source AI and the ease of running local LLMs and generative AI tools like Stable Diffusion. It highlights the best tools for compute, favoring Nvidia GPUs, and explores the options between renting and buying. The video also delves into the latest Nvidia RTX 40 Super Series GPUs, their features, and performance, comparing them to older models. It further discusses the potential of the Nvidia Tensor RT platform for AI inference and showcases impressive DIY setups for running multiple high-end GPUs. The video concludes with recommendations on which GPUs to consider for different AI tasks.

Takeaways

  • 🚀 Open source AI has significantly progressed, enabling local LLMs and generative AI like Stable Diffusion for images and video, and efficient podcast transcription.
  • 💡 Nvidia GPUs are currently the leading choice for compute cost and options, with Apple and AMD closing the gap.
  • 💻 For those wanting to experiment or do in-depth work, buying a GPU is more cost-effective than renting from services like RunPod or Tensor Dock.
  • 🎯 The latest Nvidia GPUs offer great value, but older models may still provide better cost-performance ratios.
  • 🤔 The release of the Nvidia RTX 5090 is uncertain, with speculation of a potential release at the end of 2024.
  • 💰 The new RTX 40 Super Series GPUs have been released, starting at $600, with claims of improved performance and AI capabilities.
  • 🌟 Nvidia's Ada Lovelace architecture delivers high Shader, RT, and AI teraflops, enhancing gaming and AI-powered PC performance.
  • 📈 Nvidia focuses on AI with technologies like DLSS and AI-powered PC Computing, improving inference and reducing latency with Tensor RT.
  • 🔍 The 4070 Super is highlighted as a cost-effective option for those focused on inference tasks and working with smaller models.
  • 🔧 Nvidia Tensor RT is an SDK for high-performance deep learning inference, offering significant performance boosts for certain applications.
  • 🛠️ Creative implementations of Nvidia GPUs, such as using A100 GPUs in custom setups, have shown high performance but may be complex and costly.

Q & A

  • What advancements have been made in open source AI in the last year?

    -Open source AI has made massive advancements, particularly in running local LLMs and generative AI like Stable Diffusion for both images and video, and transcribing entire podcasts in minutes.

  • What are the best tools for running AI models in terms of compute cost and efficiency?

    -Nvidia GPUs are considered the best tools for running AI models due to their performance and efficiency. However, Apple and AMD are getting closer in terms of providing competitive options.

  • Should one rent or buy GPUs for AI development?

    -For those who want to experiment and work with various tools or developers who want to do in-depth work, buying their own GPU makes more sense than renting from services like RunPod or Tensor Dock.

  • What is the current status of Nvidia's GPU releases and their messaging?

    -Nvidia's messaging is all over the place with many releases in the last week. They have a variety of Enterprise CPUs aimed at specific tasks and have been tagging AI onto everything.

  • When is the Nvidia 5090 GPU expected to be released?

    -The exact release date for the Nvidia 5090 GPU is uncertain, but it is speculated that it may be released towards the end of 2024.

  • What are the key features of the newly released Nvidia RTX 40 Super Series GPUs?

    -The new RTX 40 Super Series GPUs offer improved performance for gaming and AI-powered PCs, with features like DLSS technology for pixel inference and AI tensor cores for high-performance deep learning inference.

  • How does quantization impact the performance and capacity of AI models on GPUs?

    -Quantization is a method that adjusts the representation of data sets, allowing larger AI models to be compressed and run on smaller GPUs without significant loss of accuracy or capability.

  • What is Nvidia's Tensor RT and why is it significant?

    -Nvidia's Tensor RT is an SDK for high-performance deep learning inference that includes an optimized runtime for low-latency, high-throughput inference applications. It improves efficiency and can significantly boost performance in certain tasks.

  • What are some of the large AI models that have been enabled with Tensor RT?

    -Some of the large AI models enabled with Tensor RT include Codex, LLaMA 70B, Cosmos 2, and Seismic 4M, which are used for various tasks such as coding assistance, multi-language understanding, and communication障碍的处理.

  • What is the potential of using Nvidia's A100 GPUs in custom setups?

    -Custom setups using Nvidia's A100 GPUs can offer significant performance for AI tasks. Users have managed to configure multiple A100 GPUs in unique ways to achieve high token processing rates for AI models.

  • What recommendations are there for those looking to purchase GPUs for AI development?

    -The Nvidia 3090 is recommended for its affordability and performance, while the 4070 Super is a good option for those focused on inference tasks due to its 16GB of fast RAM.

Outlines

00:00

🚀 Advancements in Open Source AI and GPU Options

The paragraph discusses the significant progress in open source AI, particularly highlighting the ease of running local generative AI models like Stable Diffusion for images and video, and transcribing podcasts quickly. It emphasizes the importance of Nvidia GPUs for these tasks, while acknowledging the competition from Apple and AMD. The main question revolves around whether to rent or buy GPUs, with a preference for buying for those wanting to experiment and work in-depth. The paragraph also touches on Nvidia's diverse product offerings and strategies, including enterprise CPUs and the anticipation surrounding new releases.

05:01

💡 Nvidia's GPU Releases and Market Position

This section delves into the specifics of Nvidia's recent GPU releases, focusing on the RTX 40 Super Series announced in January. It explains the 'Super' series as a means for Nvidia to stretch a generation of GPUs with performance improvements. The paragraph outlines the features of the new GPUs, such as increased Shader, RT, and AI teraflops, and the inclusion of DLSS technology for resolution enhancement. It discusses the target audience for these GPUs, which includes gamers and AI developers, and mentions the price points starting at $600. The discussion also touches on the performance comparisons with previous models and the potential value of older Nvidia GPUs.

10:01

📈 Innovations in AI Model Compression and Inference

The paragraph explores advancements in AI model compression, specifically the art of reducing the size of large AI models to fit on smaller GPUs. It discusses the progress made in quantization methods, which have allowed for significant reductions in model size without substantial loss of accuracy or capability. The focus is on the ability to run compressed models on GPUs like the 3090, 4060, and the newly released 4070 Super. The paragraph also highlights the importance of inference tasks and the different memory requirements compared to training. It mentions the EXL 2 method and its effectiveness in reducing model size for efficient inference on GPUs with limited RAM.

15:02

🌐 Nvidia Tensor RT and its Impact on AI Deployment

This section discusses the significance of Nvidia's Tensor RT platform, an SDK designed to optimize deep learning inference for high performance and low latency. It explains how Tensor RT improves efficiency and reduces RAM usage, leading to faster inference. The paragraph also covers Nvidia's integration of Tensor RT with other technologies like Nvidia Triton for model deployment. It highlights the successful application of Tensor RT with large models like Codex, LLaMA 70B, and Cosmos 2, showcasing its capabilities. The discussion also touches on the availability of Tensor RT for Windows users, making AI development more accessible. Finally, it mentions the innovative use of Nvidia GPUs in configurations outside of their intended design, highlighting the creativity and resourcefulness of the tech community.

🔧 Custom GPU Setups and Market Trends

The final paragraph narrates a Reddit user's experience in creating a custom GPU setup using Nvidia's A100 GPUs, which were initially designed for use in Nvidia's own chassis. It details the technical challenges and the creative engineering solutions used to connect multiple GPUs to a motherboard. The user's setup managed to achieve impressive performance, running 12 tokens per second, despite the complexity and high power consumption. The paragraph also reflects on the market trends, noting the increased difficulty in finding affordable A100 GPUs in the SXM4 format due to the widespread knowledge of their potential use outside of Nvidia's hardware. It concludes with a recommendation for those interested in such setups to reach out to the creator of the custom setup and a reminder to consider alternative GPU options like the 3090 or the 4070 Super for different needs.

Mindmap

Keywords

💡Open source AI

Open source AI refers to artificial intelligence systems that have publicly accessible code, allowing for collaborative development and modification. In the context of the video, this term highlights the rapid advancements in AI technology that have been made possible through the collective efforts of the global tech community, emphasizing the ease with which one can now run local AI models for various applications, such as image and video generation.

💡Nvidia GPUs

Nvidia GPUs, or Graphics Processing Units, are specialized hardware accelerators designed for handling complex graphical and parallel computing tasks. They are particularly effective for AI and machine learning applications due to their ability to process large amounts of data simultaneously. In the video, Nvidia GPUs are discussed as a leading choice for compute power needed to run AI models, with a comparison to other manufacturers like Apple and AMD.

💡DLSS

DLSS, or Deep Learning Super Sampling, is a technology developed by Nvidia that uses AI to upscale lower resolution images in real-time, effectively increasing performance while maintaining visual quality. It is an example of how AI can be leveraged to enhance gaming and creative experiences by intelligently predicting and generating pixels, thus reducing the computational load.

💡AI quantization

AI quantization is a process that reduces the precision of AI model parameters to require less memory and computational resources, making it possible to run larger models on smaller hardware. This technique is crucial for deploying AI models on devices with limited resources, such as consumer-grade GPUs.

💡TensorRT

TensorRT is an SDK (Software Development Kit) by Nvidia that optimizes deep learning models for deployment, offering high-performance inference on Nvidia GPUs. It includes a runtime and optimizations that deliver low latency and high throughput for AI applications, making it a valuable tool for developers working with AI models.

💡Enterprise Hardware

Enterprise Hardware refers to high-performance computing systems and components designed for business and industrial use, often with a focus on reliability, scalability, and support for complex tasks. These systems are typically more powerful and robust than consumer-grade hardware, catering to the needs of professional environments.

💡AI development

AI development encompasses the process of designing, training, and deploying artificial intelligence models. This includes tasks such as coding, data preparation, model optimization, and integration into applications or services. AI development is a rapidly growing field, with a focus on creating increasingly sophisticated and efficient AI systems.

💡Gaming

Gaming refers to the creation, distribution, and play of video games, which often involve interactive electronic systems and virtual environments. The gaming industry is a significant consumer of technology, particularly graphics and processing power, making it a key beneficiary of advancements in GPU technology.

💡AI-powered PCs

AI-powered PCs are personal computers equipped with hardware and software that enable them to run AI applications and models. These systems are designed to handle the computational demands of AI tasks, such as machine learning, deep learning, and neural network processing.

💡Inference

In the context of AI, inference refers to the process of using a trained model to make predictions or decisions based on new input data. It is a critical step in applying AI models to real-world problems and is generally less resource-intensive than the training process.

Highlights

Open source AI has seen massive advancements in the last year, making it easier to run local LLMs and generative AI like Stable Diffusion for images and video, and transcribe entire podcasts in minutes.

Nvidia GPUs are considered the best option in terms of compute cost and versatility, with Apple and AMD getting closer but not yet at par.

The decision between renting or buying GPUs comes down to individual needs, with purchasing making more sense for those who want to experiment and develop in-depth.

Nvidia's messaging can be confusing, with a variety of products aimed at different markets, but their focus on AI over consumer GPUs is clear.

The release of the new RTX 40 Super Series in early January 2024 has brought performance improvements to stretch the GPU generation further.

The RTX 4080 Super and RTX 4070 Super are positioned as the core of AI-powered PCs, with the 4070 Super starting at $600.

Nvidia's DLSS technology allows for AI-generated pixels, increasing resolution without additional ray tracing, and claims to accelerate frame rates by up to four times with better image quality.

The GeForce RTX Super GPUs are marketed as the ultimate way to experience AI on PCs, with Tensor Cores delivering high performance for deep learning inference.

Nvidia's Tensor RT is an SDK for high-performance deep learning inference, optimizing runtime for low latency and high throughput in inference applications.

Quantization methods in AI have improved, allowing large models to be compressed to run on smaller GPUs without significant loss of accuracy.

The 4070 Super is recommended for those doing inference and using AI models, as it provides a good balance of performance and cost.

Nvidia's Tensor RT platform is becoming more accessible to consumer cards, previously restricted to workstation cards.

The A100 GPUs, originally intended for Nvidia's chassis, have been repurposed by users to create powerful and cost-effective AI computing setups.

The user on Reddit, Boris, demonstrated a setup with five A140 GPUs running at 450 watts each, showcasing the potential of repurposed Nvidia hardware.

The Goliath 8bit model with GGF outperformed the EXL 2 6bit model on Boris's setup, indicating potential advantages in GPU-to-GPU transfers.

The 3090 GPU is still a strong recommendation due to its affordability and performance, especially for those looking for a balance of cost and capability.

The 4070 Super is a viable option for those focusing on inference tasks and local AI development, with 16GB of RAM providing sufficient speed.

The video encourages viewers to share their thoughts in the comments, promoting engagement and discussion on the topic.

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