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 costs and emphasizes the dominance of Nvidia GPUs. The video also explores the option of renting vs. buying GPUs, the latest Nvidia RTX 40 Super Series releases, their features, and performance comparisons. It delves into the potential of the Nvidia Tensor RT platform for AI inference and showcases the capabilities of the latest models like Codex, Cosmos, and M4. The video concludes with a look at the enterprise hardware options and the impressive DIY setup of multiple A100 GPUs by a Reddit user.


  • 🚀 Open source AI has significantly advanced, making it easier to run local LLMs and generative AI like Stable Diffusion for images and video, and transcribe podcasts quickly.
  • 💰 Nvidia GPUs are currently the best option in terms of compute cost and versatility, with Apple and AMD closing the gap.
  • 💡 The decision to rent or buy GPUs depends on the user's needs, with buying being more suitable for experimentation and in-depth work.
  • 🎉 Nvidia released the RTX 40 Super Series in early 2024, focusing on improved GPU performance and AI capabilities, starting at $600.
  • 🌟 The new GPUs claim to deliver high Shader, RT, and AI teraflops, with DLSS technology enhancing resolution without additional ray tracing.
  • 📈 Nvidia's Ada Lovelace architecture-based GPUs aim to supercharge gaming and AI-powered PCs, with TensorRT being a significant part of the platform.
  • 🔍 The RTX 4070 Super is positioned as a cost-effective option for AI inference tasks, with 16GB of RAM and improved performance over previous models.
  • 🔥 The potential of model quantization is highlighted, allowing large AI models to run on smaller GPUs while maintaining functionality.
  • 🔧 Nvidia's TensorRT is an SDK for high-performance deep learning inference, with optimizations that improve latency and throughput for inference applications.
  • 🌐 The script mentions the use of TensorRT with models like Codex, Cosmos, and M4, showcasing its capabilities in various AI applications.
  • 💸 The rise of creative solutions for deploying Nvidia GPUs, such as using A100 GPUs in custom setups, demonstrates the community's drive for efficient and cost-effective AI computing.

Q & A

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

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

  • What factors should be considered when choosing the best tools for AI computation in terms of cost?

    -The cost of compute can be determined by considering the cost of tokens per dollar, and for most options, Nvidia GPUs are currently the best choice. Apple and AMD are getting closer in terms of competitiveness.

  • What is the main advantage of buying your own GPU over renting?

    -Buying your own GPU makes more sense for people who want to experiment, mix and match with different kits, or developers who want to do more in-depth work, as opposed to renting on services like RunPod or Tensor Dock.

  • What is Nvidia's position in the AI compute market compared to other companies?

    -Nvidia is clearly more focused on AI than consumer GPUs, offering a wide range of options for AI compute and being a leader in the market.

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

    -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.

  • What is the significance of the Nvidia Tensor RT platform?

    -The Tensor RT platform is an SDK for high-performance deep learning inference, which includes optimizations and a runtime that deliver low latency and high throughput for inference applications.

  • How does the EXL 2 quantization method impact the ability to run large AI models on smaller GPUs?

    -The EXL 2 quantization method allows for large AI models to be compressed into a smaller size, enabling them to run on GPUs with less VRAM, such as the 3090 or 4060.

  • What is the current status of the speculated Nvidia 5090 GPU?

    -The release date for the Nvidia 5090 GPU is uncertain. It is speculated that it may not come out until the end of 2024 at the earliest.

  • What are the main differences between the 4070 Super and the 3090 GPUs?

    -The 4070 Super is advertised as being faster than a 3090 at a fraction of the power, but it maintains the same price point. The 4070 Super has 16GB of RAM, which can be sufficient for running inference of AI models.

  • How has the availability of Nvidia A100 GPUs in the market changed due to the discovery of their alternative use cases?

    -Since the discovery of alternative use cases for Nvidia A100 GPUs, their availability in the market has decreased, with the SXM4 format becoming harder to find at reasonable prices due to increased demand.

  • What is the potential performance boost provided by the Tensor RT LLM compared to other models?

    -The Tensor RT LLM can provide an 8X performance boost compared to the A100, and about a 2X boost when compared to the H100, making it a significant improvement for certain applications.



🚀 Advancements in Open Source AI and GPU Options

The paragraph discusses the significant progress in open source AI, particularly in the first month of 2024, highlighting the ease of running local LLMs and generative AI for images and videos. It raises the question of the best tools for this, focusing on the cost of compute in terms of tokens per dollar. Nvidia GPUs are identified as the leading option, with Apple and AMD closing the gap. The discussion then转向s whether to rent or buy GPUs, suggesting that owning a GPU makes more sense for those who want to experiment and conduct in-depth work. The paragraph also touches on Nvidia's messaging and the variety of Enterprise CPUs available, which may not be as accessible to consumers.


💡 Nvidia's New GPU Releases and Deep Learning Super Sampling Technologies

This section delves into Nvidia's recent GPU releases, specifically the RTX 40 Super Series, and their implications for gaming and AI-powered PCs. It mentions the features enabled in the newest GPUs and speculates on the release of the 5090 in 2024. The paragraph highlights the capabilities of the new GPUs, such as Shader teraflops, RT teraflops, and AI tops, and discusses the pricing and performance comparisons with previous models. It also covers Nvidia's DLSS technology, which allows for AI-generated pixels to increase resolution without additional ray tracing, and the company's focus on AI tensor cores for improved deep learning inference.


🌐 Nvidia's Focus on LLMs and AI Development

The paragraph focuses on Nvidia's efforts in Windows tooling to facilitate AI development, particularly in the context of LLMs. It discusses the capabilities of Nvidia GPUs in video manipulation for streamers, such as real-time chroma key and noise removal. The paragraph provides a detailed comparison of various Nvidia GPUs, including the 480 Super, 4070 TI Super, and 4070 Super, and their performance relative to older models. It also explores the concept of model quantization, which allows for the reduction of large models to fit on smaller GPUs, and the potential of the 4070 Super for inference tasks and AI development.


🔍 Nvidia Tensor RT Platform and Its Impact on AI Efficiency

This section highlights the significance of Nvidia's Tensor RT platform, an SDK for high-performance deep learning inference that optimizes runtime for low latency and high throughput. It discusses the benefits of the platform in terms of efficiency and performance, and how it integrates with other Nvidia technologies like Triton. The paragraph also mentions the enhanced performance of specific AI models using Tensor RT, such as Codex LLaMA 70B, Cosmos 2, and seamless m4t. It emphasizes Nvidia's pride in Tensor RT and its introduction to consumer cards, as well as the community's innovative use of Nvidia GPUs, including the assembly of multiple GPUs on a single system.

🛠️ Custom GPU Setups and Recommendations for AI Hardware

The final paragraph shares a Reddit user's experience in creating a custom GPU setup using Nvidia A100 GPUs, showcasing the challenges and impressive results of such a configuration. It details the power requirements, PCIe switch connections, and the use of P2P RDMA for efficient GPU communication. The paragraph also touches on the performance of different AI models on this setup and the user's decision to sell the system due to the complexity of assembly. It concludes with the author's recommendations for GPU options, favoring the 3090 for its affordability and the 4070 Super for those focused on inference tasks, and encourages viewers to share their thoughts and experiences.



💡Open Source AI

Open Source AI refers to artificial intelligence systems whose source code is made available to the public, allowing for collaborative development and modification. In the context of the video, it is associated with the significant advancements in AI technology that have been made over the past year, making it easier for users to run local AI models for various applications such as image and video generation.

💡Local LLMs

Local Large Language Models (LLMs) refer to AI models that are run on an individual's local machine or device, as opposed to being accessed through a cloud service. The video discusses the ease of running such models locally, indicating a shift towards more accessible and powerful AI tools that can be utilized without reliance on external servers.

💡Nvidia GPUs

Nvidia Graphics Processing Units (GPUs) are specialized hardware accelerators used in computing, particularly for processing complex图形 and parallel computations. In the video, Nvidia GPUs are highlighted as a preferred choice for AI and deep learning tasks due to their performance and the variety of options they offer for different user needs.


Deep Learning Super Sampling (DLSS) is a technology developed by Nvidia that uses AI to upscale lower resolution images in real-time, effectively rendering higher quality visuals with less computational overhead. It is a key feature of Nvidia's RTX series GPUs and is central to the discussion in the video, which highlights its ability to improve gaming performance and image quality.

💡AI Tensor Cores

AI Tensor Cores are specialized processing units within Nvidia GPUs designed to accelerate deep learning tasks, such as inference and training. They are optimized for high throughput and low latency, making them ideal for AI-powered applications. The video emphasizes the importance of these cores in delivering the computational power needed for AI tasks on PCs.


Quantization in the context of AI and machine learning is the process of reducing the precision of a model's parameters to save space and reduce computational requirements. This technique allows for larger models to be compressed into a size that can be efficiently run on hardware with limited resources, such as GPUs with less memory.

💡Tensor RT

Tensor RT is an SDK (Software Development Kit) by Nvidia that optimizes deep learning models for deployment on their GPUs. It includes a runtime and libraries for high-performance inference, improving the efficiency and speed of AI applications. The video highlights Tensor RT as a significant technology that enables enhanced performance on Nvidia GPUs for AI tasks.

💡Enterprise Hardware

Enterprise Hardware refers to high-end, specialized computing equipment designed for business and industrial use, often characterized by high performance, reliability, and scalability. In the video, the discussion around enterprise hardware focuses on the capabilities of Nvidia's AI-focused GPUs and the potential for consumers to utilize these for advanced AI tasks.

💡AI Development

AI Development involves the creation, training, and deployment of artificial intelligence models and applications. The video discusses various aspects of AI development, including the hardware required for different stages of development, such as inference and training, and the impact of advancements in AI hardware on the ease and efficiency of AI development.


In the context of AI and machine learning, inference refers to the process of using a trained model to make predictions or decisions based on new input data. It is a critical component of AI applications and is often distinguished from the training process, which involves learning from a dataset to improve the model's accuracy.

💡Consumer Cards

Consumer Cards refer to graphics cards and other hardware components that are designed and marketed for general consumer use, as opposed to specialized enterprise or industrial applications. The video discusses the features and capabilities of consumer-grade Nvidia GPUs, which have become increasingly powerful and accessible for AI and deep learning tasks.


Open source AI has seen massive advancements in the past 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 closing the gap.

The decision between renting or buying GPUs leans towards purchasing for those who want to experiment and develop with various tools and kits.

Nvidia's messaging can be confusing, with a variety of Enterprise CPUs targeted at specific tasks and the company's tendency to label everything with 'AI'.

The release of Nvidia's new RTX 40 Super Series GPUs in early January is significant, as these are designed to stretch the performance of a GPU generation further.

The new GPUs boast up to 52 Shader teraflops, 121 RT teraflops, and 836 AI tops, indicating increased compute capabilities.

Nvidia's DLSS technology can infer pixels to increase resolution without additional ray tracing, offering up to 4 times faster performance with better image quality.

The RTX 4070 Super, starting at $600, is marketed as an AI-powered PC core, capable of handling the latest games and deep learning tasks.

The 4080 Super and 4070 Super are positioned as high-performance options for gaming and AI development, with the 4070 Super being 20% more performant than the RTX 470.

Nvidia's Tensor RT is an SDK for high-performance deep learning inference, designed to deliver low latency and high throughput for inference applications.

Tensor RT has been enabled for major models like Codex, LLaMA 70B, and Cosmos 2, showcasing its versatility and potential for various AI applications.

The Reddit user Boris demonstrated the potential of using Nvidia A100 GPUs in unconventional setups, achieving high performance for AI tasks.

The innovative use of Nvidia GPUs in a DIY setup by Boris, involving multiple GPUs and a complex PCIe switch, highlights the community's creativity in leveraging technology.

The discussion around the capabilities and pricing of different Nvidia GPUs, such as the 3090, 4070 Super, and 4080 Super, provides valuable insights for those looking to invest in AI hardware.

The video emphasizes the importance of Linux for AI development, while also noting the improvements made by Nvidia for Windows users.

The presenter's recommendation of the 3090 as a cost-effective and powerful option for AI tasks, due to its affordability and performance, is noteworthy.

The video concludes with an invitation for viewers to share their thoughts and experiences, fostering a community of AI enthusiasts and professionals.