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 computing, favoring Nvidia GPUs for their versatility and performance. The discussion includes the cost-benefit analysis of renting vs. buying GPUs and the latest releases from Nvidia, including the RTX 40 Super Series. It also touches on the potential of the upcoming 5090 GPU, the progress in LLM quantization, and the significance of Nvidia's Tensor RT platform for AI development. The video concludes with a real-world example of using Nvidia GPUs for high-performance AI tasks and recommendations on which GPUs to consider for different needs.


  • ๐Ÿš€ Open source AI has significantly advanced, making it easier to run local LLMs and generative AI for images, video, and podcast transcriptions.
  • ๐Ÿ’ฐ Nvidia GPUs are currently the top choice for compute cost and token efficiency, with Apple and AMD closing the gap.
  • ๐Ÿ’ก Renting vs. buying GPUs depends on individual needs; for experimentation and development, buying may be more cost-effective.
  • ๐ŸŽฏ Nvidia's messaging can be confusing, with a variety of products aimed at different markets, but their GPUs offer the most options.
  • ๐Ÿค” The release of Nvidia's new RTX 40 Super Series GPUs in early 2024 brings performance improvements and focuses on AI capabilities.
  • ๐Ÿ”ข The new GPUs boast high teraflops for shader, RT, and AI computations, with the 4070 Super starting at $600.
  • ๐ŸŽฎ Nvidia's AI advancements include DLSS technology, which allows for AI-generated pixels, improving resolution without intensive ray tracing.
  • ๐Ÿ’ป The GeForce RTX Super GPUs are marketed as the ultimate way to experience AI on PCs, with Tensor Cores enhancing deep learning inference.
  • ๐Ÿ“ˆ Nvidia Tensor RT is an SDK for high-performance deep learning inference, improving efficiency and reducing RAM usage for inference applications.
  • ๐ŸŒ Tensor RT has been enabled for large language models like Codex, LLaMA 70B, and Cosmos 2, showcasing its versatility.
  • ๐Ÿ”ง Creative engineering has enabled the use of Nvidia's A100 GPUs outside of their intended hardware configurations, offering high performance at a cost.

Q & A

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

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

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

    -Nvidia GPUs are considered the best option for running AI models in terms of compute cost and efficiency, with Apple and AMD getting closer in competition.

  • Should one rent or buy GPUs for AI development?

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

  • What is the current status of the Nvidia RTX 5090 release?

    -As of the knowledge cutoff in early 2024, the release date of the Nvidia RTX 5090 is uncertain, with speculation that it may not come until the end of 2024.

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

    -The Nvidia RTX 40 Super Series GPUs offer improved performance for gaming and AI tasks, with features like DLSS (Deep Learning Super Sampling) technology for pixel inference and improved AI capabilities.

  • How does the performance of the RTX 4070 Super compare to previous models?

    -The RTX 4070 Super is claimed to be faster than a 3090 at a fraction of the power, but it maintains the same price point, and its performance is about 1.6 times faster than a 3070 Ti.

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

    -The Nvidia Tensor RT platform is an SDK for high-performance deep learning inference, offering optimized runtime for low-latency, high-throughput inference applications.

  • How has LLM quantization progressed recently?

    -LLM quantization has made significant progress, allowing for larger models to be compressed to fit on smaller GPUs without significant loss of accuracy or capability.

  • What are some of the models enhanced with Tensor RT?

    -Some models enhanced with Tensor RT include Codex LLaMA 70B, Cosmos 2 from Microsoft Research, and the multimodal foundational model, Seamless M4T.

  • What is the potential of using Nvidia A100 GPUs in a DIY setup?

    -DIY setups using Nvidia A100 GPUs can offer significant performance for AI tasks, with some users managing to run multiple A100 GPUs together for high-speed processing, though this requires advanced technical knowledge and can be costly.

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

    -For those in the market for GPUs, the Nvidia 3090 is recommended for its affordability and performance, while the 4070 Super is a good option for those focusing on inference tasks and needing a GPU with at least 16GB of RAM.



๐Ÿš€ Advances 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 video. 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 choice, with Apple and AMD closing the gap. The discussion then่ฝฌๅ‘s the decision between renting or buying 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 varied messaging and the challenge of choosing the right GPU, especially with the anticipation of new releases and the potential value of older models. The video's aim to condense information and provide insights on the latest Nvidia GPUs and their capabilities is also mentioned.


๐ŸŒŸ Nvidia's New Releases and AI Capabilities

This paragraph delves into Nvidia's recent releases, specifically the RTX 40 Super Series GPUs announced in January, and their AI capabilities. It explains that the Super Series signifies performance improvements from Nvidia to extend a generation of GPUs. The new GPUs are positioned as having AI as their superpower, with features like DLSS (Deep Learning Super Sampling) that allows for AI-generated pixels to enhance resolution without additional ray tracing. The paragraph also covers the technical specifications of the 4080 Super and 4070 Super, their price points, and comparisons with previous models. It discusses the concept of AI-powered PCs and Nvidia's Tensor RT, which is an SDK for high-performance deep learning inference, and how it improves efficiency and performance for AI applications.


๐Ÿ’ก Innovations in LLM Quantization and GPU Performance

The focus of this paragraph is on the advancements in LLM quantization, which allows for the reduction of large AI models to fit on smaller GPUs. It discusses the progress made in this area, particularly with models like LLaMA 2 and Hugging Face Transformers, which have enabled the compression of large models without significant loss of accuracy or capability. The paragraph also explores the implications of these advancements for GPU selection, questioning the necessity of high RAM capacity in GPUs for AI development tasks. It highlights the capabilities of the 4070 Super and 3090 GPUs in running compressed AI models and the importance of memory bandwidth for inference tasks. The discussion concludes with a look at the potential of the Tensor RT platform and its impact on AI model deployment and performance.


๐Ÿ”ง Custom GPU Configurations and Market Trends

This paragraph narrates a Reddit user's experience with configuring a custom GPU setup using Nvidia A100 GPUs, which were initially intended for use in Nvidia's own chassis. It details the technical challenges and achievements of wiring up to five A140 GPUs, each with a high power draw, and the use of a dedicated PCIe switch with extenders to connect them to a motherboard. The setup leveraged P2P RDMA technology to enable efficient communication between GPUs without direct physical connections. The user managed to run a variety of AI models on this system, showcasing its capabilities. The paragraph also touches on the market trends for these types of GPUs, noting the difficulty in finding affordable A140 40 and 80 GB GPUs due to the increased demand and knowledge of their potential use outside of Nvidia's hardware. It concludes with a recommendation for those interested in such configurations to reach out to the creator of the setup for advice and potentially purchase the necessary hardware directly.

๐Ÿ“Š GPU Recommendations and Future Outlook

The final paragraph provides a summary of the narrator's recommendations for GPUs based on their experience and the current market trends. It suggests that while the 3090 GPU remains a strong and affordable option, the 4070 Super is also a good choice for those primarily focused on inference tasks due to its 16 GB of RAM. The paragraph discusses the potential of the Tensor RT platform to bring down advanced AI capabilities to consumer cards and the excitement around this development. It also mentions the impressive custom GPU setup built by a Reddit user, highlighting the creative ways people are finding to maximize the performance of their systems. The paragraph concludes with a call to action for viewers to share their thoughts and experiences, and an invitation to engage with the content by liking, subscribing, and sharing the video.



๐Ÿ’กopen 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, this has led to significant advancements in the field, making it easier for individuals to run local AI models and perform tasks like image and video generation.

๐Ÿ’กNvidia GPUs

Nvidia GPUs, or Graphics Processing Units, are specialized hardware designed for handling complex graphical and computational tasks. They are highlighted in the video as a top choice for compute power needed to run AI models, with a discussion on whether to rent or buy them for optimal performance and cost-effectiveness.

๐Ÿ’กcompute cost

Compute cost refers to the expenses associated with performing computational tasks, typically measured in terms of monetary cost, energy consumption, or resource usage. In the video, the compute cost is discussed in relation to the pricing of tokens and the overall expense of using Nvidia GPUs for AI computations.

๐Ÿ’กRTX 40 Super Series

The RTX 40 Super Series is a line of GPUs released by Nvidia, designed to enhance gaming and creative computing experiences with improved AI capabilities. These GPUs are characterized by their high performance in shader, ray tracing, and AI tensor operations, making them suitable for a variety of AI-powered tasks.


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 visual quality without the need for more intensive ray tracing. It is an example of how AI can enhance graphical performance in gaming and other visually intensive applications.

๐Ÿ’กAI tensor cores

AI tensor cores are specialized processing units within Nvidia GPUs that are optimized for deep learning computations, particularly for AI inference tasks. They are designed to deliver high throughput and low latency, making them ideal for running AI models efficiently.


Quantization is a process in machine learning that reduces the size of AI models by decreasing the precision of the numerical values used in the model. This technique allows for larger models to be compressed into smaller sizes, making them suitable for deployment on GPUs with limited memory.


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 step in applying AI models to real-world tasks and is often less resource-intensive than the training process.


TensorRT is an SDK (Software Development Kit) by Nvidia that optimizes deep learning models for deployment on their GPUs. It includes tools for inference optimization and runtime that enhance the efficiency and performance of AI models, leading to faster and more efficient AI computations.

๐Ÿ’กA100 GPUs

A100 GPUs are high-end, data center-class graphics processing units developed by Nvidia, specifically designed for complex AI and deep learning tasks. These GPUs offer advanced features and high performance, making them suitable for enterprise-level AI computations.

๐Ÿ’กEnvy Link and RDMA

Envy Link is a high-bandwidth, low-latency interconnect technology developed by Nvidia that allows multiple GPUs to communicate directly with each other. RDMA (Remote Direct Memory Access) is a method of directly accessing memory on a remote computer without involving the processor, which can be utilized in GPU-to-GPU communication for faster data transfers.


Open source AI has made 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 on par.

The decision between renting or buying GPUs comes down to personal needs, with buying being more sensible 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 is clear.

The RTX 40 Super Series GPUs were released in early January, offering performance improvements to stretch out the GPU generation.

The new GPUs deliver up to 52 Shader teraflops, 121 RT teraflops, and 836 AI tops, indicating a significant increase in compute capabilities.

DLSS technology allows for AI-generated pixels, increasing resolution without additional ray tracing, and claims to accelerate full-rate racing by up to four times with better image quality.

Nvidia's TensorRT is an SDK for high-performance deep learning inference, focusing on low latency and high throughput for inference applications.

The new GPUs are capable of running large language models like Codex and LLaMA 70b, showcasing the power of TensorRT.

Quantization methods have advanced to the point where large models can be compressed to run on smaller GPUs, making them more accessible for various tasks.

The RTX 4070 Super is recommended for those doing inference and working with models, as it offers a good balance of performance and cost.

Nvidia's TensorRT and Triton technologies are designed to improve the deployment of AI models in applications.

The Reddit community has found ways to use cheaper, enterprise-grade GPUs for high-performance AI tasks, showcasing the potential for cost-effective AI development.

The user 'Boris' on Reddit demonstrated how to run multiple A100 GPUs in a unique setup, achieving impressive performance for AI tasks.

The advancements in AI and GPU technology are making it possible for individuals to achieve high-performance results that were previously only available to large corporations or institutions.

The RTX 3090 remains a strong option for those looking for an affordable and powerful GPU for AI and inference tasks, with its 24GB of RAM and high performance.

The video encourages viewers to share their thoughts and recommendations on the discussed GPUs and technologies, fostering a community of AI enthusiasts and developers.