NVIDIA is quickly becoming one of the most impactful AI-focused companies in the world – offering organizations both the hardware and software they need to accelerate AI initiatives. But to really unlock the power of this brand’s growing ecosystem, companies need a comprehensive plan for optimizing AI with NVIDIA tools.
There’s more to upgrading your AI strategy with NVIDIA than simply purchasing the right GPUs, or investing in a DGX system. You need to figure out how you’re going to fine-tune your models for complex deep-learning tasks, leverage NVIDIA software, and even manage resource allocation.
- Find out everything you need to know about NVIDIA’s evolving ecosystem in this guide to NVIDIA AI for enterprises. Or take a deep dive into some of NVIDIA’s cutting-edge solutions with our guides on NVIDIA AI GPUs, NVIDIA AI Enterprise, and NVIDIA DGX systems.
Based on our research into the top challenges companies face with their AI initiatives, here’s our step-by-step guide to optimizing AI with NVIDIA tools.
Optimizing AI with NVIDIA Tools: The NVIDIA Ecosystem
NVIDIA’s AI ecosystem has evolved drastically in the last few years. Today, companies can access various versatile tools, from the CUDA Compute Unified Device Architecture (CUDA) to TensorRT for deep learning, and even Agentic AI Blueprints.
No matter what your AI strategy might involve, from building agentic workflows to designing analytics platforms, NVIDIA has something to offer every organization. You can even use a comprehensive cloud platform (NVIDIA AI Enterprise).
Outside of the software and cloud computing space, NVIDIA also manufactures some of the best hardware for AI technologies. It produces cutting-edge GPUs and comprehensive DGX systems—purpose-built AI supercomputers that integrate storage, GPUs, networking capabilities, and more.
The real power of NVIDIA’s ecosystem emerges when all of these tools and solutions are working in harmony. Developers can utilize CUDA to write efficient code that leverages GPU acceleration, employ TensorRT to optimize models for real-time inference, and deploy these models on DGX Systems for unparalleled performance.
Optimizing AI with NVIDIA Tools: Steps for Success
So, how can you make sure you’re really optimizing AI with NVIDIA tools? Ultimately, the right strategy for success depends on your end goals. For instance, if you’re diving into the era of Agentic AI, you’ll probably want to explore the NVIDIA AI Enterprise platform. Plus, you might tap into NIM microservices and AI Blueprints combined with DGX systems.
On a broader scale, however, there are steps you can take regardless of your AI initiative that should help you achieve more with your NVIDIA tech.
1. Best Practices for Tuning AI Models on NVIDIA GPUs
Let’s start with fine-tuning your AI models with NVIDIA technology. One of the great things about NVIDIA’s hardware is that it’s custom-built to accelerate training, development, and inference in AI model strategies. Here’s how you can take full advantage of NVIDIA’s systems:
- Mixed-Precision Training: Implementing mixed-precision training, combining 16-bit and 32-bit floating-point operations, can drive significant performance gains without problems with accuracy. This approach leverages the Tensor Cores in NVIDIA GPUs too, accelerating computation and reducing memory usage.
- Efficient memory management: NVIDIA GPUs already benefit from robust storage and memory capabilities. Still, it’s worth making sure you’re using memory effectively. Techniques like memory coalescing, minimizing data transfers between CPU and GPU, and utilizing shared memory can reduce latency and improve throughput.
- Use NVIDIA Profiling Tools: Solutions like NVIDIA Nsight Systems and Nsight Compute are great for profiling your applications. These tools help identify bottlenecks and provide insights into GPU utilization – helping to streamline optimization.
With these strategies (and a plan for choosing the right GPU or DGX System), you can enhance the performance of your AI models running on NVIDIA hardware.
2. Accelerating Inference with NVIDIA TensorRT
NVIDIA TensorRT is one of the most powerful solutions for Optimizing AI with NVIDIA tools. This is particularly true if you’re focusing on deep-learning initiatives. Developers can convert trained models to TensorRT to take advantage of various great features, like precision calibration, layer fusion, and kernel auto-tuning, which minimize latency.
As you develop in this ecosystem, ensure the “batch size” is optimized for your specific application to balance throughput and latency. You could even look at using INT8 quantization to further accelerate inference on a massive scale.
Amazon is a great example of a company that has demonstrated the value of TensorRT for AI optimization. The company deployed a T5 NLP model for automatic spelling correction using this solution alongside the NVIDIA Triton Inference server. The integration reduced inference latency to under 50 milliseconds and increased throughput by five times!
3. Leveraging CUDA for Enhanced Performance
If you’re not familiar, NVIDIA CUDA is a programming model and parallel computing platform. It’s designed to help developers make the most of GPUs for various applications. It gives organizations access to the memory and virtual instruction sets they need to design applications at scale.
To make the most out of this platform, you’ll first need to “parallelize” your workloads. Identify portions of code that can run concurrently and restructure them to take advantage of NVIDIA’s GPUs. Again, make sure you’re optimizing memory access to ensure faster data retrieval and increase bandwidth utilization.
Perplexity AI is a great example of a leading tech company optimizing AI with NVIDIA tools like CUDA. This organization used the CUDA ecosystem (alongside other NVIDIA tools) to enhance the performance of its proprietary large language model – achieving massive performance gains.</p>
4. Ensuring Efficiency Across Diverse Workloads
As you scale your AI strategy, you’ll likely notice that AI applications generally involve varying workloads. Maintaining efficiency across those workloads can be complicated. Taking advantage of NVIDIA solutions, like the NVIDIA AI Enterprise platform, with optimized NIM Microservices can help. The NIM microservices help to accelerate LLM throughput by 5 times and improve retrieval throughput by two times, on average.
Beyond taking advantage of flexible cloud-based solutions, other strategies you can explore include:
- Dynamic Resource Allocation: Implement systems that adjust GPU resource allocation based on workload demands in real-time. This should help to improve resource allocation, reduce waste, and improve performance.
- Workload Balancing: Distribute tasks evenly across available GPUs to prevent some units from being overburdened while others are underutilized. Tools like NVIDIA’s Base Command software for DGX systems can help here.
- Utilize Scheduling Tools: NVIDIA’s scheduling tools are great for managing and allocating resources effectively. They can ensure that high-priority tasks receive the necessary computational power without delay.
5. Advanced Strategies for Optimizing AI with NVDIA Tools
Usually, more advanced strategies for optimizing AI with NVIDIA tools require advanced technical knowledge or expert support (like NVIDIA’s DGXperts team). However, if you already have access to the proper support, you can experiment with strategies like model pruning and quantization to minimize model complexity and improve inference speeds.
Staying up-to-date can make a huge difference. Regularly monitor NVIDIA’s website (and visit us here) for new insights into the latest tools and resources the company offers (like Agentic AI blueprints). Use monitoring and analytical tech to track the performance of your AI solutions in real time and make gradual updates.
Remember to invest in training and upskilling your teams, too. Providing employees with insights into how they can take full advantage of NVIDIA systems should help to foster a culture of continuous improvement and innovation.
Optimizing AI with NVIDIA Tools: Case Studies
Want an insight into how companies worldwide are already optimizing AI with NVIDIA tools? Here are just some great case studies to educate, and inspire you.
- Terray Therapeutics: In the pharmaceutical space, Terray Therapeutics leveraged the NVIDIA DGX Cloud system to train foundation models for chemistry tasks. This helped them to produce generative AI solutions that can actively design small molecules, speeding up the drug discovery process and reducing costs.
- Amgen: In healthcare, Amgen uses the NVIDIA DGX Cloud and BioNeMo to train LLMs for designing and predicting protein properties. Optimizing AI with NVIDIA tools allowed Amgen to accelerate drug discovery efforts and produce a range of unique therapies.
- Accenture: Global consulting firm Accenture partnered with NVIDIA and Microsoft to transform its AI solutions. By leveraging NVIDIA’s advanced hardware and software platforms, Accenture improved its AI performance, creating industry-specific Copilots for clients across sectors like manufacturing, retail, and energy.
Optimizing AI with NVIDIA Tools: Finishing Thoughts
Worldwide, countless organizations are optimizing AI with NVIDIA tools, achieving greater efficiency, stronger results, and better accuracy at scale. Whether you’re building a new generation of agentic AI solutions for your teams, using AI for product development, or even designing custom models, NVIDIA can help you take your AI initiative to the next level
