Large Language Models (LLMs) demand substantial computational resources for inference. GPUs offer high memory bandwidth and dedicated hardware designed specifically to accelerate these workloads.
Selecting an optimal GPU for LLM inference requires taking into account key considerations, including model size, precision levels and fine-tuning techniques. A GPU with more CUDA cores and Tensor cores improves performance significantly.
1. NVIDIA H100
The NVIDIA H100 GPU offers massive compute power for AI and HPC tasks, thanks to its Hopper architecture and Transformer Engine, setting new standards of efficiency and power consumption. This GPU has revolutionized AI deployments while driving advances in areas like autonomous systems research.
NVIDIA H100 GPUs feature an expansive memory bandwidth, making them capable of handling very large models with ease. Their large memory bandwidth enables engineering teams to train faster while their lower latency processing helps eliminate bottlenecks in I/O operations for improved efficiency – making these GPUs perfect for machine learning training, HPC applications, and LLM inference applications.
An H100 GPU provides over 3 TB/s of memory bandwidth and supports up to 16GB of GDDR5 RAM, making it suitable for AI/ML, HPC and deep learning projects. Equipped with features such as NVLink and FP16 precision for exceptional scalability. A single H100 can increase performance for machine learning tasks up to fourfold; making it perfect for large language models (LLMs) or GPT-series NLPs.
Combined with the MAX platform, the NVIDIA H100 makes developing, training, optimizing and deploying AI models easier than ever. Its superior scalability and architecture innovations help engineers optimize inference pipelines while speeding up research projects and production deployment times.
H100 GPUs are now available as on-demand instances on Paperspace and Hyperstack, making them easy to try on test projects for free and use the resultant model to make predictions with low latency inference.
Reserved instances provide a flexible solution for longer-term projects at predictable costs, eliminating the need for expensive hardware that might never get used and allowing you to pay only for what you use.
RunPod’s H100 instances are housed in Tier 3/4 data centers offering enterprise-grade security, compliance and reliability. They offer stable power supply, redundant networking capabilities and strict isolation from other tenants to protect and run smoothly – important features if storing sensitive information in the cloud.
2. NVIDIA A100
The NVIDIA A100 GPU is an exceptional choice for training, deploying, and serving AI apps. Ideal for AI builders and enterprises seeking maximum performance without breaking the bank, A100 instances are readily available in the cloud for easy scaling based on individual user needs – typically costing less than $10 an hour depending on provider and additional services provided.
The A100 can handle an array of artificial intelligence workloads thanks to its powerful processor and abundant memory capacity, making it suitable for AI training (for fine-tuning a massive language model) as well as HPC applications (like scientific simulations and molecular modeling). Furthermore, its lightning fast AI inference capability offers lightning-fast results when processing data or images while its Multi-Instance GPU (MIG) feature allows simultaneous processing multiple inference models simultaneously – ideal for use with deep learning models! Finally, support for both FP32 and INT8 precision makes the A100 even more suitable for deep learning models!
A100’s 7nm NVIDIA Maxwell architecture makes for another notable advantage, enabling more transistors to be placed closer together for improved performance and power efficiency. Furthermore, an NVLink interconnect is included to optimize memory and compute performance.
Performance wise, the A100 can outperform CPUs up to 249X in inference tasks – making it ideal for speeding up critical AI applications like echocardiography during the COVID-19 pandemic. Caption Health used an A100-powered system during this pandemic to perform echocardiograms on patients to diagnose and treat COVID as quickly as possible.
A100 instances are widely available through cloud providers, making scaling and managing AI deployments a simple task for developers and IT teams alike. This feature is particularly helpful for organizations with limited IT resources or those needing to make quick decisions regarding hardware purchases. Many of the top cloud providers provide A100 instances, providing easy access to powerful GPUs for AI projects without spending a great deal of time or money on purchasing their own GPU.
3. NVIDIA L40
NVIDIA L40 GPU servers are specifically tailored for 24-by-7 enterprise data center operations and offer power-efficient hardware with secure boot functionality with internal root of trust, as well as virtual workstation capabilities to enable complex visual computing and AI workloads. As they comply with NEBS Level 3, this choice makes an excellent addition to high-performance GPU server environments.
Accelerating AI workflows, the L40 GPU offers powerful training and inference performance at reduced completion time. With 48GB of memory capacity and support for industry standard AI frameworks, its seamless integration into existing AI applications ensures a swift learning curve and enhanced data processing performance.
The NVIDIA L40 helps increase productivity and scalability with its fast, high-quality frame rates that enable fast conversations and seamless human-machine interactions. Ideal for visual computing applications including image generative AI, rendering, video analytics, enhanced streaming throughput and reduced latency provide seamless human-machine interactions that support business.
NVIDIA Deep Learning Supersampling System 3 (DLSS 3) utilizes deep learning to accelerate image processing and render smoother frames per second (FPS), further augmenting L40 GPU’s performance – offering up to five times greater rendering performance compared to its predecessor generation.
Both the NVIDIA L40 and the L40S GPU offer excellent graphics-intensive performance for professional visualization, CAD/VFX production and virtualization tasks, but are designed for specific use cases; specifically the latter’s optimized support of AI training workloads requiring intensive AI processing power.
NVIDIA GPUs provide an invaluable foundation for advanced analytics and AI. The cost-effective NVIDIA L40 GPU offers enterprises looking to advance their AI initiatives without overspending on expensive high-end GPUs a cost-effective solution that meets these criteria. Ideal for graphics-intensive tasks like generative AI inference and moderate inference as well as data intensive work such as machine learning (ML), deep learning (DL), HPC computing and business intelligence (BI), it leverages Ada Lovelace architecture providing revolutionary neural graphics, virtualization compute and AI capabilities which make it ideal for GPU-accelerated data centers.
4. NVIDIA RTX
NVIDIA RTX is the world’s premier AI and graphics platform. It powers next-generation design, simulation, and AI workflows for millions of professionals worldwide.
NVIDIA RTX makes real-time ray tracing possible in games and professional applications with up to 6x faster performance than previous generations, providing gamers and professionals with unparalleled ray tracing realism for gaming, film production and virtual reality applications.
NVIDIA Turing GPU architecture and Tensor Cores, intelligent accelerators designed to speed up compute-intensive tasks like neural network training and inference, power the RTX platform and enable stunningly realistic graphics, as well as cutting-edge AI features like DLSS.
The latest NVIDIA RTX 50-series cards feature fifth-generation Tensor Cores to accelerate DLSS performance even further, and Multi Frame Generation allows GPUs to generate up to three extra frames for every traditionally rendered one, providing significant FPS increases without compromising visual quality.
DLSS may not be a panacea to all videogame issues; it still requires considerable processing power for it to work and many titles don’t take advantage of its potential. But if implemented by developers correctly and adopted by consumers, DLSS could make a dramatic difference to gaming experiences across the board.
NVIDIA RTX platform also provides tools for AI developers and data scientists. NVIDIA RTX Cloud offers access to advanced language, vision, speech and design models tailored for NVIDIA RTX GPU architecture – which can help streamline workflows and boost productivity. In addition, this cloud provides intelligent search & retrieval features to simplify data management.
