The top GPUs for large language models (LLMs) offer the perfect balance of performance, cost, and technical comfort. These cards can handle compute needs for local inference and AI development while avoiding cloud services fees that increase operational costs.
Selecting an optimal GPU for local AI deployment requires considering model size and precision as well as hardware capability. Our review reveals that capacity overshadows architectural improvements for most users.
NVIDIA H100
The NVIDIA H100 GPU is an industry-defining breakthrough that redefines AI computing performance and efficiency. Combining Hopper architecture with Transformer engine compute power, this GPU sets new standards in compute speed for AI training, data analytics, HPC and deep learning applications as well as providing precision formats to expand application support – perfect for enterprise use! The H100 is truly groundbreaking.
This powerful GPU is ideal for AI applications involving text, image and video generation as well as analysing large datasets to provide actionable insights to businesses. Thanks to its memory bandwidth and parallel processing capabilities, the A200 provides faster model training and inference compared to its predecessor, the A100.
NVIDIA claims the H100 provides three to four times faster training and inference of Large Language Models for machine learning applications, providing significant speed boost. Furthermore, its 3.35-3.9TB/s memory bandwidth enhances AI and HPC workload performance, and support for various precision formats allows it to take on complex tasks with ease.
H100 not only boasts exceptional performance but also provides developers with access to developer tools for optimizing and debugging applications, including NVIDIA Visual Profiler for performance analysis and optimization; as well as NVIDIA Nsight Systems which offers data analytics and monitoring tools to give a holistic view of application health and performance.
The H100 requires significant computing power for its operation, which may result in high thermal output that needs to be managed. To address this, advanced power management technologies have been included that optimize energy usage based on workload requirements; this cuts costs and ensures sustainable operations.
The NVIDIA H100 is an expensive investment that may suit organizations with demanding AI and HPC needs, but before making this commitment it’s essential to assess how it would benefit your business – renting through cloud providers may prove more cost-effective as they offer flexibility as well as no upfront capital expenses.
NVIDIA A100
The A100 GPU is designed for most AI tasks and provides optimal performance for most AI workloads. Its wide array of AI workloads – training, inference and mixed AI workloads – as well as its multi-tenancy features make it suitable for high-volume AI services. Equipped with 16896 CUDA cores and advanced 4th Generation Tensor Cores that make training 2-3x faster than previous generations of Tensor Cores as well as fast inference tasks more than 100x faster than CPUs are also supported along with support for INT8 or INT4 precisions to further accelerate execution speeds.
The A100’s NVLink support offers two times greater throughput than previous generations, making it ideal for cloud computing environments. With multiple GPUs connected within a server and scaling to thousands of Gigabytes per second, its use in HPC and machine learning applications becomes effortless; all major deep learning frameworks as well as over 700 HPC applications can benefit.
It can be used for tasks ranging from data analytics, computer vision and natural language processing to medical imaging, physics simulations and other scientific applications – helping scientists and engineers gain new insights while solving complex problems more easily.
A100’s lightweight design also makes it suitable for server use without producing excessive heat, making it an excellent option for small businesses or startups that don’t wish to invest in costly equipment.
As of 2020, the A100 remains readily available and costs less than its H100 counterpart, providing companies with an option to upgrade their GPUs without incurring the extra costs associated with purchasing one of Blackwell’s (H200 or H300 models.
While not ideal for linear logic modeling (LLM), A100 GPUs make an excellent choice for AI workloads due to their availability, lower cost, and increased throughput capabilities. Their availability, lower price point, and faster throughput make them an appealing option for many users.
NVIDIA L40
The NVIDIA L40 GPU is an exceptional enterprise solution, designed to withstand various enterprise workloads. Offering up to 48GB of GDDR6 memory and third-generation RT cores designed for accelerated ray tracing acceleration. Furthermore, this GPU offers support for graphics and media applications, making it perfect for data center environments requiring high-quality rendering and visualization services.
The NVIDIA L40 provides an economical solution for speeding up AI training and inference workloads. Its advanced architecture delivers higher compute performance while providing enterprise-class reliability, while its enhanced single-precision floating point (FP32) throughput and power efficiency help decrease time to completion and workload scalability. As an ideal GPU server environment choice – such as Hydra Host’s virtual GPU offerings – Hydra Host recommends it.
NVIDIA also offers the NVIDIA L40S, designed specifically to handle AI and parallel compute workloads. With improved fourth-generation Tensor cores providing increased processing power for AI applications – helping accelerate deep learning model training, image classification workflows and data science workflows; higher memory bandwidth ensures fast training/inference operations.
Both cards are designed for 24 x 7 enterprise data center operations and feature dual-slot, power-efficient designs with maximum power draw of 300 W and support for up to four DisplayPort 1.4 outputs. In addition, both come equipped with a full suite of security features – such as secure boot and internal root of trust – along with numerous I/O ports including PCIe Gen4 and NVLink connectivity options.
NVIDIA L40 leverages the Ada Lovelace GPU architecture for breakthrough performance for data center workloads. It features revolutionary neural graphics, virtualization, compute and AI features to deliver exceptional data center workload performance with its revolutionary neural graphics, virtualization, compute and AI features such as new generation RT cores and Tensor cores which offer over 1 Petaflop of inference performance.
The NVIDIA L40 provides 18432 shaders and supports DirectX 12 Ultimate. It boasts 568 texture mapping units and 192 ROPs as well as 142 Ray Tracing Acceleration Cores; additionally it comes equipped with a 384-bit memory interface and can be found in many NVIDIA-Certified systems from leading system builders.
NVIDIA RTX 5090
If you want the highest performance possible while gaming, the RTX 5090 may be just what you need. Boasting the highest number of CUDA cores released so far and capable of driving 4K 144Hz monitors, it also comes with AIB cards for even more. Unfortunately it’s costly at $2,000 MSRP, but if your computer can manage its power draw then this card could be one of the greatest graphics cards to purchase.
Nvidia has introduced its RTX 5090 graphics card as the inaugural model in their RTX 50-series graphics cards, built on Blackwell architecture and featuring 4th gen Ray Tracer cores for animation, visual effects and CAD work, while 5th Gen Tensor Cores deliver better AI inference performance when used with TensorFlow or PyTorch frameworks. This significant leap over its predecessor makes this an excellent solution to handle demanding AI and video workloads such as games as well as AI workloads such as AI inference frameworks like TensorFlow or PyTorch.
Gaming-wise, the RTX 5090 offers significant upgrades over its predecessor with 33% more CUDA cores and plenty of memory. It can drive a 4K 144Hz display at 60 frames per second or higher while supporting Nvidia’s DLSS 4 technology that generates up to three frames using AI instead of traditional rendering methods to increase frame rates – creating smoother, more detailed games; however it requires a strong base frame rate in order for this feature to work effectively – though it might not work in every game
Nvidia uses the DLSS 4 benchmarks to highlight its performance advantages, yet their impact varies according to game. Sometimes these features allow substantial FPS gains without visual concessions while in other cases increased framerates can introduce artifacts and latency issues. For an accurate evaluation of GPU performance it’s best run alongside high-end monitors with multiple resolutions and refresh rates like we did for our RTX 5090 review.
Keep in mind that the RTX 5090 features a delicate design. If you attempt to install a custom liquid cooling block, its 16-pin power connector could become damaged and voide its warranty; unfortunately, one unfortunate RTX 5090 owner shattered his card while using one; Nvidia offered to replace his damaged card free of charge.
