NVIDIA Data Center GPUs Explained: From A100 to B200 and Beyond

For AI teams that want to self-host Generative AI (GenAI) models like LLMs, one of the most important choices is which GPU to use.

In the GPU industry, NVIDIA has established itself as the undisputed leader, especially for AI workloads. It offers a wide range of GPUs that can run AI workloads of different sizes. With so many options, it can be difficult to compare performance and cost, and making the wrong choice can be costly.

This is particularly true for workloads like on-prem LLM deployments. Once you invest in the hardware, it’s hard to undo the decision. Nobody wants to sink money into GPUs that don’t deliver the expected value.

In this post, we’ll break down NVIDIA’s GPU product lines, focusing on data center GPUs for AI inference. We’ll compare the most common ones and help you map the lineup to your model inference needs.

What are NVIDIA data center GPUs#

When people talk about GPUs for AI inference, they usually refer to NVIDIA data center GPUs. These cards are the backbone of modern AI infrastructure and you’ll often find them in cloud servers and enterprise data centers. They are optimized for large-scale AI workloads and can be shared across teams or scaled to massive clusters.

NVIDIA updates its data center lineup every one to three years. Each new generation improves memory, throughput, and efficiency. Well-known examples include the T4, L4, A100, H100, H200, and the new B200. These GPUs dominate AI inference benchmarks and show up on almost every GPU comparison chart for LLMs and GenAI use cases.

Many people confuse data center GPUs with other NVIDIA products:

• GeForce (Consumer/Gaming). Graphic cards like the RTX 5090 or 4090 are built for gaming and general use. They perform well, and some users do run GenAI workloads on them. However, they aren’t made for enterprise-scale AI deployment.
• RTX (Professional). Formerly branded as Quadro, these GPUs target creative professionals in fields like visualization, architecture, and 3D design. They’re built for stability and certified software support, but they’re not aimed at large-scale AI training or inference.

In short, GeForce is for gamers, RTX is for professional creators, and data center GPUs are for AI and large-scale computing. If your goal is to train or serve GenAI models, data center GPUs are the right choice.

GPU architectures: Turing, Ampere, Hopper, Lovelace, Blackwell#

Each time NVIDIA launches a new GPU generation, it brings a new architecture that improves performance, efficiency, and support for AI workloads. The latest Blackwell architecture packs 208 billion transistors, built on TSMC’s custom 4NP process. Blackwell GPUs feature two reticle-limited dies connected by a 10 TB/s chip-to-chip interconnect, allowing them to operate as a unified single GPU.

A quick way to identify NVIDIA data center GPUs is through their names. The naming convention follows a simple pattern that reveals key information about the chip:

• First letter: Indicates the architecture generation. NVIDIA names each generation after a well-known scientist or mathematician, like Ada Lovelace and Grace Hopper.

  • Turing (T4)
  • Ampere (A10, A100)
  • Hopper (H100, H200)
  • Lovelace (L4)
  • Blackwell (B200)

• Numbers: The number following the letter usually indicates the model’s relative position in the lineup. Within the same generation, higher numbers generally mean more powerful or feature-rich GPUs.

• Memory suffix: Some models include memory capacity directly in the name, such as the A100 80GB, which helps distinguish variants.

Best NVIDIA GPUs for AI inference: A100, H100, H200, B200 compared#

Not all NVIDIA GPUs are created for the same purpose. Within each architecture, NVIDIA offers several models aimed at different price, performance, and power targets. In general, higher-numbered models provide more compute power, memory, and features, but they also come at a higher cost.

Here are some of the most common NVIDIA data center GPUs used for AI today:

GPU	Architecture	Memory	Best For
T4	Turing	16 GB	Entry-level inference
L4	Lovelace	24 GB	Energy-efficient inference
A10	Ampere	24 GB	Mid-range inference, AI training
A100	Ampere	40 & 80 GB	High-performance LLM training & inference, HPC
H100	Hopper	80 GB	Advanced LLM training & inference, FP8
H200	Hopper	141 GB	Ultra-large models, long-context inference
B100	Blackwell	192 GB	Next-gen AI training, inference, HPC
B200	Blackwell	192 GB	Frontier-scale AI, multi-trillion parameter models

GPU benchmarks and comparisons#

When choosing between GPUs, benchmark data is one of the most useful points of reference. For LLM inference, the most common metrics are throughput (e.g., Tokens Per Second) and latency (e.g., Time to First Token). NVIDIA publishes general performance numbers for each generation, but they don’t always reflect the nuances of real-world GenAI workloads.

That’s why independent GPU comparisons can be especially useful. Benchmarking platforms and inference frameworks (e.g., vLLM and SGLang) often measure GPUs against specific LLMs or inference scenarios. These results can help you see how different compute accelerators perform under practical conditions.

GPU price considerations (T4 vs A100 vs H100)#

Performance is only part of the equation, and pricing is another key factor. GPU costs can vary widely depending on the provider and purchase model. Many vendors offer on-demand, 1-year, and 3-year committed plans, with large discounts for longer commitments.

For example, here’s how the cost of a T4 vs. A100 vs. H100 compares on Google Cloud (us-central1):

GPU	On-Demand	1-Year Commitment	3-Year Commitment
NVIDIA T4 (16 GB)	$255.50/mo	$160.60/mo	$116.80/mo
NVIDIA A100 (40 GB) — 1× A100 in a2-highgpu-1g VM	$2,681.57/mo	$1,689.37/mo	$938.57/mo
NVIDIA A100 (80 GB) — 1× A100 in a2-ultragpu-1g VM	$3,700.22/mo	N/A	N/A
NVIDIA H100 (80 GB) — 8× H100s in a3-highgpu-8g VM	$64,597.70/mo (≈$8,074.71 per GPU)	$44,810.08/mo (≈$5,601.26 per GPU)	$28,371.00/mo (≈$3,546.38 per GPU)

 

Note: Pricing was collected on August 27, 2025, and may change. For the latest details, check Google Cloud's pricing calculator.

These numbers make the contrast clear. An entry-level NVIDIA T4 GPU costs just a few hundred dollars per month; in contrast, a high-end H100 cluster can exceed $60,000/month. Ultimately, choosing the right GPU is about balancing performance requirements and cost efficiency, especially for long-term AI deployments.

Why GPU memory matters#

When running LLM inference, GPU memory (VRAM) often matters more than raw compute power. One of the biggest reasons is the KV cache. It stores attention keys and values for every token in the input sequence. This way, the model doesn’t need to recalculate them at each decoding step. The result is faster inference, but at the cost of high memory usage.

The KV cache size increases linearly with sequence length. This means that long-context scenarios can quickly use up available GPU memory, turning VRAM into the main bottleneck. The amount of GPU memory you have directly determines the size of the models and context windows you can support. For example:

• H200 (141 GB): Large enough to handle extended context windows without offloading the KV cache to external memory systems.
• T4 and A10 (16–24 GB): Ideal for smaller inference jobs, but they can struggle when sequences get longer.

Because of these constraints, the AI community is actively exploring ways to get around memory limits, such as prefill-decode disaggregation, KV cache offloading, and memory-efficient attention techniques.

In short, if your workloads involve long prompts or large batch inference, GPU memory capacity is often just as important as raw compute power when choosing a graphic card.

AMD GPU alternatives#

While NVIDIA currently dominates the AI GPU market, it’s not the only option. AMD’s MI-series accelerators have made steady progress and now offer competitive performance for certain tasks, particularly in AI training and inference. The challenge for AMD is less about hardware capability and more about software support. The NVIDIA CUDA ecosystem has become the default standard for AI development, with broad framework integration and community adoption.

We’ll cover the MI-series GPUs in more detail in a separate blog post.

Conclusion#

NVIDIA data center GPUs cover a wide spectrum, from entry-level inference on the T4 to frontier-scale inference on the B200. We hope this post gives you a clearer view of the product line and the key considerations when choosing GPUs.

At Bento, we offer a wide range of GPU options across regions and cloud providers for AI inference. Our inference platform automatically matches your workloads with the best available GPUs at the most competitive rates, and scales resources up or down as your traffic changes.

Learn more:

• Read our LLM Inference Handbook to learn practical strategies for faster, cheaper inference.
• Join our Slack community and connect with other AI teams working on GPU inference.
• Schedule a call with our experts to discuss the right GPU strategy for your workloads.