Nvidia GPUs accelerate the parallel calculations used to train AI models and generate their outputs. CUDA and libraries such as TensorRT connect software to the hardware; memory, interconnects, networking, and serving systems determine how well it performs at scale. Cloud providers package that larger system as GPU instances, managed platforms, or model endpoints, so customers can use accelerator capacity without operating the underlying data center.
What Nvidia GPUs do in AI
AI workloads repeatedly perform mathematical operations on large sets of data. A GPU can execute many operations concurrently, making it useful for the matrix-heavy computations common in modern machine learning. The GPU supplies computing capacity; it does not create a useful model or service by itself. Frameworks and libraries must direct work to it, and the surrounding system must supply data, memory, storage, and reliable access to the results.
The same GPU family may support both training and inference. The distinction is in the work being done and the system requirements, not a rule that each task needs a different kind of chip.
Training: adjusting model parameters
During training, a model processes data and repeatedly adjusts its parameters to improve its results. Training jobs can run for long periods and may distribute work across several GPUs or machines to increase throughput. That makes available GPU memory, communication between accelerators, and the ability to keep the job supplied with data important alongside raw compute capacity.
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Inference: producing a result
Inference is when a trained model processes an input and returns an output, such as a prediction or generated answer. A service handling inference must consider how quickly each request is answered, how many requests it can process, and how it behaves as demand changes. Batching, concurrency, model size, precision, and serving software all affect those outcomes.
How Nvidia’s software connects models to GPUs
CUDA is Nvidia’s programming foundation for using its GPUs. AI frameworks and libraries build on that foundation so application developers can use GPU operations without implementing every low-level instruction themselves. Performance still depends on the specific model, software, hardware configuration, and workload.
TensorRT and inference optimization
Nvidia describes TensorRT as an inference optimization and deployment tool. Its techniques include quantization, layer and tensor fusion, and kernel tuning. Quantization represents values at lower precision where suitable; fusion combines operations; and kernel tuning selects or adjusts GPU operations. These choices can change memory use and latency, but their effects depend on the model, precision, GPU, and evaluation method. An optimization that helps one workload is not a guarantee of the same result for another.
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Serving and orchestration
Serving software takes a model beyond a GPU process and makes it usable by applications. It can manage model execution, batching, concurrent requests, endpoints, and scaling. At larger deployments, orchestration software schedules workloads across machines and helps coordinate the GPU infrastructure, managed Kubernetes, AI platform, and model-serving layers. Nvidia’s cloud-partner inference architecture describes these as connected layers rather than a GPU-only service.
What a cloud GPU service includes
A cloud customer usually does not manage a bare physical GPU directly. The operator owns or rents servers, installs drivers and software, connects machines to storage and networks, and schedules customer jobs onto available capacity. Depending on the product, the customer may use a virtual machine, a Kubernetes cluster, a managed AI platform, or a hosted model endpoint.
This abstraction avoids the need for a customer to build and operate a data center, but it does not remove workload choices. Customers still need to consider which GPU configuration fits the model, how much memory it needs, where the data and compute should reside, how the system will scale, and what latency and operating cost are acceptable.
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Common ways to access capacity
| Access model | What the customer typically works with | What to evaluate |
|---|---|---|
| GPU instance | A virtual machine with allocated GPU capacity | GPU type and memory, region, storage and network setup, software support, scaling controls, and usage cost |
| Managed platform | A provider-managed environment for developing, training, or deploying models | Supported workflows, configuration options, regional availability, operational responsibilities, and cost for the expected workload |
| Model endpoint or serving platform | An interface for submitting requests to a deployed model | Latency, throughput, concurrency, reliability, scaling behavior, model and software support, and cost |
| Capacity marketplace | A way to discover or access GPU capacity offered by multiple providers | Actual GPU configuration, provider and region availability, service terms, networking, and how capacity is allocated |
Nvidia describes DGX Cloud as a co-engineered managed AI training platform offered with named cloud providers including AWS, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure. Its DGX Cloud Lepton offering is presented as a way to find GPU capacity across providers and work across regions. These descriptions do not establish that every configuration is available in every region or at every time; check current provider listings before planning a deployment.
Why large AI systems use multiple GPUs
For large models and demanding workloads, a single accelerator may not provide enough compute or memory. Systems can connect multiple GPUs within a server and link servers into clusters. The workload then has to be divided and coordinated, with data and intermediate results moving between accelerators. Interconnects and networking can therefore limit the useful work a cluster delivers even when its GPUs have substantial theoretical compute capacity.
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What Nvidia’s published examples and figures show
Nvidia’s product announcements and customer examples illustrate particular designs and deployments. They are useful for understanding what Nvidia and its customers report, but they are not universal performance guarantees or independent comparisons across vendors.
- GB300 NVL72 design: In its March 18, 2025 announcement, Nvidia described a rack-scale design connecting 72 Blackwell Ultra GPUs and 36 Grace CPUs. The same announcement said the design delivers 1.5 times more AI performance than GB200 NVL72. That comparison is Nvidia’s product claim; it should not be read as a result for every model or workload.
- Perplexity training example: Nvidia’s cloud page reports that Perplexity achieved up to 40% less model training time using Amazon SageMaker HyperPod accelerated by Nvidia GPUs. This is a vendor-reported customer result, not an independent benchmark.
- Perplexity inference example: Nvidia reports that Perplexity used Amazon EC2 P5 instances with Hopper GPUs and Nvidia software to serve 10,000 concurrent users and 100,000 queries per hour during spike periods. Those figures describe the reported deployment, not a general capacity promise.
- Writer model example: Nvidia says Writer used H100 and L4 GPUs on Google Kubernetes Engine with NeMo and TensorRT-LLM to train and deploy more than 17 large language models, up to 70 billion parameters. This is Nvidia’s account of a customer deployment.
- LiveX AI speed example: Nvidia reports a 6.1-times increase in average token speed for LiveX AI using Nvidia NIM on Google Kubernetes Engine with Nvidia GPUs. This is a vendor-reported result for that example, not a general expectation for NIM or GPU deployments.
These examples demonstrate that performance and capacity claims need their configuration and workload context. A useful comparison also needs the model, precision, batch size, target metric, and test conditions; the figures above do not provide a universal basis for ranking GPUs or providers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing local GPU hardware or cloud capacity
A workstation GPU can be useful for experimentation and local development, while a cloud cluster can offer access to more accelerators and managed infrastructure. They are not interchangeable: a consumer workstation card does not replicate a multi-node data-center cluster, and renting a cloud instance does not automatically make an inefficient workload fast.
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- Cost structure: Local hardware has an upfront purchase and ongoing maintenance; cloud capacity has usage and service costs that depend on how long and how much capacity is consumed.
- Memory and compute: Match GPU memory and capabilities to the model, precision, and workload rather than choosing by product name alone.
- Scaling: Consider whether work can benefit from multiple GPUs or nodes and whether the software can distribute it efficiently.
- Operations: Local systems require setup and maintenance; managed cloud products shift some of that work to the provider, but offer different levels of control.
- Data and location: Check region availability, data residency requirements, and the distance between the application, data, and GPU service.
- Service targets: For inference, evaluate actual latency, throughput, reliability, and cost under expected demand rather than relying on peak chip specifications.
There is no single “fastest GPU” answer that applies to every AI application. A defensible choice starts with a defined model and workload, then compares configurations against the metric that matters—such as training time, response latency, throughput, memory fit, or total cost.
What the hardware claim does—and does not—mean
Nvidia CEO Jensen Huang described Blackwell Ultra in Nvidia’s March 18, 2025 announcement as “a single versatile platform that can easily and efficiently do pretraining, post-training and reasoning AI inference.” This is Nvidia’s description of its announced platform. It expresses the intended versatility of that product design; it is not independent evidence that one configuration is optimal for every stage or workload.
More broadly, a GPU’s specifications indicate capabilities, not guaranteed application performance. The full outcome depends on the software stack, model implementation, memory needs, parallelization strategy, interconnects, networking, and deployment conditions. Cloud availability and configurations also change, so confirm the current offering and region with the provider when making a deployment decision.
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