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Nvidia GPUs power AI by carrying out many model computations in parallel. CUDA and libraries such as TensorRT connect AI software to that hardware; servers, networking, storage, and orchestration let providers combine GPUs into cloud capacity. Training uses that capacity to adjust a model’s parameters, while inference uses a trained model to produce outputs. The GPU is the compute engine, but software and the surrounding system determine how effectively it serves a workload.
Why AI workloads use GPUs
Training and running many AI models involve large amounts of mathematical work that can be divided into operations and processed concurrently. GPUs provide parallel computing resources suited to this kind of work. Their hardware generation and configuration affect supported capabilities and potential performance, but a chip’s specifications alone do not establish how fast a particular model will run.
A useful way to understand the system is as a chain: GPU hardware performs computations; programming tools and libraries make that hardware available to model software; optimization can improve execution; and serving and cloud infrastructure deliver results to users.
What GPUs do during training and inference
| Workload | What the model is doing | What the system needs to do well |
|---|---|---|
| Training | Repeated computation adjusts the model’s parameters using data. | Long-running jobs often need high throughput and may be distributed across multiple accelerators. |
| Inference | A trained model processes an input and produces an output, such as a prediction or generated answer. | Serving systems must balance latency, throughput, concurrency, reliability, and cost. |
Training and inference have different workload demands, but that does not mean they always require different GPU families. Hardware choice depends on the model, the workload, and the performance target.
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How Nvidia’s software connects models to GPUs
CUDA and libraries
CUDA is NVIDIA’s programming foundation for GPU computing. Libraries and frameworks provide reusable operations so application developers do not have to implement every low-level GPU operation themselves. This software layer helps model code make use of the hardware.
TensorRT optimization
NVIDIA describes TensorRT as an inference optimization tool that uses techniques including quantization, layer and tensor fusion, and kernel tuning. Quantization uses lower-precision representations where suitable. Such techniques can affect latency and memory requirements, but results depend on the model, chosen precision, GPU, and evaluation method; an optimization is not a universal speed guarantee.
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Serving and orchestration
Inference serving software manages model execution, batching, concurrency, endpoints, and scaling. In NVIDIA’s cloud-partner reference architecture, those capabilities sit above GPU infrastructure and managed Kubernetes, alongside AI-platform and model-serving layers. In practice, software at these layers helps turn GPU computation into a service that an application can call.
How cloud providers turn GPUs into a service
A cloud operator owns or rents physical servers, installs GPU drivers and software, connects machines to storage and networking, and schedules workloads onto available capacity. A customer may use a virtual machine, Kubernetes cluster, model endpoint, or managed AI platform without directly managing the physical GPU. That abstraction avoids the need to own a data center, but it does not eliminate decisions about capacity, region, workload performance, data location, or operating cost.
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Cloud access generally takes one of three forms. The right choice depends on how much infrastructure the team wants to manage and how much control it needs.
| Access method | What the customer works with | Main consideration |
|---|---|---|
| GPU instance | A virtual machine with access to GPU capacity. | The customer has more direct responsibility for software setup and workload operations. |
| Managed platform | A provider-managed AI environment or service. | It can reduce infrastructure work, while available configurations and controls depend on the provider. |
| Marketplace or multi-provider capacity service | Listings or discovery and allocation tools for capacity from providers. | Availability, region, configuration, and service terms must be checked for the specific provider and workload. |
NVIDIA describes DGX Cloud as a co-engineered, managed AI training platform, with offerings named for Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure. NVIDIA presents DGX Cloud Lepton as a way to discover GPU capacity from multiple providers and work across regions. These descriptions do not establish that a particular GPU configuration is currently available in every region; check the provider’s current listing.
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Why large AI workloads use multi-GPU systems
A large job may need more compute capacity than one GPU can provide. Operators can connect accelerators into multi-GPU systems and clusters, but adding chips is only part of the work: interconnects, networking, storage, scheduling, serving software, and operational reliability also affect whether the system can keep the workload supplied and deliver results effectively.
NVIDIA’s March 18, 2025 announcement described the GB300 NVL72 as a rack-scale design connecting 72 Blackwell Ultra GPUs and 36 Grace CPUs. NVIDIA also said the GB300 NVL72 provides 1.5x more AI performance than the GB200 NVL72. The first number describes the announced design; the performance figure is NVIDIA’s product comparison, not a guarantee for every model or workload. Neither establishes that every cloud provider offers that rack.
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What reported customer examples do—and do not—show
NVIDIA’s cloud materials report several customer examples. They illustrate deployments and vendor-reported outcomes, not independent benchmarks or promises of general capacity:
- NVIDIA says Perplexity achieved up to 40% less model training time using Amazon SageMaker HyperPod accelerated by NVIDIA GPUs. This is NVIDIA’s customer example, not an independently established result for other workloads.
- NVIDIA attributes 10,000 concurrent users and 100,000 queries per hour during spike periods to Perplexity’s inference deployment on Amazon EC2 P5 instances using Hopper GPUs and NVIDIA software. Those figures describe the reported deployment, not a general service-capacity guarantee.
- NVIDIA says Writer used H100 and L4 GPUs on Google Kubernetes Engine with NeMo and TensorRT-LLM to train and deploy 17+ large language models, up to 70 billion parameters. These are details of NVIDIA’s reported customer example.
- NVIDIA reports a 6.1x 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.
As NVIDIA founder and CEO Jensen Huang put it in the company’s March 18, 2025 Blackwell Ultra announcement: “We designed Blackwell Ultra for this moment — it’s 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, not an independent assessment.
How to decide between local and cloud GPU capacity
A workstation GPU can support local experimentation, but it is not equivalent to a multi-node data-center or cloud cluster. Compare the trade-offs against the workload rather than choosing a GPU based on a general “fastest” claim.
Quick Recap
- Upfront cost versus usage cost: Compare buying hardware with paying for cloud capacity over the expected workload and period. No universal cost winner follows from the hardware alone.
- Memory and compute: Check whether the available GPU configuration can run the model and workload you intend to use.
- Scale and operations: Consider whether you need one workstation, multiple GPUs, or the ability to scale across nodes, and who will maintain the software and infrastructure.
- Data and location: Check region, data residency needs, storage and network setup, and the latency between the service and its users or data.
- Service requirements: Define the needed latency, throughput, reliability, and scaling controls before comparing cloud offerings.
- Workload-specific performance: Compare results for the model, batch size, precision, hardware, and target metric that matter to your application. A single GPU ranking cannot settle every use case.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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