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GPU series serve different tasks. Select a specific VM size for the model and include disks, networking and software requirements in the budget.
Azure uses GPUs for several scenarios, including compute and graphical applications. Searching for GPU is therefore only the beginning. Establish which series, configuration and software environment match the chosen language-model engine.
Current to 1 November 2026, this is an editorial selection guide using official pages, not an independent speed comparison against other providers.
Inspect the VM series
The NC-family description concerns GPU VMs for intensive compute. Select a particular series and size within it. Record device count, available VRAM, CPU, RAM and environment characteristics.
Do not transfer graphical-workstation properties automatically to a compute server. Producing a desktop image and running a specific LLM engine are different checks. GPU availability in one Azure product likewise says nothing about another AI service’s terms.
Clarify resource access
Before the test window, check region, subscription restrictions and the creation route. Include any prior capacity approval in the plan. A catalogue does not determine a project’s start date.
For a team already using Azure, consider existing identity, network and data-placement rules. They can simplify organisation without establishing a universal price advantage. Assess external-data migration and expense separately.
Read the complete VM budget
Linux Virtual Machines pricing shows that cost depends on more than basic compute. Clarify image, licences or support, disks, networking and duration for the chosen offer.
Compare purchasing options by commitment and interruption tolerance. An uncertain pilot and continuous workload may justify different choices. Tie every expected discount to a specific product and conditions rather than the cloud’s general name.
Test an internal application migration
Suppose a company adds its own model to an existing Azure application. It selects one VM, isolates a test environment and checks the network path to the application. Then it measures quality, latency and ordinary-restart recovery.
Separate model time from end-to-end overhead. If users wait because of queueing or networking, replacing the GPU may not fix the problem. Establish that before reserving more resources.
Apply findings to owned hardware
A cloud test clarifies requirements and operating effort. It does not establish that an owned card earns what a customer pays for a VM. Cloud products and equipment supplied to an operator contain different services.
Read about inference bottlenecks and the AWS, Google Cloud and Azure comparison.