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Use case · AI inference and accelerated compute

Run accelerated workloads without purchasing and housing dedicated hardware.

Cloud GPU for flexible deployment; Bare Metal GPU for intensive isolated work.

Compare suitable servers

When is a GPU server useful?

A GPU server is useful when inference, rendering, or parallel compute is materially faster on supported accelerators. Choose by model memory, throughput, software compatibility, and region—not by GPU name alone—and confirm that the workload can use the selected accelerator.

What the server can support

  • Serve supported inference workloads
  • Run rendering or parallel compute jobs
  • Choose an accelerator and location together

Decide before deployment

  • Required GPU memory
  • Framework and driver compatibility
  • Utilization and data-transfer pattern

Representative architecture

Keep each dependency visible.

This sequence is a planning model, not a prescribed topology. Change it when the application’s measured request and data paths require a different boundary.

  1. 01Request or job queue
  2. 02Model service
  3. 03GPU memory and compute
  4. 04Result and telemetry path
UsersRequest
Latency-sensitive
ApplicationCompute
Frequent round trips
DatabaseData
Independent copy
RecoveryRestore
Place the most conversational systems close together. Keep a recovery copy reachable through an independent access path.

Selection criteria

  • Fit model weights and runtime memory on the accelerator
  • Benchmark the exact framework, precision, and batch shape
  • Account for data loading and CPU preprocessing

Failure modes to test

  • A model can fit storage but exceed GPU memory
  • Low utilization can make acceleration uneconomic
  • Driver, runtime, and framework mismatches can prevent execution

Catalogue connection unavailable

No configuration is being guessed.

Current active configurations could not be verified. Use the decision criteria on this page, then retry the catalogue when the connection recovers.

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Recovery implication

Decide what must return before choosing how to protect it.

Keep model artifacts, configuration, container or environment definitions, and output data independent of the server. Prove the workload can restart on a replacement configuration.

01 · DefineRequired data

Application, configuration, credentials, and database.

02 · ProtectIndependent copies

Server-level and application-aware recovery inputs.

03 · RestoreIsolated target

Recover without overwriting the source by default.

04 · VerifyWorking service

Check data, dependencies, access, and application behavior.

A completed backup job proves that a copy was written. A recovery drill proves that the service can return.

Pre-deployment checklist

  1. Verify software and driver compatibility
  2. Measure memory at realistic batch size
  3. Track utilization and queue time
  4. Test rebuild from documented artifacts

Choose from evidence, then observe

The suggested family is a starting point, not a guarantee of application performance. Test the software with representative traffic, monitor the constrained resources, and keep application-aware backups. Vloxt exposes eligible server controls and current catalogue choices; the application architecture remains yours.

Read the complete server-selection guide or explore platform features.