Self-hosted GPU
An inference deployment model in which a team purchases or rents GPU instances itself and manages the inference framework, drivers, monitoring and disaster recovery. Its advantages lie in data residency, model customization and full control over low-level optimization.
Timeline (last 90 days)
Self-hosted GPUs require the team to procure or rent GPU instances themselves and manage inference frameworks (such as vLLM and TGI), drivers, monitoring, and disaster recovery.
When request volume is large enough, or when there are hard requirements for data residency, self-hosted GPUs may have advantages in long-term cost and controllability.
All Facts (2)
Self-hosted GPUs require the team to procure or rent GPU instances themselves and manage inference frameworks (such as vLLM and TGI), drivers, monitoring, and disaster recovery.
50%UnverifiedWhen request volume is large enough, or when there are hard requirements for data residency, self-hosted GPUs may have advantages in long-term cost and controllability.
50%