together-dedicated-endpoints

Deploy fine-tuned models to dedicated GPU endpoints by selecting your custom model, specifying explicit hardware configurations, creating the endpoint, and waiting for readiness status before invoking inference. This provides predictable performance and SLA-backed reliability without shared-resource contention.

Dedicated endpoints provide single-tenant GPU hardware with no shared-resource contention, enabling predictable latency and service-level agreements. Shared API inference lacks dedicated hardware, making performance variable and unsuitable for production workloads requiring autoscaling and explicit hardware configurations.

Yes, Together AI supports uploading custom models from Hugging Face or S3 and deploying them to dedicated endpoints. You can manage the full endpoint lifecycle, including hardware selection, autoscaling configuration, and start, stop, and delete controls across multiple modalities.

Configure autoscaling and hardware options during endpoint creation by enumerating available GPU hardware configurations. This ensures your dedicated endpoints scale dynamically with traffic while maintaining predictable latency and SLA-backed reliability for production-grade inference workloads.

Yes, the Together API supports dedicated endpoints across 179+ models spanning chat, image, audio, embedding, and moderation categories. You can deploy these models with custom hardware configurations, autoscaling, and full lifecycle management for stable, predictable inference.

Avoid dedicated endpoints for low-volume or sporadic inference workloads where predictable latency and service-level agreements are unnecessary. Dedicated single-tenant GPU hardware is designed for production-grade traffic requiring autoscaling, explicit hardware configurations, and SLA-backed reliability.