Single-tenant GPU endpoints on Together AI with autoscaling and no rate limits. Deploy fine-tuned or uploaded models, size hardware, and manage endpoint lifecycle. Reach for it whenever the user needs predictable always-on hosting rather than serverless inference, custom containers, or raw clusters.
Instalación
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Single-tenant GPU endpoints on Together AI with autoscaling and no rate limits. Deploy fine-tuned or uploaded models, size hardware, and manage endpoint lifecycle. Reach for it whenever the user needs predictable always-on hosting rather than serverless inference, custom containers, or raw clusters.
Together Dedicated Endpoints
Overview
Use dedicated endpoints for managed single-tenant model hosting with predictable performance and
no shared serverless pool.
Typical fits:
production inference with stable latency
fine-tuned model hosting
uploaded custom model hosting
autoscaled model APIs
When This Skill Wins
The user needs always-on or single-tenant hosting
The model is supported for dedicated deployment
Fine-tuned or uploaded models must be served as endpoints
Hardware, scaling, or idle-time settings need explicit control
Hand Off To Another Skill
Use together-chat-completions for serverless chat inference
Use together-dedicated-containers for custom runtimes or nonstandard inference pipelines
Use together-gpu-clusters for raw infrastructure or cluster orchestration
For production stock-model workloads that need a defined SLA (committed throughput and reliability) without managing hardware, point users to Together's provisioned throughput tier (reserved PTU capacity, one-month minimum, contact sales). Use dedicated endpoints instead when the user needs to serve a fine-tuned or uploaded model, or wants direct control over hardware, latency, and throughput.
Confirm that the task needs dedicated hosting instead of serverless or containers.
Verify model eligibility and inspect available hardware.
Create the endpoint with explicit scaling and timeout settings.
Wait for readiness before sending inference traffic.
Stop or delete the endpoint when the workload no longer needs to run.
High-Signal Rules
Python scripts require the Together v2 SDK (together>=2.0.0). If the user is on an older version, they must upgrade first: uv pip install --upgrade "together>=2.0.0".
Model eligibility and hardware availability are gating constraints; check them early.
Endpoint management uses endpoint IDs, while inference usually uses the endpoint name as model.
Autoscaling, auto-shutdown, prompt caching, and speculative decoding materially affect operations and cost.
For custom or fine-tuned models, do not skip the intermediate verification steps before deployment.
