Tools
Configuring Dedicated Model Inference
The Together AI platform’s dedicated inference architecture consists of three immutable entities: **configs** (engine, GPU type/count, parallelism and optimization profile), **deployments** (a specifi
The Together AI platform’s dedicated inference architecture consists of three immutable entities: configs (engine, GPU type/count, parallelism and optimization profile), deployments (a specific model revision bound to a config with autoscaling and replicas) and an endpoint (a stable qualified name). Traffic is distributed across deployments via a capacity‑aware weight split, enabling zero‑downtime updates, rollouts, A/B tests, shadow experiments, and other advanced operations simply by adding/removing deployments and adjusting their routing weights. IDs prefixed with elements such as proj_, ml_, cr_, endpoint_, dep_, and rol_ make logs and scripts self‑documenting.
Related
- Serving MiniMax-M3 for efficient inference: Unlocking 1M-Token Context and Multimodality Without Regrets
- Run it on the AI Native Cloud — serverless and dedicated infrastructure. https://www.together.ai/models/minimax-m2-7
- Native-speed vLLM transformers modeling backend
- MiniMax M3 is live and Together AI is powering its inference 🚀 Tomorrow at 6pm PT we're going live on X Spaces with the teams behind the mo…
Source: Together AI Blog | 2026-07-29