Elastic Deployment
Strategy for dynamically adjusting model capacity, compute intensity, or resource allocation during inference to optimize for latency, throughput, or cost without requiring full redeployment. Enables runtime trade-offs between accuracy and efficiency.
Mechanisms
- Multi-Weight Models: Single artifact containing multiple parameter configurations or quantization levels.
- Adaptive Routing: Request-level selection of model variants based on complexity or SLA requirements.
- Hierarchical Structures: Nested model representations allowing seamless scaling of active parameters.
Case Studies
- NVIDIA Nemotron Elastic:
- NVIDIA Nemotron Elastic: Bundling Three LLMs for Flexible Deployment
- Nemotron-3 Nano V3 Elastic: Bundles three distinct model sizes (30B, 23B, 12B parameters) into a single file.
- Russian Doll Architecture: Implements nested structure for flexible capacity selection.
- Operational Benefits: Supports dynamic switching between model sizes to match hardware constraints or latency targets per inference request.
Related
- model-quantization
- speculative-decoding
- Serverless Inference
- NVIDIA Nemotron