NVIDIA Dynamo Overview
NVIDIA Dynamo (NVIDIA Dynamo 0.2) by NVIDIA provides GPU-accelerated inference with features: Disaggregated prefill/decode, KV cache offloading, intelligent routing, GPU memory tiering, agentic inference... It achieves 90-96% GPU utilization, optimized for long-context agentic workloads throughput on H100 GPUs. License: Open-source preview, NVIDIA.
Performance Benchmarks
On H100 80GB with Llama 4 Scout (17B) at FP8: prefill throughput: 8,305 tokens/second; decode throughput: 2677 tokens/second per user with 1784 max batch; TTFT (time to first token): 44ms; inter-token latency: 8ms. GPU utilization: 90-96% GPU utilization.
Feature Comparison
Key features: Disaggregated prefill/decode, KV cache offloading, intelligent routing, GPU memory tiering, agentic inference... Unique strengths: Disaggregated prefill/decode KV cache offloading intelligent routing. Production features include: health checks, metrics endpoints, graceful shutdown.
Cost-Per-Token Analysis
Cost-per-token on H100 80GB at $2.50/hr: input tokens: $0.000048/1K tokens; output tokens: $0.000167/1K tokens with NVIDIA Dynamo. At 50% utilization, cost-per-million tokens: $53-$228 for output tokens, depending on batch size and model size. Reserved pricing reduces costs by 30-50%.
Production Deployment
Deploy NVIDIA Dynamo in production: containerized deployment with Docker + NVIDIA Container Toolkit; Kubernetes with GPU node pools; monitoring with Prometheus + GPU metrics; horizontal scaling with Istio service mesh; and CI/CD integration for model updates. Recommended: 4x H100/B200 GPUs per node with NVLink.
When to Choose NVIDIA Dynamo
Choose NVIDIA Dynamo when: Disaggregated prefill/decode KV cache offloading are critical for your workloads; Open-source preview, NVIDIA license model fits your budget; and your team has experience with NVIDIA's ecosystem. Consider alternatives when: specific hardware optimization is needed, team familiarity with other frameworks, or license costs are prohibitive for your scale.