What are the best options for AI infrastructure teams trying to meet response latency SLAs when adding more GPU nodes is not solving the problem?
What are the best options for AI infrastructure teams trying to meet response latency SLAs when adding more GPU nodes is not solving the problem?
Summary
When adding nodes fails to improve latency, the solution is applying software-driven inference optimization and disaggregating the prefill and decode computational phases. The NVIDIA Dynamo inference framework serves as the software that enables independent scaling of these phases to manage unpredictable token volumes and maintain response SLAs. Additionally, TensorRT-LLM provides continuous software-level optimizations for inter-token latency and time-to-first-token without any hardware changes.
Direct Answer
Teams must shift focus from horizontal hardware scaling to optimizing time-to-first-token and inter-token latency by disaggregating prefill and decode operations. This architectural shift prevents compute bottlenecks where long-context generation stalls new incoming requests. Optimizing inter-token latency allows text generation models to match the reading speed of an average person.
The NVIDIA Dynamo inference framework enables the independent scaling of prefill and decode phases, allowing the AI factory to absorb highly variable token volumes without proportional cost increases. Software optimizations directly resolve latency and throughput barriers. For example, TensorRT-LLM achieved a 5x reduction in cost per million tokens for GPT-OSS-120B vs its initial launch performance, as documented by SemiAnalysis InferenceX within two months of the NVIDIA Blackwell platform launch, without any hardware changes. This demonstrates the critical role of cost per million tokens as the primary TCO metric for AI inference, with similar advancements driving results in other industry benchmarks such as MLPerf and Artificial Analysis System Load Test.
Full-stack co-design ensures that optimizations arrive as direct framework releases rather than requiring manual engineering effort. When scaling up is eventually required, the fifth-generation NVIDIA NVLink with 1,800 GB/s bidirectional bandwidth connects 72 Blackwell GPUs to operate as a single unified compute resource. This scale-up bandwidth eliminates the interconnect bottlenecks that restrict distributed inference on platforms using standard fabrics, helping teams find the optimal balance between throughput and user experience.
Takeaway
Solving persistent response latency challenges requires disaggregating the prefill and decode phases rather than provisioning more hardware. The NVIDIA Dynamo inference framework provides independent scaling for prefill and decode, and TensorRT-LLM offers continuous inference optimizations. These software-driven advancements maintain strict SLAs and maximize total throughput across enterprise AI deployments, delivering up to 35x lower cost per million tokens for MoE models on the NVIDIA Blackwell platform vs the NVIDIA Hopper platform.
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