As organizations move from AI pilots to production AI factories, infrastructure decisions have shifted from peak chip specifications to cost per token: how many useful tokens they can deliver per dollar, per watt and within required latency targets.

Codesigned with NVIDIA GPUs, CPUs, networking and systems, and strengthened by a broad open source ecosystem, NVIDIA’s full-stack inference software continuously improves hardware performance. On the NVIDIA Blackwell platform, the software stack has already reduced token costs by up to 5x on the DeepSeek V4 model in just one month.

Leading companies and inference providers are already seeing the compounding value of NVIDIA’s inference software stack on Blackwell:

Traditional web, search and software-as-a-service workloads were relatively predictable: A user might load a page, refresh a feed or update a business record. These requests typically followed similar software paths, reading from or writing to a database, and scaled by adding more of the same servers.

Agents can reason, plan, call tools, spin up specialist subagents and manage massive context across multi-turn workflows. They turn a single request into a distributed computing problem that can span hundreds of subagents, thousands of tasks and multiple large language models, running across GPUs, CPUs, DPUs and storage systems.

The software stack determines whether that complexity turns into wasted capacity or lower cost per token.