Blackwell · DeepSeek · Nvidia · AI Inference · Cursor · NVIDIA Blog
How NVIDIA’s Inference Software Stack Powers the Lowest Token Cost
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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.
Key facts
- 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
- Explore how software multiplies hardware performance in this NVIDIA AI Podcast on tokenomics and this inference solutions page
- Leading companies and inference providers are already seeing the compounding value of NVIDIA’s inference software stack on Blackwell
- Disaggregated serving, large expert parallelism over NVIDIA NVLink interconnect technology, NVFP4 precision and multi-token prediction each deliver meaningful gains on their own
Summary
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. Leading companies and inference providers are already seeing the compounding value of NVIDIA’s inference software stack on Blackwell:. Baseten used the NVIDIA TensorRT-LLM open source library to serve DeepSeek V4 Pro on Blackwell GPUs for reasoning, coding and long-context workloads, applying proprietary runtime optimizations to deliver up to 50% more tokens per second. Cognition is using the NVIDIA Dynamo inference framework to manage inference GPUs, giving its team a ready-made path to scale reinforcement learning workloads without needing to build that infrastructure from scratch.