Blackwell delivers up to 10x inference cost cuts — but software and precision formats drive the gains

Nvidia published production results indicating that combining its Blackwell accelerator family with carefully optimized inference stacks and modern open-source models can reduce per-token inference costs substantially, typically between fourfold and tenfold. The company’s review of four customer deployments shows that raw hardware improvements often account for about a twofold gain, while precision-format changes and software co-design supply the remaining savings. One healthcare deployment reported roughly a 90 percent cost cut alongside major latency improvements after moving from a proprietary model and legacy stack to an open-source model on a Blackwell-powered platform. A gaming provider reduced cost per million tokens from around $0.20 on older hardware to $0.10 on Blackwell, then to about $0.05 after adopting a lower-precision numeric format. Other examples include a multi-agent chat workload that achieved 25–50 percent better efficiency and a voice support system that cut cost per query by about six times while keeping response times under 400 milliseconds. Three technical variables explain the spread in outcomes: the numeric precision used for inference, the chosen model architecture (mixture-of-experts versus dense models), and how tightly the software stack is tuned to the hardware. Mixture-of-experts topologies reap outsized benefits from fast NVLink-like fabrics because only a subset of parameters activate per request, raising effective throughput when paired with low-bit formats. Dense architectures, which touch most parameters on every invocation, see smaller relative improvements unless similar software and precision tactics are applied. The practical implication is that high-volume, latency-sensitive applications that generate many tokens per query will capture the largest portion of potential savings, while low-volume or high-latency-tolerant use cases should prioritize software and model optimization first. The recommended evaluation approach is staged: test precision and stack changes on existing infrastructure, measure throughput and per-token economics on production traffic, then decide on full hardware migration. Finally, the analysis reminds teams there are alternative accelerators and cloud-managed inference services; the right choice balances per-token pricing with operational complexity and vendor management costs.