Nvidia's Strategic Move: Boosting AI Inference Efficiency Through Investment and Software

MS participates in attracting $14 million investment in Simply Smart, improving efficiency amid supply shortage, leading to sales

NVIDIA invested in Simpli Smart, a software company that lowers AI model operating costs. Inference efficiency can reduce the computational resources required for the same task. The impact of efficiency improvements on GPU demand depends on the amount of AI service usage and the availability of existing facilities.

Simply Smart announced on the 27th that Nvidia and others participated in a $14 million Series B investment led by Dallas Venture Capital. The company optimizes task distribution and memory usage during the inference process in which the AI ​​model generates answers or results. NVIDIA's individual investment amount and shareholding ratio were not disclosed.

The application case presented by the company is InVideo, an AI video production service. Simpli Smart announced that it reduced service provision and GPU-related costs by 56% by reducing repetitive calculations and reusing intermediate calculation results during image and video processing. These are company-reported figures for a specific customer's workload and do not indicate the rate of decrease in GPU numbers or the average performance of all customers.

NVIDIA is also developing software to increase inference efficiency. 'Dynamo 1.0', released last March, distributes tasks to multiple GPU and memory resources and reduces redundant calculations. NVIDIA explained that this can lower token creation costs and expand revenue opportunities for infrastructure operators. Lower inference costs give customers room to process more AI tasks with the same budget.

The key is how much actual work volume can compensate for the increased processing capacity. If the amount of work to be processed and the quality of service are the same, improved efficiency can reduce the need for additional equipment. On the other hand, if cost reduction leads to price reduction or service improvement, and the increase in usage more than offsets the effect of reducing the amount of computation per task, total computation demand may increase. Even in this case, if there is room in existing facilities, the increased work can be handled without additional purchases.

At Microsoft, increased processing capacity led to actual sales. Chief Financial Officer (CFO) Amy Hood said at the fiscal 2026 fourth quarter performance briefing held on July 29 (local time) that customer demand is exceeding available supply. The explanation is that the additional capacity secured through improved CPU and GPU operating efficiency and shortened new facility operation procedures was quickly translated into sales within the quarter.

The MS case is a case where demand that could not be met due to supply constraints was handled with additional capacity. However, since improvements in new facility operation are also included, it is difficult to separate and confirm the effect of inference optimization alone or the causal relationship with additional GPU orders.

The next confirmation point is this week's earnings announcements from major cloud providers. Microsoft will release its performance on the 28th and Amazon will release its performance on the 29th (local time). The key points to watch are whether supply constraints persist, whether the capacity secured through efficiency improvements translates into sales, and how computing infrastructure investment plans change.