NVIDIA has outlined its comprehensive strategy for optimizing AI inference performance at scale, introducing the "Think SMART" framework as a guide for enterprises building and operating "AI factories." This initiative addresses the escalating demands of advanced AI models, which generate significantly more tokens per interaction and require robust infrastructure to deliver intelligence efficiently.
According to a recent post on its blog, the company emphasizes that simply adding more compute power isn't enough to meet the growing needs of AI adoption across industries, from research assistants to autonomous vehicles. Instead, a holistic approach is necessary to deploy AI with maximum efficiency. The Think SMART framework provides a five-pronged evaluation for inference: Scale and complexity, Multidimensional performance, Architecture and software, Return on investment, and Technology ecosystem.
As AI models evolve from compact applications to massive, multi-expert systems, inference infrastructure must keep pace with increasingly diverse workloads. These range from quick, single-shot queries to complex, multi-step reasoning involving millions of tokens. This expansion introduces significant implications for resource intensity, latency, throughput, energy consumption, and overall costs. To tackle this complexity, AI service providers and enterprises, including partners like CoreWeave, Dell Technologies, Google Cloud, and Nebius, are rapidly scaling up their AI factories.
Scaling complex AI deployments necessitates that these factories offer the flexibility to serve tokens across a broad spectrum of use cases while meticulously balancing accuracy, latency, and costs. Some applications, such as real-time speech-to-text translation, demand ultra-low latency and high token output per user, pushing computational resources to their limits. Others prioritize sheer throughput for latency-insensitive tasks, like generating answers to dozens of complex questions simultaneously. Most popular real-time scenarios, however, require a balance: quick responses for user satisfaction and high throughput to serve millions simultaneously, all while minimizing cost per token. NVIDIA's inference platform is engineered to strike this balance, powering benchmarks on models like gpt-oss, DeepSeek-R1, and Llama 3.1.
NVIDIA's Full-Stack Approach to Inference
Achieving optimal inference performance is an engineering challenge that requires hardware and software to work in perfect synchronicity. The NVIDIA Blackwell platform is central to this, promising a 50x boost in AI factory productivity for inference. The NVIDIA GB200 NVL72 rack-scale system, which integrates 36 NVIDIA Grace CPUs and 72 Blackwell GPUs via NVLink interconnect, is projected to deliver 40x higher revenue potential, 30x higher throughput, 25x more energy efficiency, and 300x more water efficiency for demanding AI reasoning workloads. Furthermore, the new NVFP4 low-precision format on Blackwell significantly reduces energy, memory, and bandwidth demands without compromising accuracy, enabling more queries per watt and lower costs per token.
Beyond accelerated architecture, NVIDIA's full-stack inference platform includes multiple layers of solutions and tools designed to work in concert. The NVIDIA Dynamo platform facilitates dynamic autoscaling from one to thousands of GPUs, optimizing data flows and delivering up to 4x more performance without increasing costs. For optimizing performance per GPU, frameworks like NVIDIA TensorRT-LLM streamline AI deployment with a new PyTorch-centric workflow, eliminating manual engine management. These tools, when combined, enable mission-critical inference providers like Baseten to achieve state-of-the-art model performance even on frontier models.
The platform also includes NVIDIA Nemotron, a family of open training data models designed for transparency and efficient, accurate token generation without increased compute costs. NVIDIA NIM packages these models into ready-to-run microservices, simplifying deployment and scaling while reducing total cost of ownership. Together, these layers — dynamic orchestration, optimized execution, well-designed models, and simplified deployment — form the backbone of inference enablement for cloud providers and enterprises.
Performance directly drives return on investment. NVIDIA highlights that a 4x increase in performance from its Hopper to Blackwell architecture can yield up to 10x profit growth within a similar power budget. In power-limited data centers, generating more tokens per watt directly translates to higher revenue per rack. The industry is already seeing rapid cost improvements, with stack-wide optimizations reducing costs-per-million-tokens by as much as 80%.
Finally, the technology ecosystem and install base play a critical role. Open models now drive over 70% of AI inference workloads, fostering collaboration and democratizing access. NVIDIA actively contributes to over 1,000 open-source projects on GitHub and hosts 450 models and 80 datasets on Hugging Face. This commitment ensures maximum inference performance and flexibility across configurations, integrating popular frameworks like JAX, PyTorch, and vLLM, and collaborating on open models such as Llama, Google Gemma, NVIDIA Nemotron, DeepSeek, and gpt-oss.
The NVIDIA inference platform, coupled with the Think SMART framework, aims to equip enterprises with the infrastructure needed to keep pace with rapidly advancing AI models, ensuring each generated token delivers maximum value.
