With the announcement this week  of the NVIDIA Nemotron 3 family of open models, AI innovation continues to evolve in ways that improve efficiency and accuracy. The Nemotron 3 models, available in Nano, Super, and Ultra sizes, provide unique new capabilities for enterprises looking to optimize their AI infrastructure.

Nemotron 3 represents a meaningful advancement in AI model efficiency and flexibility. By optimizing for small language models and introducing mixture-of-experts architectures, Nemotron 3 enables faster inference and reduced latency.

For IBM customers, this means AI workloads can be deployed more cost-effectively across existing infrastructure, especially when combined with IBM Fusion and OpenShift orchestration. IBM business partners benefit by delivering solutions that scale without requiring massive hardware investments, supporting multi-model environments and advanced use cases like content-aware storage. In short, Nemotron 3 helps enterprises do more with less, accelerating innovation while controlling costs.

One of the most notable updates in Nemotron 3 is the adoption of NVFP4 (4-bit floating point) precision for training the two larger models. FP4 precision reduces the memory footprint of model parameters while preserving accuracy, allowing more computations to fit within the same GPU resources. NVFP4 quantization also allows enterprises to run multiple models on fewer GPUs during inference, lowering infrastructure costs and improving throughput. By optimizing precision where it matters most, NVFP4 enables faster inference and training cycles without compromising performance, an important step toward making AI deployments more efficient and scalable.

IBM supports the NVIDIA AI Data Platform reference design, which is built to handle large-scale data ingestion, transformation, and inferencing across multiple models. The NVIDIA Nemotron 3 efficiency improvements, particularly FP4 precision and mixture-of-experts design, make these workflows faster and more resource-efficient.

NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs often serve as the compute backbone for these AI data platform deployments, delivering the high memory bandwidth and parallel processing needed for multi-model execution. With Nemotron 3, these NVIDIA GPUs can now host more models concurrently without sacrificing accuracy, reducing latency in tokenization, prefill, and inference cycles.

Combined with IBM Fusion and OpenShift orchestration, AI data platform environments can scale more effectively, enabling enterprises to process and analyze massive datasets with less infrastructure overhead and improved performance.

The new models are a great fit for Fusion, which enables mixed-model deployments, orchestrated through OpenShift for ease of management. Fusion content-aware storage and KV cache integration further reduce compute cycles, treating tokens as first-class data assets. The result is that enterprises can deploy multiple models more efficiently without adding complexity.

You can learn more about the NVIDIA 3 family of open models here.

To learn more about how IBM supports efficient AI scaling, check out this IBM Research blog post on Accelerating AI Inferencing.