NextGen GPU Architecture
Next-generation GPU platform designed for trillion-parameter AI models, real-time inference, and enterprise-scale training workloads.
Revolutionary AI Performance
The NextGen GPU architecture represents a fundamental leap in GPU design, purpose-built for the demands of modern large language models and generative AI. At its core, NextGen delivers 10x the inference performance of previous generations through a combination of architectural innovations and cutting-edge manufacturing.
Built on advanced 4nm process technology, NextGen GPUs integrate 208 billion transistors across two GPU dies connected by a 10TB/s chip-to-chip interconnect. This innovative design enables unprecedented compute density while maintaining efficient power characteristics critical for large-scale deployments.
Transformer Engine 2.0
The second-generation Transformer Engine brings native FP4 precision support alongside enhanced FP8 capabilities. This hardware-accelerated unit dynamically manages numerical precision during training and inference, maintaining model accuracy while dramatically increasing throughput.
By leveraging mixed-precision techniques at the hardware level, Transformer Engine 2.0 delivers up to 5x faster training for GPT-class models compared to traditional FP16 training, with no loss in model quality.
HBM3e High-Bandwidth Memory
NextGen-200 GPUs feature 192GB of HBM3e memory with 8TB/s bandwidth, representing a 75% increase over previous generation HBM3. This massive memory capacity and bandwidth eliminates bottlenecks when training models with hundreds of billions of parameters.
HBM3e's advanced architecture provides the memory throughput required for processing long context windows in transformer models, enabling efficient training of models with 100K+ token contexts without performance degradation.
High-Speed Interconnect 5.0
Fifth-generation high-speed interconnect provides 1.8TB/s bidirectional bandwidth per GPU, enabling efficient scaling across multi-GPU configurations. This represents a 50% increase over previous generations and ensures linear scaling for distributed training workloads.
The interconnect fabric allows GPUs to share memory space seamlessly, effectively creating a unified memory pool across the cluster. This architecture enables training of models too large to fit on a single GPU without the performance penalty of traditional PCIe-based solutions.
Combined with advanced switch technology, high-speed interconnect 5.0 enables full all-to-all GPU communication at full bandwidth, critical for efficient gradient synchronization during distributed training.
Technical Specifications
NextGen-100
NextGen-200
Ideal Use Cases
Large Language Models
Train GPT-4 class models with hundreds of billions to trillions of parameters. Transformer Engine 2.0 and HBM3e memory enable efficient training of the largest language models.
Real-Time Inference
Deploy production AI services with sub-100ms latency. FP4 precision delivers 10x throughput for serving foundation models at scale with minimal overhead.
Computer Vision
Process high-resolution images and video streams in real-time. Massive memory bandwidth supports vision transformers and diffusion models for generation tasks.
Recommender Systems
Handle trillion-parameter embedding tables for personalization at scale. HBM3e capacity and bandwidth eliminate memory bottlenecks in deep learning recommendation models.
Scientific Computing
Accelerate molecular dynamics, climate modeling, and computational biology. Double-precision performance and ECC memory ensure accuracy for scientific workloads.
Generative AI
Power diffusion models, GANs, and multimodal generation systems. Unified memory architecture simplifies complex generative pipelines requiring multiple models.