GTC China (early) Jensen Keynote

Category: China Keynote · Year: 2018 · ▶ Watch

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Segments (11)

  • 00:00 · Introduction & Vision
    • Opening video and Jensen Huang’s introduction to the record-breaking GTC China event.
  • 03:50 · Reinventing Computer Graphics
    • Introduction of the Turing architecture, RT Cores, Tensor Cores, and real-time ray tracing.
  • 17:10 · Deep Learning Super Sampling (DLSS)
    • Explanation of how Tensor Cores enable DLSS to improve image quality and performance.
  • 27:00 · The Path Forward for Computing
    • Discussion on the end of Moore’s Law and the need for accelerated computing across the full stack.
  • 44:20 · AI Transforming Computing
    • How AI is changing the landscape of High Performance Computing (HPC) and Hyperscale data centers.
  • 50:30 · NVIDIA HGX-2
    • Introduction of the HGX-2 platform for large-scale HPC and AI training.
  • 58:00 · NVIDIA T4 Cloud GPU & TensorRT
    • Launch of the T4 GPU and TensorRT Inference Server for hyperscale AI inference.
  • 78:10 · RAPIDS: Accelerating Data Science
    • Introduction of the RAPIDS open-source libraries for GPU-accelerated machine learning and data analytics.
  • 97:40 · AI Automating the World
    • Transition to autonomous machines, robotics, and the introduction of the Xavier SoC.
  • 100:40 · NVIDIA AGX Platforms
    • Overview of domain-specific AI platforms: Clara (Medical), Metropolis (Cities), Isaac (Robotics), and DRIVE (Auto).
  • 107:20 · NVIDIA DRIVE & Autonomous Vehicles
    • Updates on the DRIVE AV platform and announcements of partnerships with Chinese automakers and mobility services.

Product Announcements (7)

  • [03:50] Turing Architecture
    • New GPU architecture featuring RT Cores for ray tracing and Tensor Cores for AI.
    • specs: 10 Giga Rays, 14 TFLOPS, 114 TFLOPS Tensor Core
    • availability: N/A
  • [20:50] RTX 2070
    • Consumer graphics card based on the Turing architecture.
    • specs: Faster than Pascal 1080 Ti
    • availability: $499
  • [50:30] NVIDIA HGX-2
    • AI and Data Analytics platform for HPC.
    • specs: 16 V100 GPUs, 2 PFLOPS, 512GB HBM2 memory, 80,000 CUDA cores
    • availability: N/A
  • [58:00] NVIDIA T4 Cloud GPU
    • Universal GPU for hyperscale data centers, optimized for inference.
    • specs: 70W, 65 TFLOPS FP16, 130 TOPS INT8, 260 TOPS INT4
    • availability: N/A
  • [61:30] TensorRT Inference Server
    • Open-source containerized inference microservice.
    • specs: Supports multiple frameworks, runs on Kubernetes, maximizes GPU utilization
    • availability: Available now
  • [78:10] RAPIDS
    • Open-source suite of data processing and machine learning libraries.
    • specs: cuDF (Pandas-like), cuML (Scikit-Learn-like), cuGraph, built on Apache Arrow
    • availability: Open source
  • [98:10] Xavier
    • World’s first AI computing processor designed for autonomous machines.
    • specs: 9 Billion transistors, 350mm2, 12nFFN, 30 TOPS, 30W
    • availability: In full production

Specific Numbers (6)

Timestamp Metric Value Context
08:30 TFLOPS 114 Turing architecture Tensor Core performance.
20:50 $ 499 Price of the RTX 2070 GPU.
35:10 Supercomputers 127 Number of NVIDIA GPU systems on the TOP500 list.
50:30 PFLOPS 2 Performance of the HGX-2 platform.
59:30 TOPS 260 INT4 performance of the T4 Cloud GPU.
98:10 Transistors 9 Billion Number of transistors in the Xavier SoC.

Benchmark Claims (3)

  • [18:50] Ray Tracing + DLSS Performance: RTX 2070
    • vs: GTX 1080 Ti
    • gain: 3.5x performance improvement.
  • [58:10] Inference Performance: T4 GPU
    • vs: CPU
    • gain: Up to 40x faster inference performance.
  • [87:40] Data Science Workflow (ETL + ML): 1 DGX-2
    • vs: 20 CPU Nodes
    • gain: Reduced processing time from hours to minutes (50x speedup).

Customer Stories (5)

  • [09:00] NetEase
    • Integrated RTX ray tracing into their game ‘Justice’.
    • outcome: Created the first China RTX game with realistic reflections and lighting.
  • [19:20] Kingsoft
    • Integrated DLSS into their game ‘JX3’.
    • outcome: Created the first China DLSS-accelerated game, improving performance by 1.8x.
  • [91:00] BGI
    • Used RAPIDS XGBoost for cancer immunotherapy and normal cell classification.
    • outcome: Achieved 10x faster processing compared to CPU.
  • [105:00] JD.com, Meituan, Cainiao
    • Selected Jetson AGX Xavier for their autonomous delivery robots.
    • outcome: Enabled complex AI processing for last-mile delivery logistics.
  • [118:20] FAW, Full Truck Alliance, Plus.ai
    • Partnered to develop an autonomous truck hailing service using NVIDIA DRIVE.
    • outcome: Targeting production in 2021 to solve driver shortages and reduce costs.

Key Technologies (4)

  • RT Core: Dedicated hardware on Turing GPUs that accelerates real-time ray tracing calculations.
  • DLSS (Deep Learning Super Sampling): Uses Tensor Cores and deep learning to generate high-resolution images from lower-resolution inputs, improving frame rates.
  • TensorRT Inference Server: A containerized microservice that maximizes GPU utilization by running multiple models concurrently.
  • RAPIDS: Open-source libraries that allow data scientists to execute end-to-end data science and analytics pipelines entirely on GPUs.

Demos Shown (6)

  • [09:40] Real-time ray tracing demo in the game ‘Justice’, showcasing reflections and lighting.
    • True
  • [21:50] Porsche 911 Speedster Concept rendered in real-time using Turing.
    • True
  • [65:00] T4 Inference Server classifying thousands of flower images per second in real-time.
    • True
  • [86:20] RAPIDS accelerating a mortgage risk analysis workflow from hours on CPUs to minutes on GPUs.
    • True
  • [111:30] NVIDIA DRIVE AV software stack navigating a car autonomously on a highway.
    • True
  • [123:00] Project Sol cinematic demo rendered in real-time, showcasing advanced robotics and graphics.
    • True

Predictions / Commitments (3)

  • [27:00, Current and ongoing] Moore’s Law has come to an end, requiring a full-stack optimization approach to continue advancing computing performance.
  • [38:10, Current and ongoing] AI is automating the world, impacting industries from healthcare to transportation and manufacturing.
  • [97:40, Future] In the future, AI will empower all moving machines, leading to billions of autonomous robots and vehicles.

Companies Mentioned (3)

Baidu, Tencent, Alibaba, Huawei, Inspur, Lenovo, Sugon, QCT, Supermicro · Baidu Cloud, Tencent, JD Cloud, iFLYTEK · Volvo Cars

Notable Quotes (2)

Moore’s Law has come to an end. — Jensen Huang @ 27:00

The more you buy, the more you save. — Jensen Huang @ 90:40

Key Topics

Turing Architecture · Ray Tracing · DLSS · High Performance Computing (HPC) · Hyperscale Data Centers · T4 GPU · TensorRT · RAPIDS · Data Science · Xavier SoC · Autonomous Machines · NVIDIA DRIVE · Autonomous Vehicles

Takeaways

  • NVIDIA is reinventing computer graphics with the Turing architecture, combining ray tracing and AI (DLSS) for photorealistic real-time rendering.
  • With the end of Moore’s Law, NVIDIA advocates for a full-stack accelerated computing approach to continue driving performance gains.
  • The HGX-2 platform unifies HPC and AI workloads, providing massive computational power for complex simulations and deep learning.
  • The T4 Cloud GPU and TensorRT Inference Server are designed to efficiently scale AI inference in hyperscale data centers.
  • RAPIDS brings GPU acceleration to traditional data science and machine learning workflows, drastically reducing processing times.
  • The Xavier SoC and AGX platforms are enabling a new era of autonomous machines across robotics, healthcare, smart cities, and transportation.
  • NVIDIA DRIVE is seeing broad adoption among automakers, EV startups, and mobility services for developing autonomous driving solutions.