Summary

The NVIDIA GH200 Grace Hopper Superchip is revolutionizing AI inference by delivering up to 2x faster performance in multiturn interactions with Llama models. This breakthrough addresses the long-standing challenge of balancing user interactivity with system throughput in deploying large language models (LLMs). By leveraging key-value (KV) cache offloading to CPU memory and NVLink-C2C technology, the GH200 Superchip significantly reduces computational burdens and enhances time to first token (TTFT).

Unlocking Faster AI Inference with NVIDIA GH200

The deployment of large language models (LLMs) in production environments often requires making hard trade-offs between enhancing user interactivity and increasing system throughput. While enhancing user interactivity requires minimizing time to first token (TTFT), increasing throughput requires increasing tokens per second. Improving one aspect often results in the decline of the other, making it difficult for data centers and AI application developers to optimize LLM deployments.

The Power of NVIDIA GH200

The NVIDIA GH200 Grace Hopper Superchip is changing this landscape. By tightly coupling an NVIDIA Grace CPU with an NVIDIA Hopper GPU using NVIDIA NVLink-C2C, a high-bandwidth, low-latency interconnect, the GH200 Superchip delivers outstanding performance across every generative AI benchmark in MLPerf Inference v4.1.

  • Key Features:
    • Unified Memory Architecture: The GH200 Superchip allows for seamless, zero-copy data transfers between CPU and GPU memory, avoiding the bottlenecks typically associated with PCIe connections.
    • High-Bandwidth Interconnect: NVLink-C2C offers up to 900 GB/s of total memory bandwidth, which is 7x higher than the typical PCIe Gen5 speeds found in x86-based systems.
    • KV Cache Offloading: The GH200 Superchip uses key-value (KV) cache offloading to CPU memory, significantly reducing the computational burden and enhancing TTFT by up to 14x compared to traditional x86-based NVIDIA H100 servers.

Real-World Benefits

The NVIDIA GH200 Superchip’s advanced memory architecture and performance improvements are particularly beneficial for applications that rely on complex, real-time dialogue systems. For example, in multiturn interactions with Llama models, the GH200 Superchip accelerates inference by 2x, enhancing user interactivity without compromising system throughput.

  • Case Studies:
    • Abacus.ai: Recorded nearly a 2x improvement in latency over using NVIDIA H100 Tensor Core GPUs when running large-context inputs with a fine-tuned Llama 2 70B model.
    • Leonardo.ai: Achieved over 3x throughput improvements over using NVIDIA A100 Tensor Core GPUs by porting an existing image captioning pipeline to Lambda’s NVIDIA GH200 Superchip cluster.

Economic Advantages

The GH200 Superchip also offers superior cost efficiency for larger models. For instance, when comparing a single instance NVIDIA GH200 to a single instance NVIDIA H100 SXM, running inference with CPU Offloading, the GH200 delivers 7.6x better throughput and an 8x reduction in cost per token.

Widespread Adoption

Currently, the NVIDIA GH200 powers nine supercomputers globally and is available through various system makers and cloud providers. Its ability to enhance inference speed without additional infrastructure investments makes it an appealing option for data centers, cloud service providers, and AI application developers seeking to optimize LLM deployments.

Future Prospects

The GH200 Superchip continues to push the boundaries of AI inference capabilities, setting a new standard for the deployment of large language models. With its advanced memory architecture and performance improvements, it is poised to play a crucial role in the future of AI applications.

Conclusion

The NVIDIA GH200 Grace Hopper Superchip is a game-changer for AI inference, offering up to 2x faster performance in multiturn interactions with Llama models. By leveraging key-value (KV) cache offloading to CPU memory and NVLink-C2C technology, the GH200 Superchip significantly reduces computational burdens and enhances time to first token (TTFT). Its superior performance and cost efficiency make it the optimal choice for deploying large language models (LLMs) in production environments. As AI applications continue to evolve, the GH200 Superchip is set to play a pivotal role in shaping the future of AI inference.