Deploying large language models (LLMs) in production environments often requires making hard trade-offs between enhancing user interactivity and incre

NVIDIA GH200 Superchip Accelerates Inference by 2x in Multiturn Interactions with Llama Models

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2024-10-28 20:30:06

Deploying 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, cloud service providers (CSPs), and AI application providers to find the right balance.

Leveraging the NVIDIA GH200 Grace Hopper Superchip can minimize these tradeoffs. This post explores how IT leaders and infrastructure decision makers can harness the converged memory architecture of NVIDIA GH200 Grace Hopper Superchip to boost TTFT in multiturn user interactions by up to 2x on the popular Llama 3 70B model, compared to x86-based NVIDIA H100 servers, without any tradeoffs to system throughput.

LLM models are rapidly gaining adoption across various use cases, including question answering, summarization, and code generation. Before responding to a user’s prompt, these models must build a contextual understanding of the input sequence and any additional information retrieved during the inference request, such as in the case of retrieval-augmented generation (RAG).